Effects of formative assessment with technology on students’ meaningful learning in chemistry equilibrium concepts
Tadesse
Hagos
*a and
Dereje
Andargie
b
aChemistry Department, College of Education and Behavioral Studies, Addis Ababa University, Ethiopia. E-mail: tadessehagos23@gmail.com
bInstitute of Education, Debre Berhan University, Ethiopia. E-mail: derejefanaye@gmail.com
First published on 4th October 2023
Abstract
This study examines how students’ conceptual and procedural knowledge of chemical equilibrium is affected by technology-supported formative assessment (TSFA) strategies. This study's embedded/nested mixed method research design was used to achieve the study's objective. A random sampling method was used to choose the sample of two intact classes for the treatment group and one intact class for the comparison group. To gather quantitative data, the chemical equilibrium conceptual test and the chemical equilibrium procedural test were adapted from the literature. The qualitative data were also gathered using semi-structured interviews and classroom observations. Descriptive statistics and one-way ANOVA were employed to analyze the quantitative data, and theme analysis was utilized to examine the qualitative data. One-way ANOVA results revealed that, in comparison to students who were taught using conventional methods and formative assessment strategies, students who were taught using technology-supported formative assessment strategies demonstrated improvements in conceptual and procedural knowledge. The results of semi-structured interviews and classroom observations also show that, when compared to students who are taught using conventional methods and formative assessment alone, students who are taught using technology-supported formative assessment strategies have a high improvement in learning outcomes of learning chemical equilibrium concepts. In conclusion, conventional methods and formative assessment alone were shown to be less successful for students’ conceptual and procedural knowledge in learning chemical equilibrium concepts than technology-supported formative assessment strategies. These results led the authors of this research to recommend that TSFA be used by chemistry teachers to enhance their students’ conceptual and procedural understanding of chemical equilibrium concepts.
Introduction
When students are able to remember, comprehend, and apply what they have learned, they are said to have taught meaningfully (Dumbrajs et al., 2011). Students’ ability to apply knowledge to novel settings depends on how much of their chemical learning occurs through understanding. Students must move between the three domains of cognition (macroscopic, microscopic, and symbolic) in order to comprehend chemistry. Higher-order thinking abilities and mental models are both necessary for making sense. Students therefore produce meaning through higher-order thinking processes (Zohar, 2013). According to Anderson and Krathwohl (2001), meaningful learning occurs when students make an effort to relate new ideas and propositions to ones that already exist in their cognitive framework. Learning that is meaningful involves students trying to create chemical knowledge (Gabel, 1999; Özmen, 2008; Vallori, 2014).A variety of studies have been conducted recently on how to enhance students’ relevant learning of chemistry. In order to develop and produce a wide range of conceptual, procedural, and metacognitive knowledge as well as a wider range of cognitive processes, chemistry students demand student-centered learning environments (Anderson and Krathwohl, 2001; Brindha, 2018). In order to help students understand chemistry on the macro, micro, and symbolic levels, a variety of chemistry teaching methodologies have been proposed and applied (Miroslav et al., 2018). Learning chemistry in particular necessitates the presenting of topics in ways that are both accurate representations of scientific concepts and simple enough to be understood.
Technology plays a critical role in maintaining such a setting of active involvement through enhanced content visualizations, simulations, and modeling, as well as supporting laboratory instructions (Krause et al., 2017; Haluk and Akbar, 2018). In a chemistry classroom with these types of settings, students can utilize cell phones, projectors, wireless internet access, interactive whiteboards, laptop computers, tablets, and other developing technologies. These tools, when utilized correctly, can improve student-centered education. But, studies on classroom practices show that chemistry educators are not integrating technology as well or efficiently as expected or required (Awad, 2014; D’Angelo, 2018; Nawzad et al., 2018; Gupta and Belford, 2019).
The other recommendation, for learning chemistry in general and chemical equilibrium in particular, is linked to a need for a methodological shift in how it is taught, a detailed examination of typical misunderstandings, and the detection of any existing errors (Chapman and Aspin, 2013). The scientific literature has not given formative assessment strategies much weight, despite their potential to help students better understand chemistry in general and chemical equilibrium learning in particular (Bernal-Ballen and Ladino-Ospina, 2019). Teachers’ assessments of their student achievement and how they learn are inextricably intertwined. Contrarily, formative assessments give precise information about students’ learning styles, pace, strengths, weaknesses, difficulties, prior knowledge, and concepts (Goos et al., 2011; Fook, 2013). Formative assessment has an impact on students’ experiences, satisfaction, outcomes, and overall performance (Andersson and Palm, 2018).
Formative assessment is the process of assessing student achievement and providing ongoing feedback to teachers and students in order to help them improve their teaching and learning. It also assists students in improving by avoiding them from repeating previous errors. According to a prior study (Cauley and McMillan, 2010), teachers who use formative assessments to provide students with detailed and timely feedback have a greater influence on their academic achievement than those who do not. As a result, formative assessment activities are incorporated into instructions to track learning and assess learners’ understanding in order to alter teaching and influence subsequent learning to happen through the application of formative assessment and constructive feedback to ensure the required knowledge is attained (Riley-Ayers, 2014).
Formative assessment also bridges the gap between current and expected performance and offers students with high-quality learning material; constructive feedback has a substantial influence on students’ future performance. Formative feedback classroom practices, according to Kiplagat (2016), improve students’ motivation, confidence, and self-esteem by maximizing their educational potential. Successful formative feedback should be able to provide information that may be used to correct learners’ ineffective learning methods, mistakes, and misconceptions (Andrade et al., 2015). Formative feedback must be timely, constructive, motivated, personal, manageable, and firmly tied to assessment criteria and learning goals in order to be effective (Aslam and Khan, 2020).
As was previously said, among the most effective methods for improving student learning is formative assessment. It frequently helps give individualized instructions, promotes student involvement, gathers diagnostic information, and offers quick feedback. Due to the constrained time allotted for teaching new material and providing individualized feedback, teachers frequently believe they do not have enough time to assess students (Gobert et al., 2013). Digital technology is opening up new opportunities for assessments that are more individualized, quick, and interesting. In order to test students’ understanding of scientific phenomena in dynamic and interactive ways, teachers may use new methodologies made possible by technology to improve formative assessment (Gobert et al., 2013). These methodologies allow for more immediate feedback and feedback to be displayed in ways that are easily understood (Gobert et al., 2013). In order to support the development of 21st-century abilities, a quick shift in pedagogy towards dynamic problem-based and inquiry-based learning is required, which calls for adjustments in formative assessments (Lu et al., 2014). With the development of new learning technologies, the opportunity to use technology to enhance formative assessment for learning has also grown (Johnson-Leslie et al., 2019). Digital tools have been used to improve formative assessment procedures, but this has not yet resulted in a transformation of present practices (Webb et al., 2018; Luthfiyyah et al., 2021).
Therefore, to encourage the study of many subjects, particularly basic science disciplines like chemistry, teachers need to be able to quickly find educational technology resources and incorporate them effectively into the formative assessment process. Numerous studies have highlighted the challenges that students face when studying chemistry, so it is critical to determine the most effective methods of instruction and learning. As a result, integrating technology into the formative assessment classroom can promote better learning, aid in a greater comprehension of complex chemical concepts, and lessen learning difficulties for students. To address issues including time limits, large classes with a variety of students’ demands, and a broad curriculum, many studies also advise integrating technology into formative assessment procedures (Weiss and Belland, 2016; Grob et al., 2017). Additionally, according to many academics, technology can assist teachers in overcoming the challenges associated with gathering and analyzing formative data (Jeong et al., 2020). However, there has not been enough empirical research on how technology-supported formative assessment enhances students’ understanding of chemistry in particular or science in general. The current study has been designed to fill this gap by examining the effects of technology-supported formative assessment on the conceptual and procedural knowledge development of secondary school students. As a result, the following research issues are addressed in this study:
(1) Are there significant mean differences between the formative assessment alone received, technology-supported formative assessment received and comparison groups in the students’ conceptual and procedural knowledge of chemical equilibrium learning?
(2) How does technology-supported formative assessment help students’ understanding of chemical equilibrium during intervention lessons?
Literature review
The concept of formative assessment
Over the years, researchers have presented a variety of definitions of formative assessment in order to figure out what makes an assessment formative. Indeed, one of the reasons for the misunderstanding of formative assessment and its unsuccessful implementation in the classroom may be the vast variety of conflicting definitions (Filsecker and Kerres, 2012; Havnes et al., 2012). According to studies, teachers who do not understand the components that constitute an assessment formative are less likely to use formative assessment with their students (Clark, 2012; Trumbull and Lash, 2013). Because of the varying definitions of formative assessment, the researchers looked into its many meanings. One of the earliest formal definitions of formative assessment was provided by Black and Wiliam (1998). According to Black and Wiliams, formative assessment is defined to encompass activities conducted during teaching learning by teachers or students to improve the instruction through the utilization of feedback (Black and Wiliam 1998).Formative assessment, according to Dixson and Worrell (2016), is a collection of strategies that teachers use to assess student comprehension, learning needs, and academic achievement in the middle of a lesson, unit, or course. It also aids teachers in identifying concepts that students are having difficulties in grasping, abilities that they are having problems mastering, or standards that they have not yet met so that modifications to lessons, instructional strategies, and academic support may be implemented. Formative assessment, according to Loertscher (2015), is also used to check for understanding. For this author, checking for understanding is an important stage in teaching and learning because it is impossible to know what students are getting out of a lesson if you do not check for understanding and identifying and provoking misunderstandings is one of the most important aspects of any learning process.
