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DOI: 10.1039/C8RP00165K (Paper) Chem. Educ. Res. Pract., 2019, 20, 288-301

The effects of microcomputer-based laboratories on students macro, micro, and symbolic representations when learning about net ionic reactions

Jianqiang Ye , Shanshan Lu and Hualin Bi *
College of Chemistry, Engineering and Materials Science, Shandong Normal University, Ji’nan, Shandong, China. E-mail: bihualin@sdnu.edu.cn

Received 6th July 2018 , Accepted 7th November 2018

First published on 7th November 2018

Abstract

This study uses graphs of conductivity measured by a microcomputer-based laboratory (MBL) to promote students’ macro, micro, and symbolic representations when learning about net ionic reactions (NIR). A total of 54 students, aged 14–15 years old participated in this research, and were randomly divided into an experimental group (N = 27) and a control group (N = 27). The students in the experimental group were given graphs of conductivity measured by MBL, while the control group had a demonstration of acid–base titration experiments. The results reveal that the graphs of conductivity have a large effect on students’ macro, micro, and symbolic representations, that is, the students in the experimental group build more representations than the students in the control group.

Introduction

Chemistry is sometimes viewed as a difficult discipline because it requires students to transfer between macro (observable, concrete), microscopic (invisible, abstract), and symbolic levels of reaction (Johnstone, 1991). Several chemical education researchers have focused their efforts on describing students’ conceptions regarding the reactions in aqueous solutions (Fensham, 1987; Ebenezer, 2001; Çalýk et al., 2005; Devetak et al., 2009; Barke et al., 2009; De Berg, 2012; Eslek and Tulpar, 2013; Adadan and Savasci, 2012; Adadan, 2014). Net ionic reaction (NIR) is one topic that needs students to transfer between different representations in solution chemistry. Specifically, ions change in a solution at the microscopic level, with a macroscopic observable color change of solution and precipitation, and it needs students to write net ionic equations, which is at the symbolic level. Many researchers have found that students find it difficult to understand the representation of particles existing in solution (Ebenezer, 2001; Naah and Sanger, 2012, 2013; Adadan, 2014), and also the electrostatic interaction of opposite charged ions (Sendur, 2014). College and high-school students often miss the important fact that solutions of all soluble salts, strong acids, and strong bases are ionic, containing positive and negative ions that are extensively associated (Martin, 1999). One reason is due to the abstract idea of the ions charge that students cannot “see” at the microscopic level.

Mastering the concept of NIR helps students recognize the micro nature of reactions of electrolytes in aqueous solution, and enables students to learn the solution chemistry from the macro level into the particulate level (Wruck, 1996). However, most previous researchers have found that students have difficulty understanding the micro level, and connecting macro, microscopic and symbolic levels to learn NIR (Laugier and Dumon, 2004; Barke et al., 2009; Smith and Nakhleh, 2011; Cigdemoglu and Geban, 2015; Nyachwaya, 2016; Wang et al., 2017). For example, Laugier and Dumon (2004) explored students’ understanding of the reaction between iron and hydrochloric acid, the nature of this reaction is that iron reacts with hydrogen ions to form ferrous ions and hydrogen. The result revealed that students could write the above net ionic equation correctly, but showed great difficulty in drawing the particle representations of this NIR. Similarly, Barke et al. (2009) found that even if a net ionic equation had been given to students directly, they were still unable to represent the ions that actually participated from the particulate level. It is difficult for students to transfer from the macro perceptible properties to the particulate imperceptible level (Çalýk et al., 2005; Kelly and Akaygun, 2016; Kelly et al., 2017).

Lots of studies have made efforts to work out how to help students “see” the ions change in the solution. One approach is to create computer animations of chemical reactions at the particulate level (Williamson and Abraham, 1995; Ebenezer, 2001; Sanger and Greenbowe, 2000; Kelly and Jones, 2008; Gregorius et al., 2010a, 2010b; Kelly and Akaygun, 2016; Kelly et al., 2017). Wu et al. (2001) point out that visualization such as animations could facilitate students to formulate a representation of physical and chemical phenomena and procedures. According to Kelly and Jones (2008), animations of micro chemistry processes could help students visualize molecular structure and dynamics. Although these animations provide a concrete picture of ions changing for students to understand the process of the reaction in water, students still persisted with their own misunderstanding. Only making the ions change visibly with animation seems to be ineffective for students to understand NIR. For example, Ebenezer (2001) provided 11th grade students with an animation of sugar dissolving in a hypermedia environment, half the students who had a misconception insisted that their views were consistent with what they had seen in the animation. One reason as Laugier and Dumon (2004) pointed out, is the lack of transformation from the microscopic representation to other representations. They stated that to better understand any of the reaction, the necessity was to know what is happening in the bulk situation with the underlying atomic and molecular changes, that is, switching between microscopic and macro.

Additionally, Naah and Sanger (2012) explored students’ misinterpretations and misconceptions when learning NIR using computer animations. The result turned out that animation negatively impacted students’ explanations of the source of the blue colour in the reaction of aqueous silver nitrate and solid copper metal. Kelly and Akaygun (2016) stated that although animation portrayed micro mechanisms, students still did not fully understand why or how these particulate level representations and mechanisms account for macro evidence, and they fail to establish a connection between the micro and macro level. Naah and Sanger (2013) considered that the color of animation made students believe that particles themselves are colored, which led students to construct wrong relationships between micro nature and macro phenomena. Kelly et al. (2017) pointed out that it is crucial that teachers should help students make connections between macro laboratories and the micro level when learning chemical reactions, because these connections will promote students’ thinking skills.

To connect microscopic and macro, some researchers designed classroom experiments based on technology which combines the ionic change by data collection and display on screen, with the experimental phenomena in the laboratory setting (Linn, 2003). The data collection and display in graph form is time-consuming work, thus microcomputer-based laboratory (MBL) is popular with many researchers to capture and display data automatically (Russell et al., 2004; Struck and Yerrick, 2010; Barnea et al., 2010). Although MBL has the advantage of displaying data for students, it failed in teaching practice (Russell et al., 2004). One reason was that teachers did not guide students to interpret data by connecting the microscopic and macro level. Analyzing and interpreting data is one of the eight scientific practices reported in current documents (National Research Council, 2011, 2013). Once collected, data must be interpreted to have meaning. The process of data interpretation includes connecting disciplinary knowledge with the information visualized. NIR is the reaction relating to the ions changing in solution, the concept of conductivity is introduced as a measure of the number of free ions in solution (Eslek and Tulpar, 2013). In this study, we focus on how to resolve students’ learning difficulties when learning NIR. As NIR is a reaction about ion change, the number of ions can be easily measured by MBL.

The value of MBL in chemistry learning has been confirmed by lots of empirical studies, but there is few instructional research about promoting students’ macro, micro, and symbolic representation based on MBL (Friedler et al., 1990; Rogers, 2008), and few studies pay close attention to how MBL can help students’ macro, micro, and symbolic representations in learning chemical concepts as well (Pierri et al., 2008; Davies et al., 2012). For instance, Tortosa (2012) designed a lab-sheet as an inquiry-guided learning cycle for chemical conceptual learning such as pH, carbon dioxide, and organic volatile compounds based on a pedagogical intervention of predict–experiment–observe–explain through MBL, which inspired interest in learning and several competencies and higher order learning skills in students. Chen et al. (2014) investigated the effects of MBL on inquiry, conceptual learning and attitude based on an order of instruction: planning, experimenting, evaluating, improving the experiment, experimenting, evaluating and concluding, and designing a new experiment. The result showed that MBL contributed to inquiry practice and increased the enjoyment of the laboratory, but no significance in conceptual learning.

It can be found that most pedagogical research (Linn, 2003; Russell et al., 2004; Struck and Yerrick, 2010; Tortosa, 2012; Chen et al., 2014) involving MBL gives attention to inquiry activities, which may promote students’ ability in practical work and interest in learning chemistry, but failed to cultivate students’ chemical thinking such as connecting the macro, micro and symbolic levels. Although the particulate representations have been taken seriously by some instructional studies, most of them neglected the importance of students’ ability to connect macro, microscopic and symbolic levels. Additionally, considering that the MBL activity is not a structured inquiry we need to ensure that teaching interventions are completed within the prescribed time. The pedagogical approach in this study is mainly based on Tortosa's (2012) four-step sequential guide.