Formative assessment, according to Trumbull and Lash (2013), is a strategy used by teachers to uncover particular misunderstandings and mistakes made by students while they are still learning. Formative assessment is a deliberate action in which teachers and students profit from the evidence by being able to adapt their continuing learning and instruction (Dunn and Mulvenon, 2009). According to Wiliam (2011), evidence gathered through formative assessment gives information on relevant instructional strategies that may lead to learning improvement. Clark (2011) categorizes formative assessment into two categories: assessment for learning and assessment as learning. Assessment for learning focuses on the student and is used to analyze progress toward a goal, with the purpose of closing the gap between the learner's current level of learning and the targeted learning goals. It entails discussions about intended learning objectives, performance criteria, questioning, and feedback, all of which aid in the achievement of the desired learning objectives. Assessment as learning, on the other hand, encourages self-directed learning and self-assessment, as well as peer learning and assessment involvement. Students may develop and share their learning goals and criteria for success when they use assessment as a learning tool (Clark 2011).
Formative assessment, according to Popham (2014), is a planned process. Other scholars, on the other hand, agree that it can be planned or unplanned (Havnes et al., 2012; Antoniou and James, 2014). According to Chappuis (2015), formative assessment can be thought of in two ways: formal formative assessment, which is planned ahead of time to gather information about student understanding during instruction; and informal formative assessment, which is done on-the-fly or on the spur-of-the-moment. Teachers can utilize formative activities anytime they detect a need to check for student knowledge by thinking of formative assessment as planned or unplanned.
The researchers considered the many definitions and features of formative assessment found in the literature while defining the formative assessment definition for this study. In this study, formative assessment is defined as a systematic method of collecting data about learning in which formative activities are used on a regular basis throughout the learning process to provide feedback on students’ current levels of understanding so that teaching and learning can be modified to address any gaps in understanding and improve student conceptual and procedural knowledge of chemistry in general and chemical equilibrium in particular.
Studies on chemistry education and conceptual and procedural understanding
Chemistry is understood by many students as a problematic, sophisticated, and abstract topic that takes exceptional intellectual abilities and a great deal of work to understand. There are a variety of reasons why students struggle in chemistry (Cardellini, 2012). The abstract nature of many chemical concepts, teaching approaches used in class, a lack of teaching tools, and the complexity of the chemistry language are all factors that make chemistry topics challenging (Childs and Sheehan, 2009). Particulate nature, physical state, and change of matter (Kapici and Akcay, 2016); chemical bonding (Kindu and Mekonnen, 2016); chemical thermodynamics (Sokrat et al., 2014); acid–base concepts (Lathifa, 2018); structure of matter (Tümay, 2016); and chemical kinetics and equilibrium (Şendur et al., 2011) are the most difficult fundamental concepts diagnosed. Much of the difficulties and misunderstanding in this topic has been linked to a lack of basic mathematical knowledge, ineffective teaching techniques, and a lack of laboratory work (Kindu and Mekonnen, 2016).Chemical equilibrium has been revealed to be one of the most difficult fundamental concepts in this regard (Chiu, 2007; Şendur et al., 2011; Kindu and Mekonnen, 2016). Chemical bonding, chemical equations, oxidation–reduction, acid–base equilibrium, reaction rate, and solubility equilibrium all need this concept (Chiu, 2007). In diagnostic research on learning chemical equilibrium, a range of misconceptions about dynamism, reversibility, and completeness of reaction have been observed (Miroslav et al., 2018). Students, for example, are confused about the dynamic nature of chemically equilibrated states; they incorrectly connect chemical equilibrium with static balance (Gorodetsky and Gussarsky, 1986); and they believe that the forward reaction is completed before the reverse reaction begins (Wheeler and Kass, 1978). According to Özmen (2008), chemistry education research should be focused on overcoming the majority of chemistry learning obstacles in general, and chemical equilibrium learning challenges in particular.
Therefore; several parts of chemical equilibrium are becoming widely misunderstood, and they are rapidly losing their relevance (Childs and Sheehan, 2009; Kindu and Mekonnen, 2016). The most common cause of failures to understand chemical equilibrium, according to recent studies, is the use of ineffective didactic and pedagogical strategies (Kindu and Mekonnen, 2016; Bernal-Ballen and Ladino-Ospina, 2019). One of the educational implications suggested to aid in the learning of chemical equilibrium is formative assessment. Several research papers have shown the value of formative assessment in improving learning. Black and Wiliam conducted a literature review of roughly 250 articles relevant to formative assessment research (1998). Students made higher academic progress when they took part in formative assessment, were tested more frequently, received corrective feedback, and focused on learning objectives rather than performance goals, according to their findings. Formative assessment has also been shown to improve students’ performance and standards in many meta-analysis studies (Kingston and Nash, 2011; Li et al., 2020).
However, efforts to implement formative assessment in many countries, including Ethiopia, are hindered by several challenges that contribute to ineffective practices. The time-consuming nature of formative assessment strategies and the time restrictions of class sessions were some of the problems that led to poor practices, making it difficult for teachers to incorporate these strategies into their instruction. Furthermore, setting up formative assessment tasks in a classroom with a large number of students is challenging for teachers (Janbo et al., 2020; Lajane et al., 2020; Yan et al., 2021). Many scholars feel that technology can help to overcome challenges such as time constraints, large classrooms with diverse students, a broad curriculum content coverage and the difficulties in collecting and interpreting formative data (Weiss and Belland, 2016; Grob et al., 2017).
Investigators have also recently recognized the potential of technology to facilitate formative assessment of learning in numerous areas at various levels of school (e.g., Laborda et al., 2015; Gikandi and Morrow, 2016). Students’ participation in assessment activities may be increased using technology-supported formative assessment and quick feedback can be made to advice teachers and students on future teaching and learning directions (Gikandi and Morrow, 2016). Some of the key functions of technology identified by Looney (2011) to support formative assessment processes include rapid assessment, greater access to diagnostic information, more timely feedback to students, interactive learning, easy access to student work, peer feedback and collaboration, efficiency and cost effectiveness, and the ability to capture and assess conceptual and procedural knowledge.
Students must have both conceptual and procedural knowledge in order to challenge any problem appropriately (Sangguroa and Surifa, 2019). Students who have both knowledge and experience can address chemical difficulties, such as what they have learned in relation to specific chemicals or chemical substances they have handled, and they can conduct experiments to better comprehend the chemical concepts involved (Surif et al., 2012). Conceptual knowledge is the understanding of chemistry's conceptual concepts and theoretical concepts, whereas procedural knowledge is knowing how to apply principles and procedures during the process of solving problems (Sangguroa and Surifa, 2019). There are several studies that demonstrated the ability of many students to be able to handle procedural difficulties; they were unable to grasp the fundamental concepts (Sangguroa and Surifa, 2019).
Knowing the explanation behind a concept or a method is not vital in procedural knowledge; what matters is that you know how to apply it. However, understanding chemical concepts is crucial in conceptual knowledge (Rittle-Johnson and Schneider, 2015). In this aspect, the majority of students have a lack of conceptual knowledge. Aydin et al. (2016) found that a lack of conceptual knowledge led to a lack of conceptual application in problem solving. This idea is supported by the fact that many students can solve problems that involve procedures easily but challenged on questions that involved concepts (Cracolice et al., 2008). This indicates the observation that students can recognize and apply formulas and procedures even if they lack to understand the key concepts. This stands to be the same as that of Heyworth (1999), who looked at students who had the intellectual ability to solve any acid–base concept but were unable to do it successfully. After solving these chemistry questions, it has been conjectured that students do not need to apply conceptual knowledge to solve problems that involve quantification in chemistry (Surif et al., 2012).
To invoke a student's deeper understanding of chemical concepts, conceptual issues in chemical equilibrium demand higher-order thinking abilities or higher-order cognitive skills (Prokša et al., 2018). To be employed with chemical information, many conceptual problems require three types of representation: macroscopic, microscopic, and symbolic (Miroslav et al., 2018). Traditional chemistry teaching techniques and instructional strategies, according to Zoller (2002), are insufficient for conceptual learning and the application of higher-order thinking abilities.
One of the recommendations for improving students’ conceptual and procedural knowledge in learning chemistry in general and chemical equilibrium in particular is linked to a call for a change in the way it is taught, a careful examination of common misconceptions, and the detection of any errors that may already be present (Chapman and Aspin, 2013). Educators believe that formative assessment strategies are effective in improving students’ conceptual and procedural knowledge because teachers can help identify students’ misconceptions, make these misconceptions visible to the learners, and implement instructional strategies based on the feedback they receive from the students to address their learning needs (Wiliam, 2011; Moeed, 2015; Maier et al., 2016; Spector and Yuen, 2016; Ozan and Kincal, 2018; Bernal-Ballen and Ladino-Ospina, 2019; Robertson et al., 2019). As indicated by cognitive science research that emphasizes integrated conceptual and procedural knowledge, technology has also a critical role in learning by facilitating the implementation of formative assessment strategies. However, there has not been enough empirical investigation into how technology-supported formative assessment might help students’ learning outcomes such as conceptual and procedural knowledge (Bhagat and Spector, 2017). The current research is an empirical investigation of the effects of adopting technology-supported formative assessment in the teaching–learning process of chemical equilibrium on the improvements of conceptual and procedural knowledge.
Research design and methods
Research design
The study's embedded/nested mixed methods research design was used to answer the research questions. According to Creswell (2014), the nested mixed methods research design has a primary quantitative method that directs the investigation and a secondary qualitative method that supports the processes. In other words, the second method is nested or embedded within the primary method. In this mixed research design, the researcher can collect both types of data simultaneously through an intervention, offering the study the benefits of both quantitative and qualitative data (Creswell, 2014).The treatment and comparison groups in the quantitative study were assigned at random to explore the impact of technology-supported formative assessment on students’ conceptual and procedural knowledge in learning chemical equilibrium. Conducting the research with intact classes avoids the disruption of students and school administrative rules. Hence, the intact classes were randomly assigned to treatments rather than having students assigned to treatments. The method has one comparison group and two treatment groups, with a pretest and a posttest. Students in group one were subjected to Technology-Supported Formative Assessment (TSFA), students in group two were exposed to Formative Assessment (FA), and students in group three received existing instruction.