Therefore, this study aims to explore the effects of MBL on macro, micro, and symbolic representations of NIR. In this paper, we apply MBL to NIR learning to collect data and display graphs of the conductivity in the solution, and guide students to interpret the information in the graph based on the macro–micro-symbolic representations. Our question is as follows: comparing conventional experimental teaching, what is the effect of MBL activities on junior high school students’ macro–micro-symbolic representations of NIR? Specifically, it can be separated into three parts: what are the effects of MBL activities on students’ macro representation of NIR? What are the effects of MBL activities on students’ micro representation of NIR? What are the effects of MBL activities on students’ symbolic representation of NIR?

Theoretical framework

Macro, micro, and symbolic representation

Johnstone (1991, 1993) proposed the relevance of the triplet relationship based on chemical thinking levels, which contain three levels: the macro, microscopic and the symbolic. The macro area is what can be seen, observed, and smelt; the micro area is the model of the matter which is used to explain or predict the properties of substances; the symbolic area is the representation of the macro and micro area. Johnstone (2000) emphasized the importance of linking among these three levels in helping students to learn chemical concepts.

The graph is a part of the symbolic learning in Johnstone's original point. The graph is a symbol system particularly well adapted to convey information about experimental measurements (Gilber et al., 2008). A graph constitutes a key symbol system in science because it summarizes the covariance of two or three variables over a large number of measurements. It also allows us to use our powerful visual pattern recognition facilities to see trends and spot subtle differences in shape (Shaw et al., 1983). Talanquer (2011) stated that graphs were used to visually represent core components of a theoretical model. Taber (2013) pointed out that the graphs are useful in thinking about communicating chemical concepts.

Net ionic reaction (NIR)

The NIR is the reaction focusing on ions reacting in solution. Martin (1999) described that the NIR is a much more accurate representation of double replacement reaction. Solutions of all soluble salts, strong acids, and strong bases are ionic. For example, when a solution of H2SO4 containing the ions H+ and SO42− is combined with a solution of Ba(OH)2 containing the ions Ba2+ and OH−, the equation is:
image file: c8rp00165k-u1.tif

The BaSO4 formed in the above reaction is an insoluble substance; that is, a solid forms and separates from the solution. Such a reaction is called a precipitation reaction, and the solid that forms is called the precipitate (College Board, 2014, pp. 140).

Since HCl and NaOH react in the solution, there is no solid product. The equation is:

image file: c8rp00165k-u2.tif

This equation is called the complete ionic equation; all substances that are soluble strong electrolytes are represented as ions. The complete ionic equation reveals that only some of the ions participate in the reaction. The Na+ and Cl− ions are present in solution both before and after the reaction. The ions that do not participate directly in the reaction are called spectator ions. The ions that participate in this reaction are the H+ and OH− ions, which combine to form liquid H2O:

image file: c8rp00165k-u3.tif

This equation, called the net ionic equation, includes only those solution components directly involved in the reaction. Spectator ions are not included in net ionic equations. The former equation of H2SO4 and Ba(OH)2 is also a net ionic equation.

Microcomputer-based laboratory (MBL)

Microcomputer-based laboratory (MBL) is the measuring equipment used for expending our physical intuition. It has been used for over 30 years in science education since it was developed by Tinker (1981) (Linn, 1987; Thornton, 1987, 1992; Nakhleh, Krajcik, 1994; Russell et al., 2004; Struck and Yerrick, 2010). Priest et al. (2014) gave a description of MBL, which students use hand-held graphing data logger devices specifically designed to display and analyze data collected from associated probe ware, or alternately the probe ware may be connected to a notebook or desktop computer equipped with the necessary software. Fig. 1 shows the MBL equipment.
image file: c8rp00165k-f1.tif
Fig. 1 The picture of MBL used in this paper.

MBL is composed of a set of devices. The three main parts of MBL can be seen in Fig. 1. The probe ware with the sensor at the head is used to measure values of variance and to collect data, and the screen is used to demonstrate the graphs of data equipped with necessary software.

Struck and Yerrick (2010) set four reasons to use MBL as a powerful tool in learning concepts. Firstly, MBL uses multiple modalities to expand our visual experience of seeing the physical phenomenon change. For example, MBL helps us to “see” the change of conductivity in the solution in this paper, which we cannot experience directly in conventional experiments. From the graph in the screen, students can “see” the decreasing pitch of a negative slope and rising pitch of a positive slope. Secondly, MBL provides a real-time link between a concrete experience and the symbolic representation of that experience. MBL may be a bridge between concrete and abstract. Thirdly, MBL brings a unique level of understanding of the data to the graph when the data comes from an experiment. Since MBL experiments do not have graph-skill development as a primary goal, students can use these graphs to understand phenomena. Lastly, it eliminates the drudgery of graph production. While point-by-point graph production is important in giving children a concrete sense of graphs, we argue that children need very few of these exercises in graphing penmanship, then they understand the meaning and utility of graphs.

Tortosa (2012) proposed a four-step sequence to guide MBL activities in chemistry classrooms: prediction, experimental preparation, observation and explanation. Firstly, students need to predict the evolution of the phenomena. Then teachers/students get the equipment ready to perform the experiment. Thirdly, students are required to observe the process of data collection (the production of the graphs) as well as the macro phenomena of the reaction occurring in a conical flask. In the end, students need to compare results with their predictions and confront results with theoretical models. For example, Tortosa (2012) designed a four phase learning-cycle for learning the concept of the rate of reaction. The activities that are proposed to be carried out using a predict–observe–explain approach as described above. Specifically, students are guided to (i) observe several reactions measuring their time of reaction and classify them as “quick” or “slow”, (ii) students obtain data of pressure variations and they calculate the rates of reaction at different intervals of time of the reaction between calcium carbonate and hydrochloric acid, (iii) after which students are taught the collision theory and about efficient collisions, then (iv) draw their conclusions to answer the initial question.

This study designed MBL activities based on Tortosa's (2012) pedagogical model. Specifically, students not only need to explain the micro nature of two acid–base titrations by observing macro phenomena which occurrs in a breaker, but are also required to observe the variations of the conductivity curve which were presented by MBL during the titrations. Then, students were asked to explain and describe the micro nature of the above reactions. In the end, students were called upon to write the balanced chemical equation of the two reactions based on their previous observations of macro phenomena and explanations of the sub-micro nature of those titrations. After students participated in the above two titrations conducted by MBL, researchers would help students summarize the micro nature of these two titrations, and introduce the concept of NIR and teach students how to write a correct net ionic equation based on their experience. In the teaching practice of NIR, researchers not only pay attention to the cultivation of students’ ability of particulate representation, but also try to promote students to learn the concept of NIR by focusing on building connections among macro, micro, and symbolic levels.

Methods

Research design

This study conducts MBL activities for NIR teaching. A total of 54 junior students voluntarily took part in this study. They came from the same class in the same school. We have two treatments: one is conducting MBL activity, and the other is conducting conventional experiments. Since we have two treatments, we randomly assigned 54 students into two groups considering their student ID. Specifically, one group with all odd numbers and the other with all the even numbers. Then we used a coin toss to separate these two groups into the experimental group and the control group, and randomly assigned two interventions to these groups.

The researchers believe that gender has an effect on the results. After random assignment, we checked the number of male and female students in each group. As a result, there are 13 female students and 14 male students in the experimental group, and 12 female students and 15 male students in the control group. A Chi-square test of the gender was performed to examine the difference between the experimental group and the control group. There is no significant difference in the two groups, χ2(1) = 0.074, p > 0.05. The students’ previous achievements may also affect the results and to ensure that the control group and the experimental group are as equal as possible before the intervention, both groups participated in a regular chemistry test. Then, an independent-samples t-test was performed, the results show that there is no significant difference t = 1.698, p > 0.05.