In a quasi-experimental study, the dependent variable can be affected by extraneous variables. The present study is designed in such a way that the change can be attributed to the independent variable (technology-supported formative assessment and formative assessment alone) and also to minimize the effect of extraneous variables. Therefore, steps were taken to minimize the effect of differences among the two treatments and the comparison group. First, all groups were given pre-tests to compare their abilities on the examined learning outcomes before the intervention (Creswell, 2014). Second, in order to reduce the teacher effect, three teachers with equivalent academic qualifications (master's degree holders) and years of experience (18, 18, and 19) were identified and randomly assigned to the comparison and experimental groups. Finally, the researcher adapted similar conceptual and procedural questions for pre- and post-test to minimize the effect due to test difference, but the time range was set with the purpose to decrease the remembered influence of students on test topics. Similarly, qualitative data were collected to supplement the quantitative data and provide a more in-depth examination of the treatment's implementation during the teaching and learning process (Creswell, 2014).
The sample and sampling techniques
The researcher chose Addis Ababa City Administration in Ethiopia as the research site due to easy access to improved connectivity, student IT exposure, and experimentation feasibility using a convenience sample approach. Since there are many secondary schools in the sub-cities of Addis Ababa, three sub-cities were selected as the target population using simple random sampling approaches by using the lottery method. Following that, one public secondary school was randomly picked as a sample from each of the three sub-cities. By using a simple random selection procedure, one intact class from grade 11 was chosen from each of the selected schools in each of the sub-cities, and the three sections were simply allocated two for the treatment group and one for the comparison group at random.Then, for each school, one chemistry teacher was purposefully chosen by using inclusion criteria of qualification and experience to make sure that well-qualified and experienced chemistry teachers are included. Following the identification of the teachers, one section of their classes was chosen as the target group. The topic, which was already chosen for the intervention purpose, was discovered in the grade eleven textbook; that is why grade eleven students were chosen as research participants. Furthermore, the two teachers who participated in the treatment groups were chosen for semi-structured interviews following the intervention using a purposive sampling technique.
Variables of the study
The intervention (assessment strategies) was used as the study's independent variables. The intervention has been divided into three categories. The two categories have been used for treatment purposes (TSFA and FA alone) and one for comparison group (CG) purposes. The study's dependent variables were students’ conceptual and procedural knowledge scores on tests of chemical equilibrium.Instruments for data collection
The Chemical Equilibrium Conceptual Test (CECT), Chemical Equilibrium Procedural Test (CEPT), semi-structured interview, and classroom observation checklist were utilized as data collection techniques in the study in order to answer the research questions. To reduce students’ remembered effects on test topics, the researcher adapted pre- and post-test conceptual and procedural questions that were similar but not identical. Below is a detailed description of each data gathering instrument utilized in the study.The qualitative interview and classroom observation instruments were developed by the researchers. Two experts in chemistry education looked at the content validity of the semi-structured teacher interview and classroom observation check list forms. The necessary revisions to the forms were made in response to the educators’ recommendations. Furthermore, the researcher believed that estimating the internal consistency (the Cronbach alpha coefficient) and dependability of quantitative research instruments during the pilot test was adequate to assess the instrument's reliability, and the findings are reported in the next section.
The Kuder–Rechardson coefficient alpha for test scores (conceptual and procedural knowledge test scores) was used to determine the internal consistency and reliability of the instruments. Using the Kuder-Richardson formula (K-R20), it was found that chemical equilibrium conceptual tests had a reliability coefficient estimate of 0.72 and chemical equilibrium procedural tests had a reliability coefficient estimate of 0.75, respectively. According to Awang et al. (2015), a reliability coefficient of 0.70 or above is suggested for use in research studies. As a consequence, the tools were appropriate for the study.
For the qualitative analysis, to minimize the chance of bias resulting from our own experiences and presumptions, peer review and inter-coder testing were utilized. The co-author looked over the codebook. To verify and strengthen the validity of the study, Cohen's kappa, a measure of inter-coder reliability, was computed. Initially, a co-author randomly selected and coded a teacher's transcript. The test produced a kappa value of 0.56, which is within the range of moderate agreement. Disagreements were noted and talked about. A census was conducted as a result. Moreover, certain changes were made. The co-author was then requested to perform the coding once again using the census. The final result was a Cohen's kappa score of 0.74, which is within acceptable range.
The pre-test chemical equilibrium concept test and the chemical equilibrium procedural test were administered to each group from the comparison and treatment groups. Finally, the task is followed by the delivery of the intervention. The concepts of chemical equilibrium were presented to all groups by using English as a medium of instruction. The groups received classroom instruction for seven weeks, in three 45-minute sessions each week. Both the treatment and comparison groups learned for the same amount of time. The two treatment groups’ lessons focused on utilizing technology-supported formative assessment and formative assessment alone to examine the improvement of students’ conceptual and procedural mastery of chemical equilibrium concepts. In the following, general explanations on the activities of intervention are given.
Lessons were often delivered in cooperative group settings to enhance student participation. The teacher divided the students into groups based on a variety of factors, including gender, academic achievement, emotional characteristics, and so on. This approach resulted in the formation of eight groups of four to five students. Students were given time to think before answering questions that challenged their high-level thinking abilities and encouraged them to think throughout the lesson. In the classroom, teachers used the following formative assessment techniques: concept mapping, conceptual diagnosis, observation, self-evaluation, quizzes, oral questions, think-pair-share, think-write-pair-share, one question and one comment, a three-minute pause, and a one-minute essay. This means that when teachers use formative assessment strategies to teach with the aim of improving students’ higher-order cognitive knowledge, they must offer meaningful feedback during the process.
Feedback is critical in formative assessment. This feedback helps students fix conceptual mistakes while also encouraging teachers to adjust teaching activities based on their success. In general, students’ in-class and homework assignments were assessed using comments rather than scores or points, and they were given the opportunity to improve their activities or assignments based on the teacher's input. A quiz was given at the end of each class to determine the students’ learning gaps and provide comments. In the context of tests, assignments, and activities, instructional arrangements were formed based on the input from students. Formative assessment data were recorded during student evaluations, and their names were coded. To demonstrate appreciation and stimulate future student progress, rewards were employed. The conceptual knowledge test and the procedural knowledge test were given as post-tests after the research period ended, and their conceptual knowledge and procedural knowledge scores were compared with those of the two groups.
The teacher developed individual and peer formative activities, as well as the course objectives and success criteria, using Power Point. The teacher presented the lesson goals and success criteria in the classroom by displaying the activities on a computer desktop using a plasma screen, utilizing a connecter cable to connect the computer desktop to the plasma television. The teacher used a plasma screen to display both the individual and peer conceptual and procedural formative activities on a computer desktop. During this period, the teacher allows enough time for individual and group discussions about the formative tasks. They reflect on their thoughts and knowledge with their colleagues. After reflection, the teacher displayed the scientific answer on the plasma screen in order to have a common understanding among the students. The teacher's role in this classroom was to help and guide the students.
A telegram group was also formed by the teacher and the students in the TSFA group. The work of this telegram group demonstrates how they used telegrams, and the teacher always attached both conceptual and procedural homeworks to them so that students could do them at home. When a student committed a mistake, the teacher used a messenger to send comments outside of the classroom. This telegram group was also used by the teacher to attach essential instructional resources, assisting the students in developing their conceptual and procedural knowledge. The researchers tested the technological equipment prior to the lessons, and at the start of the treatment, the researchers gave the students instructional materials for performing treatments. Only 6 of the 45 students that participated in technology-supported formative assessment strategies did not have smartphones. They did utilize their families’ smartphones during the implementation. The conceptual and procedural knowledge tests were given as post-tests after the research period ended, and their conceptual and procedural knowledge scores were also compared.
Methods of data analysis
The researcher coded and examined the data acquired from all of the instruments used. The quantitative data were analyzed using descriptive and inferential statistics, while the qualitative data were analyzed using theme analysis of interviews and classroom observations. To see if there were any statistically significant differences between the mean scores of two treatment and one comparison groups, a one-way analysis of variance (ANOVA) was used.On the other hand, the qualitative data for this study were examined in accordance with the type of data gathering instruments used. Interview transcripts and organized classroom observation notes were used to organize the data. Theme analysis was the approach to qualitative data analysis used in this study. Transcripts were used to organize and analyze the data from both instruments. As a result, coding was used as an analytical method. Initial coding, focused coding, and axial coding are the three steps. To begin the process of developing themes, we began by listening to the individual audio recordings. We listened to the audio recording to familiarize ourselves with the topic and make any necessary corrections to the transcription. Following that, we read the transcript to become familiar with the participant's experience (Creswell, 2009). After that, each transcript was manually coded, paying great attention to each line of text. In a Word document, we kept count of the large number of codes created by each unique transcript. Before moving on to the next round of coding, we analyzed the codes that were recorded by going over each list and looking for growing patterns and similarities.
Following that, we used focused coding; we examined the existing codes and grouped them together based on commonalities using focused coding. After defining a category, we assigned a tentative heading to each group. By using focused coding, we were able to arrange the data and see the growing categories and supporting codes. The importance of codes was determined by manually analyzing their frequency. The last coding method used was axial coding. The primary categories were identified using axial coding, which was then linked to subcategories. We started by going over the categories that had been developed during the last round of coding. We also examined the list to see if any additional categories were required. After that, we looked at the categories and supporting codes for a more in-depth analysis. At times, we renamed categories to better express the developing theme. Following that, the categories and supporting codes were added to the table. On the codes, each participant's pseudonym was recorded. This provided participants with a visual aid for studying and evaluating the codes they created.
During this process, we also reviewed categories and decided which ones were most suited to answering the study question. We additionally searched for themes that corresponded with the subject and calculated the number of codes related to each participant. As a result of this method, some categories were removed. We developed subcategories that were related to the main category when it was needed. The final themes and subthemes, as well as the participants’ raw statements, were then organized to ensure that they were included in the report's findings. Finally, the descriptive case study's final themes and subthemes were generated using the categories created throughout the axial coding process.
Findings
Findings of pre-test data
The assumption at the start of this study was that the intervention groups that would be used in it would be comparable. As a result, the researcher tried to analyze the homogeneity of the intervention groups prior to implementing the instruction as described by Wiersma and Jurs (2005). The pretest mean scores for the two experimental and one comparison groups were compared using one-way ANOVA based on data acquired from the pre-administration of the pre-conceptual knowledge test and the pre-procedural knowledge test. In Tables 1 and 2, the statistical findings of each group were analyzed and reported.| Dependent variable | Group | N | Mean | Std. deviation |
|---|---|---|---|---|
| Pre-test conceptual knowledge | TSFA group | 45 | 7.87 | 2.64 |
| FA group | 43 | 6.95 | 3.08 | |
| CM group | 44 | 8.27 | 2.490 | |
| Total | 132 | 7.70 | 2.78 | |
| Pre-test procedural knowledge | TSFA group | 45 | 4.09 | 1.62 |
| FA group | 43 | 3.40 | 2.52 | |
| CM group | 44 | 3.84 | 1.96 | |
| Total | 132 | 3.78 | 2.07 | |
| Dependent variables source | SS | Df | MS | F | Sig. | |
|---|---|---|---|---|---|---|
| Pre-test conceptual knowledge | Between groups | 38.94 | 2 | 19.47 | 2.58 | 0.072 |
| Within groups | 972.54 | 129 | 7.54 | |||
| Total | 1011.48 | 131 | ||||
| Pre-test procedural knowledge | Between groups | 10.24 | 2 | 5.12 | 1.20 | 0.281 |
| Within groups | 548.39 | 129 | 4.25 | |||
| Total | 558.63 | 131 | ||||
The mean score of the groups (M = 7.84, for the TSFA group; M = 6.96, for the FA group; M = 8.27, for the CM group of conceptual test scores; and M = 4.09, for the TIFA group; M = 3.40, for the FA group; and M = 3.84, for the CM group of procedural test scores) appears to be somewhat different, according to the descriptive statistics results of the pretest for conceptual test scores and procedural test scores.
After the descriptive statistics were analyzed, a one-way ANOVA was used to see if there was a significant difference between groups on their two dependent pre-tests. Before conducting the analysis, the pre-test scores were checked to ensure that the assumption was met. In the two dependent variables, the skewness and kurtosis z-values of the pretest data were within acceptable limits (see Appendix Table 9). This indicates that the data were approximately normally distributed. Homogeneity of variance for the dependent variables, pre-conceptual knowledge and pre-procedural knowledge, was ensured by Levene's test (see in Appendix Table 10). This ensured that the populations from whom the samples were taken had no variation, allowing the use of ANOVA.
The ANOVA results also indicated statistically no significant mean difference across the treatment and comparison groups: F(2, 129) = 2.58, p = 0.072 for the conceptual test, and F(2, 129) = 0.1.20, p = 0.281 for the procedural test, implying that the groups were similar in terms of conceptual and procedural test scores (see Table 2). It is important to note that before intervention, the groups were nearly similar, as justified by the ANOVA results given above. This means that there is no significant difference in learning conceptually or procedurally between the three groups prior to using the technology-supported formative assessment strategies.
Finding of post-test data
The CECT and CEPT scores on the post-test for the three groups were also analyzed using SPSS. A one-way ANOVA was used to compare the means of these three groups, namely the technology-supported formative assessment (TSFA) group, the formative assessment (FA) alone group, and the conventional method (CM) group. The posttest means of the TSFA group, the FA alone group, and the CM groups were compared to determine the most successful strategy for increasing students’ learning results in studying chemical equilibrium.Because there were many comparisons, the post hoc test was followed by a one-way ANOVA to determine the best treatment. As a result, these statistical methods were used for inferential analysis, and interpretation was provided in order to arrive at the findings. One of the study's objectives was to see how technology-supported formative assessment affected students’ conceptual and procedural knowledge when learning chemical equilibrium. The next two sub-sections examine and provide the mean score of each group on the post-test results of the conceptual and procedural knowledge tests.
Treatment effects on conceptual knowledge test scores of students
To investigate the effect of the treatment on students’ conceptual knowledge, a one-way ANOVA was employed. The participants were separated into three groups: TSFA (technology-supported formative assessment), FA (formative assessment) alone, and CM (conventional method). The outcome variable was determined to be normally distributed (see in Appendix Table 9), and equal variances were assumed based on Levene's test results: F(2, 129) = 1.07, p = 0.345. This means that the one-way ANOVA assumption was not violated. As a consequence, based on the conceptual knowledge test scores, the post-test total, mean, and standard deviation of the three groups were analyzed, and the findings are given in the tables below.For three group levels, there was a statistically significant difference in conceptual test results, F(2, 129) = 12.71, p < 0.05, η2 = 0.17. As a consequence of the significant finding, it is concluded that there is a considerable difference between the three groups’ degrees of conceptual knowledge. According to Cohen's (1992) guidelines for evaluating the effect size, the magnitude of the difference between the means and the effect size was large (see Table 4). Furthermore, post hoc comparisons were carried out to analyze pairwise differences among group means using the Scheffé test to see if uneven variance or sample size was tenable.
The results of the tests revealed significant pairwise differences in the mean scores of students in the TIFA group (M = 18.93, SD = 2.99) and the CM group (M = 15.05, SD = 4.06, p < 0.001). There was also a statistically significant difference between the TIFA and FA alone groups (M = 18.93, SD = 2.99, p = 0.01). Students in the FA alone group (M = 16.63, SD = 3.86) were not significantly different from those in the CM group (M = 15.05, SD = 4.06, p = 0.112) (see Table 5). A simple bar mean graph shown in Fig. 1 also reveals that the students’ conceptual test scores improved after being taught by the technology integrated formative assessment and formative assessment alone groups. The control group, on the other hand, who were taught using the conventional method, showed minimal progress. As a result, students who used the TSFA and FA approaches performed higher on conceptual test scores than those who used conventional methods (Table 3).
 | ||
| Fig. 1 The effect of intervention on conceptual knowledge for TSFA, FA and CM groups. | ||
| Group type | Scores for conceptual knowledge | ||
|---|---|---|---|
| N | M | SD | |
| TSFA group | 45 | 18.93 | 2.99 |
| FA group | 43 | 16.63 | 3.86 |
| CM group | 44 | 15.05 | 4.06 |
| Total | 132 | 16.89 | 3.97 |
| Source | SS | Df | MS | F | Sig | η 2 |
|---|---|---|---|---|---|---|
| Between groups | 340.54 | 2 | 170.27 | 12.71 | 0.000 | 0.17 |
| Within groups | 1728.76 | 129 | 13.40 | |||
| Total | 2069.30 | 131 |
| Scheffé | ||||||
|---|---|---|---|---|---|---|
| (I) group of students | (J) group of students | Mean difference (I–J) | Std. error | Sig. | 95% confidence interval | |
| Lower bound | Upper bound | |||||
| a The mean difference is significant at the 0.05 level. | ||||||
| TSFA group | FA group | 2.30a | 0.78 | 0.010 | 0.45 | 4.16 |
| CM group | 3.89a | 0.78 | 0.000 | 2.05 | 5.73 | |
| FA group | TSFA group | −2.31a | 0.78 | 0.010 | −4.16 | −0.45 |
| CM group | 1.58 | 0.79 | 0.112 | −0.28 | 3.44 | |
| CM group | TSFA group | −3.89a | 0.78 | 0.000 | −5.73 | −2.05 |
| FA group | −1.58 | 0.79 | 0.112 | −3.44 | 0.28 | |
Treatment effects on procedural knowledge test scores in students
The researcher used a one-way ANOVA to evaluate students’ post-test results on the chemical equilibrium procedural test to see if technology-supported formative assessment had an influence on student procedural knowledge. The assumptions of normality and homogeneity of variance were assessed before doing the one-way ANOVA. The outcome variable's findings were determined to be approximately normally distributed (see in Appendix Table 9), and equal variances were assumed based on Levene's Test (F(2, 129) = 0.438, p = 0.646) (Table 6). Tables 6–8 show the descriptive data findings as well as the one-way ANOVA results summarized.| Groups | Procedural knowledge scores | ||
|---|---|---|---|
| N | M | SD | |
| TSFA group | 45 | 11.56 | 1.49 |
| FA group | 43 | 10.12 | 1.45 |
| CM group | 44 | 9.45 | 1.37 |
| Total | 132 | 10.39 | 1.68 |
| Source | SS | Df | MS | F | Sig | η 2 |
|---|---|---|---|---|---|---|
| Between groups | 102.86 | 2 | 51.43 | 24.90 | 0.000 | 0.28 |
| Within groups | 266.44 | 129 | 2.07 | |||
| Total | 369.30 | 131 |
| Scheffé | ||||||
|---|---|---|---|---|---|---|
| (I) group of students | (J) group of students | Mean difference (I–J) | SE | Sig. | 95% confidence interval | |
| Lower bound | Upper bound | |||||
| a The mean difference is significant at the 0.05 level. | ||||||
| TSFA group | FA group | 1.44a | 0.31 | 0.000 | 0.71 | 2.17 |
| CM group | 2.10a | 0.30 | 0.000 | 1.38 | 2.82 | |
| FA group | TSFA group | −1.44a | 0.31 | 0.000 | −2.17 | −0.71 |
| CM group | 0.66 | 0.31 | 0.084 | −0.10 | 1.39 | |
| CM group | TSFA group | −2.10a | 0.30 | 0.000 | −2.82 | −1.38 |
| FA group | −0.66 | 0.31 | 0.084 | −1.39 | 0.07 | |
Table 6 presents descriptive data on student procedural test scores for the three student groups. The comparison group had the numerically lowest mean level of student procedural test scores (M = 9.45, SD = 1.37), while the TSFA group had the numerically greatest mean level of procedural test scores (M = 11.56, SD = 1.49). The impact of treatments on students’ procedural knowledge test scores was investigated using a one-way ANOVA between groups. The one-way ANOVA results revealed a statistically significant difference in the post-test mean scores of the procedural knowledge exam between the three groups, F(2, 129) = 24.90, p < 0.001, η2 = 0.28. According to Cohen's (1992) standards, the effect sizes associated with statistically significant effects are considered large.
The statistically significant ANOVA was followed up with three post hoc tests to further assess the mean differences between the three groups. The TSFA (M = 11.56, SD = 1.49) and FA (M = 10.12, SD = 1.45, p < 0.001) had a statistically significant difference. The difference between the TSFA and CM groups was also statistically significant (M = 9.45, SD = 1.37, p < 0.001). Finally, no statistically significant differences in the FA and CM groups were found (M = 10.12, SD = 1.45, p = 0.084) (see Table 8). The simple bar mean graph for procedural test scores also revealed that students in the technology integrated formative assessment and formative assessment alone groups improved their procedural knowledge test scores. The comparative group, on the other hand, who were taught using the conventional method, showed only minor improvements. As a result, in comparison to the conventional technique group, the TSFA and FA groups performed better on procedural test scores (Fig. 2).
 | ||
| Fig. 2 The effect of intervention on procedural knowledge for TSFA, FA and CM groups. | ||
Findings of qualitative data
To answer the study's second research question, a descriptive case study was undertaken with three participant teachers who were randomly assigned to experimental or control groups, and a semi-structured interview was conducted with those teachers who were randomly assigned to experimental groups. An audio recorder and field notes were used to document the teacher–student interactions and the teachers’ use of assessment procedures, in which the teacher elicits, identifies, and uses student feedback. Themes were used to assess the qualitative data, and the results were stated as follows:The data analysis yielded three core themes and ten subthemes. The following main themes emerge: (a) the effectiveness of formative assessment strategies, (b) potential obstacles associated with formative assessment strategies, and (c) coping with the strategies. Motivating and engaging students; optimizing teachers’ preparation, follow-up, and support; optimizing student cooperation; exhaustion and extra burdens; individualized feedback; use of technology; availability and distribution of materials as hardcopies; and shared activities for the benefit of their peers were the subthemes that emerged from the data analysis. In the sub-sections that follow, these ways, challenges, and threats are discussed in detail, together with typical excerpts that reflect the true senses.
Effectiveness of formative assessment strategies
… in general, they were motivated. Because when we take questions for the next day, then they are ready for the next class and are actively participating in the next class. So, uh, they have more and will initiate themselves to the next class if we give the activities every day in the class and they become ready to read other materials and other things. So, it was good.
Such an effect of the TSFA was found to also extend to improving students’ communications within both their individual and peer groups. The use of available technological platforms, such as telegram, Xender, and simulations displayed on the plasma television, is the other feature that lets students appreciate and become eager to take part. The following quote from the transcripts of Teacher B explains this aspect very well:
By the way, teaching this topic using technology gives me more important things than others. Because they interact with each other, they discuss, they ask each other questions, they will help each other, and if there are problems, they will call me, so the interaction of the students is very intense. It makes them buzzy [to mean “busy”], so because after discussing, they see different videos, so that makes them happy, so they interact, there is a presentation, and they present it for their classmates, so it was very, very interesting. The interaction of students in groups as well as individually was different than others that I have used before.
Such optimized motivation and engagement were also well noticed during the classroom observation. In particular, students who participated in the technology-supported formative assessment group had great motivation toward learning chemical equilibrium. In our observations among the three teachers, we did not observe strong evidence of them sharing the criteria for success with students. The major limitations found during the observations were the failure to introduce instructional objectives by the teacher who participated in the control group, the passivity and limited participation on the part of the students, and the unsuitability of the seating arrangements. On the other hand, the teacher who participated in the technology-supported formative assessment group had a higher rate of implementing learning goals than the other two teachers.
In this regard, the teachers showed greater strengths in connecting current lessons to future learning and addressing the learning goal throughout the lesson (learning goal implementation). They introduce the lesson content, instructional objectives, and the type of formative assessment to be used. In many of the classes, the majority of the students were observed while taking notes. Some students were also listening attentively to the teacher without taking notes. Whenever the students attempt to fulfill the assessment criteria, the teachers motivate them to a large extent. Among the three teachers, A and B were ranked at developing and effective levels, respectively, in all areas of the sharing learning intentions and criteria for success subcategory. On the other hand, teacher C was ranked at the beginning level for the sharing learning intentions and criteria for the success subcategory (see in Appendix B).
Furthermore, the researcher discovered from classroom observation that the questioning pattern (more probing questions), wait time for responses, eliciting evidence of learning (revealing students’ thinking), determining learners’ progress, and using evidence to adjust instruction were all indicators of an effective classroom discussion (questioning) strategy. Among the teachers, teachers A and B were ranked at the most effective level in implementing questioning strategies (see in Appendix B). On the other hand, teacher C was ranked at the beginning level for the implementation of questioning strategies. Although teachers differed in their approach to allowing students to self-assess, the general steps were nearly identical. Two of the teachers, except the one who participated in the control group, said questioning strategies were used effectively during their observed lessons. On the other hand, the teacher who had participated in the control group used the lecturing strategy to deliver the contents of the lesson by asking some convergent questions. Teachers in the experimental study infused questions throughout the lesson to determine student understanding, provided appropriate wait time, and gauged student progress based on classroom discourse and interactions. In the meantime, some students attempted to answer questions while the others listened attentively.
In terms of questioning, the teacher of the TSFA group asked both divergent and convergent questions throughout a lesson on the dynamic nature of chemical equilibrium. He asked students to build on one another's predictions and descriptions of scientific experiments and pushed for detailed responses to his questions. For example, he asked specific questions about macro–micro thinking. In terms of engineering effective classroom discussion, the use of small group discussion, entertaining student viewpoints, and communicating expectations were employed significantly, especially in the TSFA group.
For the engineering (learning tasks) strategy, the researcher observed that there were indications of the connection to learning objectives (congruence), the clarity of tasks (transparency), the relevance of tasks to real-life problems (authenticity), student autonomy (student consultation on tasks), and individualized tasks (student capabilities). Both teachers A and B were rated at an effective level for the learning task domain. In contrast, teacher C was rated at the beginning level in the implementation of the learning task domain (see in Appendix B).
Teachers A and B clearly specified the focus of the lessons, including the tasks to be completed, checked for understanding with the class repeatedly throughout the lesson, and made adjustments as necessary. On the other hand, teacher C often missed opportunities to make inferences about progress and pivotal moments to adjust instruction accordingly based on student understanding. More specifically, teachers A and B were providing efforts to support students in creating a safe environment. Students were observed to be motivated and stimulated to learn the lesson. They were also actively engaged with their teachers. The teachers were also responding to students’ questions and providing logical corrections when students made mistakes.
Moreover, the teacher in the TSFA group was moving around, listening to students during the discussion, taking notes, and asking questions. In addition, the teachers demonstrated their due care and provided equal opportunities for the students to participate during the teaching and learning process. The teachers were also working hard to help students freely communicate and interact with one another. The teachers were also sharing their high expectations with the students. As a result, they are more interested, motivated, and fully engaged in the lesson.
Okay, this can be narrowed by students working together, and we can also narrow the knowledge gap by continuously following up with lower and medium-achieving students to address the needs of higher-achieving students. That is, I continuously follow up with each student…
… I also made room for collaboration with both high achievers and low achievers. In short, I have used two mechanisms. One of the mechanisms was following up with each student, and the second mechanism was cooperating with each other.
This was echoed in the testimonial reflection from the second teacher (teacher B) too. The following is an extract quoted from the transcripts of his interview in relation to this issue and its magnitude.
The interaction of students in groups and individually was unlike any other lecturing method I had used previously. Then, how much did it influence students’ motivation? To answer this question, by the way, there are individual works, not only individual works; there is a group work, even if they make it in groups.
A comparable extent of cooperation was also demonstrated during the classroom observation of the treatment groups, while few students were found to do all the writing and reflecting tasks again and again in the comparison group. Generally, Teacher B was ranked at an effective level in all constructs related to facilitating classroom collaboration. For example, students were observed working productively in collaborative groups, sharing multiple viewpoints and their respective ideas with others. In addition, student–teacher interactions had become more positive, and the teacher was spending less time on directed teaching and more time encouraging, coaching, and engaging with students. Here is an exemplary excerpt from the researcher's notes on the observation. Teachers A and C were rated at a developing level for their effective classroom collaboration. This is very typical evidence from the notes of classroom observations.
In FA and TSFA groups, teachers had students’ desks organized in groups of five. The groupings encouraged regular discussions and collaboration on assignments without requiring students to move around. Most students seemed used to working with their groups, and most were actively engaged with their peers during discussions and collaboration. As a result, providing such kinds of opportunities is useful to help students feel comfortable. This is also useful to meet the ultimate purpose of promoting a sense of belongingness and promoting cooperative participation in classroom activities.
Potential obstacles associated with formative assessment strategies
Ok, actually, I get that it was good for the students as well as for the teachers. The teachers may take on a lot of burden with that implementation in the classroom. From my experience, giving students questions every day and correcting their responses was tedious. At this time, this was the most tedious part of the implementation of formative assessment. Actually, if we apply it in a dedicated manner, it is good for the students. However, correcting individual questions is a burden for us, and performing in a large classroom takes more time. In our classroom, we have a large number of students, and correcting each individual formative question was boring. But if the number of students is small in the classroom, it is more convenient than the other techniques.
The main difficulty I’ve encountered while implementing this assessment is that the student's willingness is insufficient. Because most students complain when they are given individual as well as group activities,
This problem goes beyond the willingness of the second teacher (teacher B). According to him, the students’ limited awareness and experience could potentially prevent them from acting to the required type and level of their designated role.
Okay, one of the main issues with implementing this strategy was that it was unfamiliar to the students. One of the first week's challenges was to become acquainted with this method. They are not using this method or technique for other subjects.
Actually, I didn’t get enough time to correct and comment on all activities, especially individual activities. For group activities, I have time because the number of submitted papers was limited. But for individual activities, it was difficult. Because I have another class other than those students, and the overlap of each activity makes me too busy. So, I corrected and commented on only some of the papers, and I have run out of time to correct the remaining papers.
An extract with similar implications was also found in and quoted from the transcripts of teacher B as follows:
The third is a shortage of time to give feedback to each student. So, those are the main challenges that were faced during implementation.
It was also found from the observation that teachers attempted to provide individual feedback in a very limited manner without providing students with an opportunity to internalize or use the feedback in a meaningful way. All teachers in the study scored lower in the implementation of strategies for giving feedback. All constructs in this area were scored at the beginning level with the exception of teacher B, who scored at the developing level on the construct after assessing progress during the lessons.
Coping strategies
By the way, we are currently experiencing global change. So, technology is very important not only for these purposes; everything is changing, and every activity is related to technology, so not only for chemistry but even for other subjects, it is very important. If our students are learning using formative assessment with technology, it is very important. Because the students are learning in class theoretically, we cannot show them how to do it practically. Teaching science is teaching the real thing, but we cannot show the reality. But technology helps us show reality by using different techniques. So, if we use technology-supported formative assessment, it will encourage the students to be active and good in the future. So I will recommend every teacher use this type of teaching strategy. It must be used not only for one topic but continuously for every topic. If teachers use this strategy, our education system may promote quality learning.
So, it was very important, because I am preparing the power points and I give the power points in two mechanisms, that is, in hard copy for each student and as well, I have been trying to attach the power point using the telegram. For some students who have the availability of smart phones, we share the activities by Xender and I have been trying to share them, and even though we have been creating for that class that technology, especially a telegram, there are some students who do not have access to that technology. So, uh, especially the implementation of formative assessment, especially technology integrated formative assessment, is very interesting. It was good.
A significant increase in demand was also demonstrated at the time of classroom observation for hard copies of notes, PowerPoint slides, instructions, and written assignments. The optimized extent of the students’ attention towards the activities was also gradually demonstrated as a result of the availability of these materials.
Okay, the mechanism I used was to return the papers to the class for evaluation by their classmates. Simply put, I have exchanged their works for their classmates.
Discussion of main findings
Treatment's effects on students’ conceptual and procedural knowledge
Anderson, a Bloom's student, examined the Bloom's Taxonomy with a team of well-known cognitive psychologists and published an updated version in 2001. The previous taxonomy was reassessed and the Revised Bloom's Taxonomy was established, taking into account recent advancements in the field of education, students’ learning styles, and new assessment and evaluation methods (Anderson and Krathwohl, 2001). The updated Taxonomy's major focus is on ‘what to learn’ and ‘how to learn.’As a result, the Revised Bloom Taxonomy is a significant step forward in terms of learning outcomes. It has four levels of knowledge: factual, conceptual, procedural, and metacognitive. Remembering, understanding, applying, analyzing, evaluating, and creating are the six degrees of cognition in each knowledge domain. The types of cognitive abilities are lower-order thinking skills (factual knowledge), and higher-order thinking skills (conceptual, procedural, and meta-cognitive knowledge). The scope of this study was confined to conceptual and procedural knowledge. As a result, the major goal of this research was to see how successful technology-supported formative assessment was at improving students’ conceptual and procedural knowledge in studying chemical equilibrium.
The effects of treatment on students’ conceptual and procedural knowledge were investigated using a one-way between-groups analysis of variance. The participants in the study were separated into three groups: those who got Technology-Supported Formative Assessment (TSFA), those who received Formative Assessment (FA) alone, and those who received Conventional Method (CM). The post-test mean scores for the three groups were varied, according to the outcomes of the conceptual and procedural post-tests. The treatment groups’ (TSFA and FA alone) mean conceptual test scores were (M = 18.93, SD = 2.99) and (M = 16.63, SD = 3.86), respectively, whereas the comparison group's mean was (M = 15.05, SD = 4.06). In terms of procedural knowledge test scores, the TSFA and FA alone groups have higher mean scores (M = 11.56, SD = 1.49) and (M = 10.12, SD = 1.45) than the comparison group (M = 9.45, SD = 1.45), respectively. Treatment had a significant influence on conceptual test scores (F(2, 129) = 12.71, p < 0.001, η2 = 0.17) and procedural test scores (F(2, 129) = 24.90, p < 0.001, η2 = 0.28) according to the findings of one-way ANOVA (Tables 4 and 7).
As a consequence, there was a significant difference in students’ levels of conceptual and procedural knowledge test scores among the three groups, resulting in a significant outcome. According to Cohen's (1992) guidelines for evaluating effect size, the magnitude of the difference in the means and the effect size was large (Tables 4 and 7). This suggests that the differences between groups were related to the intervention's success in influencing students’ conceptual and procedural learning.
Furthermore, post hoc comparisons to evaluate pairwise differences among group means were conducted with the use of the Scheffé test for unequal variance. Scheffé post hoc analysis indicates a significant difference in the means among the three groups: the TSFA group and the CM group, the TSFA group and the FA alone group. However, students who were treated with FA alone in the group did not significantly differ from the CM group (Tables 5 and 8). Hence, the TSFA and FA methods showed better performance on conceptual and procedural test scores as compared to those students who thought they were using a conventional method.
The positive influence of using technology to increase learning, as shown in this study's findings, is consistent with what a lot of academics have suggested (Shirley and Irving, 2015; Ramsey and Duffy, 2016). Teachers can efficiently acquire formative evidence by utilizing technological platforms (Panero and Aldon, 2016). Kowalski et al. (2015), for example, developed a formative assessment application that takes advantage of the tablet PC's handwriting skills to better capture what students do and do not know. Students in the experimental group demonstrated larger learning gains by using this instructional strategy than students in the control group who received conventional lectures. Elmahdi et al. (2018) looked at the impact of technology-assisted formative assessment on student learning. Their findings revealed that implementing Plickers for formative assessment improves students’ engagement, saves time in class, confirms reasonable participation chances, and promotes a joyful and stimulating learning environment.
Maier et al. (2016) looked at the effects of computer-assisted formative assessment feedback on students’ mathematics learning. Two treatments and one control group were assigned to them at random. The first group received extensive instruction-based feedback, the second group received dichotomous verification feedback (basic information about the veracity of an answer), and the control group just read relevant materials and received no feedback after formative assessments. Students who employed verification codes had higher post-test conceptual understanding scores than students who received extended comments. The authors came to the conclusion that detailed feedback is only useful if students really use it.
Song and Sparks (2019) used game-based formative assessment to analyze the relative usefulness of two forms of feedback (answer-only versus explanatory feedback) for 106 sixth and seventh graders’ argumentation abilities. Some game elements, such as interaction, rules and limitations, challenges, objectives, and rapid task-level feedback, are included in the lesson and shown onscreen so that students may assess their current performance and development. Students who received explanatory feedback improved their reasoning abilities somewhat more than those who received simply answer-based feedback. Although most students performed similarly across feedback situations, highly skilled students did worse on explanatory input than on answer-only feedback.
The current findings, however, contradict those published by Shelton et al. (2016), who employed formative assessment software with laptops and tablet PCs, allowing students to write or draw their responses. There was no link between the score in writing tasks and overall conceptual knowledge, according to the researchers. Chu (2014), for example, studied the impact of web-based formative assessment tools for mobile devices. When compared to a control group that did not get technological assistance, the students did not accomplish substantial learning with this new configuration. These examples demonstrate a wide range of outcomes when utilizing various software and hardware, implying that not only the implementation but also the technical platform may influence the learning outcomes when employing formative assessment.
Many research studies investigating the impacts of various technologies in education have determined that when utilized in combination with appropriate pedagogical approaches, technology has a beneficial impact on learning and learning outcomes (Albaaly and Higgins, 2012; Erbas et al., 2015; Shanwal, 2017). Several studies have shown that formative assessment combined with technology can help students achieve better learning results (Shen and Ho, 2020; Hasan et al., 2021). Others said that the efficiency of the assessment process is determined by how teachers use technology to develop and implement it (Heflin et al., 2017). The foregoing findings imply that technology-integrated formative assessment will be used in classrooms, but the amount to which it will be well implemented and helpful remains unclear, with contradictory results.
Experience of teachers in putting treatments into practice
According to data from their interviews and observations in the classroom, teachers considered the use of appropriate formative assessment in classrooms an encouraging activity in the learning process. The findings of this study suggest that schools should better help teachers with transforming classrooms into more effective formative assessment settings. In summary, we noticed that teachers use a variety of formative assessment approaches on a regular basis, although they are inconsistently implemented. Many benefits of adopting effective formative assessment were also cited by the teachers. Contributions to learning quality, enhanced student motivation, deep learning encouragement, and understanding of learning strengths and deficiencies were among them. Although certain strategies work, all teachers need more practice and help to enhance their implementation, especially in areas that increase student ownership of their learning.Similar to the current study, Fisher et al. (2011) described a formative assessment technique that resulted in significantly higher test and overall grade scores, as well as enhanced student learning in general. According to McDowell (2013), students respond more positively to formative assessment-based courses compared to non-formative assessment-based courses. In another study, McGrath and Fischetti (2019) employed formative assessment strategies based on student performance in secondary school science classrooms. The research revealed that the teacher emphasized diagnostic assessment prior to the experimental method, but that the same teacher acknowledged the importance of formative assessment thereafter. The teacher's views about assessment procedures were found to be good, and he or she was aware of the benefits and limitations of most assessment approaches.
Using technology to create engaging formative assessments is one way to boost student learning while simultaneously preparing them for a summative assessment. One of the study's primary findings is that using this type of technology for formative assessment saves time and instructional resources. The teacher also stated that incorporating technology into formative assessment improved the students’ motivation for the session and that they enjoyed it. Teachers who used formative assessment strategies more frequently reported that before using technology for formative assessment, they must first understand and apply formative assessment in their classrooms. They are motivated to engage in and take charge of their own education. Students identify gaps in their own understanding and try to fill them while the teacher outlines learning goals, gives clear criteria for success, and provides timely feedback. According to this study's findings, teachers may require assistance in areas such as establishing a student-centered environment, implementing individual and peer assessment and sharing, and formulating student success criteria.
Although there is some evidence that using technology to aid learning might help students learn better, the research base is fragmented (Haßler et al., 2016). According to Dalby and Swan (2019), some of the major capabilities of technology that might increase formative assessment methods include rapid assessment, quick and targeted feedback, interactive learning, assessment, and monitoring. This suggests that technology can aid formative assessment and contribute to student learning, but it does not explain how effective procedures involving teachers, students, and technology are developed. The ability of digital technology to collect and analyze data on a large scale and at a fast rate implies that it may be a valuable tool for teachers to use in formative assessment (Wright et al., 2018). In formative processes, digital technology may also help and empower students by giving feedback that students can access independently as a replacement or complement to teacher-led processes.
There are still significant obstacles to establishing good formative assessment practices. The most significant subthemes of obstacles and challenges depicted in the transcripts of the teachers’ interview are exhaustion and more burdens. In reality, it appears that if teachers were requested to do so under normal conditions, neither technique would be accepted and used freely. The strong wording refers to the teacher's fear of the load and tiredness that come with the preparation and implementation of such instructions. Formative assessment implementation may be hampered by poor teaching environments. Large class sizes may make it difficult for teachers to provide individualized attention to each student (Browne, 2016). Furthermore, concerns that formative assessments will be time- and resource-intensive, especially in light of the broad curricular requirements, add to their view as an administrative burden for teachers (Browne, 2016).
Student background is another major subtheme that stands out in terms of potential obstacles and issues encountered throughout the implementation of both treatments. Students’ willingness to implement and benefit from the interventions is too low at the start, according to both transcripts. The majority of them were also found to be resistive to the interventions’ instructions and arrangements. Throughout the interventions, several students in the observed sessions were reluctant and unwilling to mark, provide feedback, and remark on their classmates’ assessment performance. This was partially owing to a lack of self- and peer-assessment monitoring in various courses. Ghaffar et al. (2020) discovered that students typically make use of existing knowledge and talents. Furthermore, while being in high school, the students appeared to have little experience or ability in giving remarks to their classmates, which the participants described as a significant obstacle to FA adoption. As a result, teachers are the only ones who adopt FA techniques, with no engagement from students (Wiliam and Leahy, 2016). As a result, there is a compelling need to increase students’ understanding of FA and their vital role in the learning process so that they can be involved in accomplishing learning goals.
The issue of class time limits and the lack of opportunities to include FA in the learning process were also raised by the participants. This is a problem with the educational system as a whole, not with individual teachers. Teachers must think about this topic from a variety of angles. They may spend a complete session to the presentation of new knowledge, followed by another class to completing assessments, and so on, to balance time between different tasks and FA implementation. Other study supports this claim, indicating that curricular needs and time constraints are real problems (Alotaibi, 2019; Bayissa and Jote, 2019). Despite competing goals, teachers are attempting to overcome these obstacles and create a climate conducive to good formative assessment.
Overall, the current study's findings were mainly similar to other research findings in terms of the difficulties of implementing FA techniques by chemistry teachers, as well as the impact of the teachers’ necessary expertise to implement FA successfully. According to the study's findings, teachers, for example, were more aware of slightly agreeing in all of the given factors, according to the study's findings, which were similar to those of Vlachou (2015), who discovered that teachers are more reactive in conducting assessments rather than proactive in anticipating assessment challenges. However, the authors claim that significant difficulties in assessments are always acknowledged by teachers as a consequence of role ambiguities and contradictory expectations in formative assessment, as well as teachers’ devalued positions in schools as a result of expanding their assessment duties.
Conclusion
Formative assessment is being used by educators all around the world to better comprehend student knowledge and communicate with students about how they may develop. This necessitated the development and implementation of assessment procedures in order to measure their impact on students’ learning outcomes. Integration of technology into classrooms has also become a must for effective teaching that enhances learning, particularly in the twenty-first century, when students’ desire for technology and digital tools inspires and motivates them to study. Teachers may now use a variety of technological tools for formative assessment because of the growing availability of technology. As a consequence, the impacts of technology-supported formative assessment strategies on students’ conceptual and procedural knowledge learning chemistry in general and chemical equilibrium in particular, were investigated in this study.This research's most obvious finding is that using technology-based tools and software enhances the use of formative assessment, which enhances student learning outcomes. Additionally, it has been found that when a teacher uses technology resources like software and tools for formative assessment, student learning outcomes improve, resulting in the development of a productive learning environment that fosters the understanding of chemical equilibrium concepts. Furthermore, these technological resources and software support the delivery of individualized instruction, creating a setting for teaching and learning that is more successful.
In light of this, the study discovered that when teachers are committed to utilizing technology-supported formative classroom assessment with a timely feedback system, student achievement will be favorably enhanced. They were exposed to different formative assessment techniques, such as teacher-guided individual and peer assessments, projects, and group assignments, in place of the exams and take-home assignments they were used to. The results of this study show that using formative assessment for diagnostic purposes enhances students’ conceptual and procedural knowledge in learning chemistry in general, and chemical equilibrium in particular, as well as gives them the opportunity to comprehend the subject's contents more thoroughly than when using conventional methods.
The study's several formative assessment strategies with feedback also enabled teachers to actively participate in the evaluation of their students. Teachers were able to acquire the skills they needed to build a variety of formative assessment procedures that were crucial for the evaluation of their students, as well as to change their teaching strategies in a way that promoted learning. Additionally, teachers were able to analyze and track their students’ academic progress on a weekly basis while also providing quick feedback to help students improve their conceptual and procedural knowledge by applying these formative assessment techniques. Also, incorporating technological resources and software promotes feedback and enhances the lesson's appeal, fun, and educational value.
The primary conclusion drawn from the observations made in the classroom and the transcripts of the interviews was that there are still a lot of barriers standing in the way of implementing effective formative assessment practices. The biggest problems that prevent good practice are the workload of the teachers, the backgrounds of the students, the size of the classroom, and the lack of time to complete all formative tasks within a given period of time.
Implications of this study
The results of this study have practical implications for both chemistry teachers and students. Findings may help chemistry teachers in developing and implementing better teaching interventions to improve students’ thinking, particularly in the area of integrating higher-order thinking skills into the instruction of chemistry in general and chemical equilibrium in particular. This study also intends to give insight into how technology-supported formative assessment may be utilized effectively in science education in general and chemistry education in particular. This study also provides further resources for researchers and educators to adopt and/or create acceptable formative assessment procedures in their own classrooms. This study's findings will assist teachers, educators, instructors, policy makers, curriculum designers, and educationalists in developing technology-supported formative assessment strategies and evaluating the success of the formative assessment and feedback process. Furthermore, this study might serve as a guide for other academics who want to expand their research into different domains. It can also be an alternate platform on which a variety of frameworks for secondary school chemistry studies can be explored. As a consequence, the findings will greatly contribute to existing research on technology-integrated formative assessment strategies.Suggestions for future research
More research is needed to determine the impact of technology-supported formative assessments as a teaching tool on students’ learning results utilizing different chemical themes than the one employed in this study. More study is also needed to see how TSFA might be employed in educational contexts other than secondary school. Research that examines all cognitive knowledge, including metacognitive knowledge, is needed. In addition, there is also a need for a study that considers all affective, cognitive, and psychomotor domains.Conflicts of interest
The authors state that there were no commercial or financial ties that may be considered as a possible conflict of interest during the research.Appendices
Appendix A. Testing statistical assumptions
| Dependent variable | Group type | Test of normality | |||||||
|---|---|---|---|---|---|---|---|---|---|
| N | Skewness | SE | z-value | Kurtosis | SE | z-value | Sig. | ||
| Pre-conceptual knowledge test | TSFA | 45 | 0.59 | 0.35 | 1.69 | 0.97 | 0.70 | 1.39 | 0.230 |
| FA only | 43 | −0.05 | 0.36 | −0.14 | 0.73 | 0.71 | 1.03 | 0.301 | |
| CM | 44 | 0.20 | 0.36 | 0.56 | −0.26 | 0.70 | −0.37 | 0.373 | |
| Pre-procedural knowledge test | TSFA | 45 | 0.08 | 0.35 | 0.23 | −0.51 | 0.70 | −0.73 | 0.007 |
| FA only | 43 | 0.49 | 0.36 | 1.36 | 0.35 | 0.71 | 0.49 | 0.000 | |
| CM | 44 | 0.33 | 0.36 | 0.92 | 0.61 | 0.70 | 0.87 | 0.000 | |
| Post-conceptual knowledge test | TSFA | 45 | 0.06 | 0.35 | 0.17 | −1.22 | 0.70 | −1.74 | 0.130 |
| FA only | 43 | −0.59 | 0.36 | −1.64 | −0.65 | 0.71 | −0.92 | 0.170 | |
| CM | 44 | −0.45 | 0.36 | −1.25 | −0.52 | 0.70 | −0.74 | 0.120 | |
| Post-procedural knowledge test | TSFA | 45 | −0.27 | 0.35 | −0.77 | −1.25 | 0.70 | −1.79 | 0.080 |
| FA only | 43 | −0.07 | 0.36 | −0.19 | 0.20 | 0.71 | 0.28 | 0.051 | |
| CM | 44 | 0.13 | 0.36 | 0.36 | −0.96 | 0.70 | −1.37 | 0.060 | |
| Dependent variables | Levene's statistic | df1 | df2 | Sig. |
|---|---|---|---|---|
| Pre-test conceptual knowledge | 0.03 | 2 | 129 | 0.969 |
| Pre-test procedural knowledge | 1.94 | 2 | 129 | 0.148 |
| Post-test conceptual knowledge | 1.07 | 2 | 129 | 0.345 |
| Post-test procedural knowledge | 0.44 | 2 | 129 | 0.646 |
Appendix B. Descriptive statistics results from the classroom observation checklist
Tables 11 and 12.| Domain of formative assessment | Teacher C | Teacher A | Teacher B | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Obs.1 | Obs.2 | Obs.3 | Obs.1 | Obs.2 | Obs.3 | Obs.1 | Obs.2 | Obs.3 | |||
| Domain A | Descriptive statistics for sharing learning intentions and success criteria | ||||||||||
| Connection to future learning | 2 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 3 | ||
| Learning goal quality | 1 | 1 | 1 | 2 | 2 | 2 | 3 | 3 | 3 | ||
| Learning goal implementation | 1 | 1 | 1 | 2 | 2 | 2 | 3 | 3 | 3 | ||
| Presentation of criteria | 1 | 2 | 1 | 2 | 2 | 2 | 3 | 3 | 3 | ||
| Average | 1.25 | 1.5 | 1 | 2 | 2 | 2 | 2.75 | 2.75 | 3 | ||
| Domain B | Descriptive statistics for effective classroom questioning | ||||||||||
| Use of questioning | 1 | 2 | 1 | 3 | 3 | 3 | 3 | 3 | 3 | ||
| Wait time | 1 | 1 | 1 | 2 | 3 | 3 | 3 | 3 | 3 | ||
| Eliciting evidence of learning | 1 | 1 | 1 | 3 | 3 | 3 | 3 | 3 | 3 | ||
| Determining progress | 1 | 1 | 2 | 2 | 3 | 3 | 2 | 3 | 3 | ||
| Average | 1 | 1.25 | 1.25 | 2.5 | 3 | 3 | 2.75 | 3 | 3 | ||
| Domain C | Descriptive statistics for effective classroom collaboration | ||||||||||
| Climate | 2 | 2 | 2 | 3 | 3 | 3 | 3 | 3 | 3 | ||
| Student collaboration | 2 | 2 | 2 | 2 | 3 | 3 | 3 | 3 | 3 | ||
| Student viewpoints | 2 | 2 | 2 | 2 | 2 | 3 | 3 | 3 | 3 | ||
| High expectations | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 3 | 3 | ||
| Average | 1.75 | 2 | 2 | 2.25 | 2.5 | 2.75 | 2.75 | 3 | 3 | ||
| Domain D | Descriptive statistics for learning tasks implementation | ||||||||||
| Connection to learning goals | 1 | 1 | 1 | 3 | 3 | 3 | 3 | 4 | 4 | ||
| Clarity of task | 1 | 2 | 1 | 3 | 3 | 3 | 3 | 3 | 4 | ||
| Adjust instruction within the lesson | 1 | 1 | 1 | 3 | 3 | 3 | 3 | 3 | 3 | ||
| Use evidence to inform future instruction | 1 | 1 | 2 | 2 | 3 | 3 | 3 | 3 | 3 | ||
| Average | 1 | 1.25 | 1.25 | 2.75 | 3 | 3 | 3 | 3.25 | 3.5 | ||
| Domain E | Descriptive statistics for instructional feedback | ||||||||||
| Assessing progress during lesson | 1 | 2 | 1 | 2 | 2 | 2 | 2 | 3 | 3 | ||
| Individualized feedback | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ||
| Self-assessment | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 2 | 2 | ||
| Peer assessment | 1 | 1 | 2 | 2 | 1 | 1 | 2 | 2 | 2 | ||
| Feedback loops | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | ||
| Average | 1 | 1 | 1.2 | 1.2 | 1.2 | 1.2 | 1.8 | 2 | 2 | ||
| Descriptive statistics by subscale in the formative assessment classroom observation protocol (FACOP) | |||||||||||
| Teacher C | Teacher A | Teacher B | |||||||||
| Learning intentions and criteria | 1.25 | 1.5 | 1 | 2 | 2 | 2 | 2.75 | 2.75 | 3 | ||
| Effective classroom questioning | 1 | 1.25 | 1.25 | 2.5 | 3 | 3 | 2.75 | 3 | 3 | ||
| Effective classroom collaboration | 1.75 | 2 | 2 | 2.25 | 2.5 | 2.75 | 2.75 | 3 | 3 | ||
| Learning tasks implemented | 1 | 1.25 | 1.25 | 2.75 | 3 | 3 | 3 | 3.25 | 3.5 | ||
| Feedback on instruction | 1 | 1 | 1.2 | 1.2 | 1.2 | 1.2 | 1.8 | 2 | 2 | ||
| Average | 1.2 | 1.4 | 1.675 | 2.14 | 2.34 | 2.39 | 2.61 | 2.8 | 2.9 | ||
| Total mean | 1.43 | 2.29 | 2.77 | ||||||||
| Score value | Score range |
|---|---|
| Beginning | 1.00–1.75 |
| Developing | 1.76–2.75 |
| Effective | 2.76–3.75 |
| Exemplary | 3.76–4.00 |
Appendix C. Samples of conceptual and procedural test questions
Part I: Example questions for conceptual knowledge1. Calcium carbonate decomposes to form calcium oxide and carbon dioxide according to the equation: CaCO3(s) + heat ⇌ CaO(s) + CO2(g).
After the system reaches equilibrium in a closed container, extra solid CaCO3 is added to the equilibrium mixture. What will happen to the concentration of carbon dioxide after addition?
A. Remains unchanged, because the concentrations of pure solids, that is, the quantities in a given volume or densities, are constant
B. Increases, because increasing the amount of CaCO3 (s) that is at equilibrium causes more dissolved ions to be produced.
C. Decreases, because CaCO3 (s) is added to the reactants’ side, equilibrium will shift the products’ side
D. Increases, because CaCO3 (s) is added to reactants’ side, equilibrium will shift the reactants’ side.
2. Given the following reaction: CO2(g) + H2(g) ⇌ CO(g) + H2O(g) Kc= 0.137
If the temperature of the system at equilibrium is increased, then the concentration of the carbon dioxide and the value of Kc will
A. decrease and stay the same, respectively B. increase and stay the same, respectively
C. increase and decrease, respectively D. decrease and increase, respectivel
3. If you have a 0.5 M solution of sodium dichromate (Na2Cr2O7) in which the following equilibrium is established
2CrO 4 2− (aq) + 2H + (aq) ⇌ Cr2O72−(aq)+ H2O(l)
Yellow orange
And you add 10 mL of 0.5 M solution of sodium dichromate to the original solution what would you observe?
A. the solution becomes yellow, because to counteract the increased amount of Cr2O72−(aq) the system will form more CrO42−(aq)
B. the solution becomes deeper orange, because there will be more collisions between particles of Cr2O72−(aq) and H2O(l)
C. the solution becomes yellow, because of the increase in Cr2O72−, Q will be greater than Keq
D. the solution remains unchanged, because there is no change in the concentration of any species
Part II: Example questions for procedural knowledge
1. For the reaction AB(g) ⇌ A(g) + B(g), at equilibrium, AB is 20% dissociated at a total pressure of P, The equilibrium constant KP is related to the total pressure by the expression
A. P = 24 KP B. P = 8 KP C. 24 P = KP D. None of these
2. Equimolar concentrations of H2 and I2 are heated to equilibrium in a 1 liter flask. What percentage of the initial concentration of H2 has reacted at equilibrium if the rate constants for both forward and reverse reactions are equal
A. 33% B. 66% C. (33)2% D. 16.5%
3. A mixture of 9.22 moles of A, 10.11 moles of B, and 27.83 moles of C is placed in a one-liter container at a certain temperature. The reaction is allowed to reach equilibrium. At equilibrium the number of moles of B is 18.32. What is the equilibrium constant for the reaction?
A(g) + 2 B(g) ⇌ 3 C(g)
A. 0.732 B. 0.632 C. 0.832 D. 0.932
Acknowledgements
This is one part of a PhD dissertation that the first author has been working on with the guidance and support of the second author as a supervisor. The entire PhD study is sponsored by the joint cooperation and agreement of Addis Ababa University and Education Bureau of Amhara Regional State, Ethiopia. We, the aforementioned authors, therefore declare that neither the university nor the Bureau has any significant competing financial, professional, or institutional interests that might have influenced publication of this article in this journal or somewhere else. We thank the students and teachers, who participated in this study. We also wish to thank many others for their assistance, support, and guidance.References
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