The independent variable is the treatment. Previous studies proved that students had difficulty in using the computing devices themselves (Rogers and Wild, 1996; Newton, 2000; Tortosa, 2012; Davies et al., 2012). For instance, Tortosa (2012) investigated students’ practical work with MBL in the chemistry classroom, the results revealed that students did not like to use MBL because “it was complex” and “incoherent”. Davies et al. (2012) found that students had difficulty in using a datalogger and sensors as well as collecting monitoring data. Moreover, students had negative views of handheld graphic data logger devices because of the unfamiliar user interface of MBL (Priest et al., 2014). On the other hand, MBL is difficult to be used in a classroom presentation by the teacher due to many unpredictable factors such as sensor errors, operation failure, time out and so on (Russell et al., 2004). Struck and Yerrick (2010) reported that digital video analysis (DVA) allows the production of graphs from captured video events and analyzes the subsequent graphs produced. Since our focus is students’ macro, micro, and symbolic representations and not graphing skills or practical work, we made videos for the MBL activity and the conventional experiments before teaching. Specifically, that is the comparison between measuring conductivity in the experimental group and no measuring conductivity in the control group.

The dependent variable is students’ macro, micro, and symbolic representation level. As a graph is a symbol of a system, it provides information about experimental measurements. In this study, the measurement is the conductivity in the solution, which has a direct relationship with the amount or concentration of the ions. Our hypothesis is that the graphs of conductivities produced by MBL will have a positive effect on the students’ macro, micro, and symbolic representations. We designed a test of NIR for two groups of students after the intervention.

Before teaching, we did some preparation. By considering that the MBL activities and experiment demonstrations take a lot of time in the classroom, we manipulated both the MBL activities and experiments into a video with the same time before we conducted the lesson. The MBL activities and experiments were conducted several times by the first author until the curve and phenomenon are stable. Also, we discussed the materials designed according to the pedagogical approach mentioned above until three authors agreed. To avoid the potential effect on student learning due to the teacher change, the authors went into the school for one month in order to get familiar with students before the intervention. During teaching, to mitigate the John Henry effect, the students were not advised as to whether they were the control or experimental group. To mitigate the Hawthorn effect, one author taught two groups and the other author observed and made sure the teaching in both groups was conducted as discussed before the class.

Additionally, what needs to be acknowledged is some uncontrollable variables such as novel factors and a students’ expectation may have some effect on the results of this study, and it is impossible to avoid all these independent variables. Therefore, in order to ensure that the students in two groups have equal attitude and curiosity in learning NIR, students in the two groups did not know that different teaching interventions were being used.

Participants

The students in this study are 9th grade consisting of 54 students (aged of 14–15). There were 13 female students in the experimental group and 12 female students in the control group. Female students made up 48.1% and 44.4% in the experimental and control groups, respectively. Before intervention, in their 8th and 9th grade, they had already learned the related concepts as follows: solution, acid and base, neutralization, double-displacement reaction, and writing chemical equations. NIR is the continuation and deepening of the above mentioned core chemical concepts (Martin, 1999). Additionally, two groups of students both have been taught the concepts of electrolyte and ionization in advance by the researcher, because learning this concept could provide students with the basis for the conceptual learning of NIR.

MBL activities design

Two MBL activities of acid–base titrations are designed for NIR learning. One is titrating dilute sulfuric acid in barium hydroxide solution, and another one is titrating hydrochloric acid in sodium hydroxide solution. The MBL is produced by Vernie Company, which automatically records data and outputs adjusted graphs.

As the activity of titrating dilute H2SO4 (aq) in Ba(OH)2 (aq), it is designed for the purpose of building the connection between the ions and the conductivity. Since the net ionic formula is as formula (1) shows, there are almost no free ions left when they react totally, with the phenomenon of color change of the solution. Students can “see” the decreasing pitch of a negative slope until it reaches near zero in the graph as show in Fig. 2a. Then the students can “see” the increasing pitch of a positive slope when they keep titrating. In classroom teaching, after playing the video, we asked students to infer what happened to the ions in the decreasing pitch, at the bottom spot, and the increasing pitch.


image file: c8rp00165k-f2.tif
Fig. 2 The graphs of titrating made, and adjusted, automatically by MBL.

As the activity of titrating HCl (aq) in NaOH (aq), it is designed for the purpose of building the change of conductivity to the ionic reaction in the solution. Since the complete ionic equation (formula (2)) and net ionic equation (formula (3)) show, Na+ and Cl− ions are spectator ions. At the beginning of the titration, the similar trend of line as the former one appears in the graph. The graph made by MBL is shown as Fig. 2b. In the classroom, students were asked to propose a hypothesis about what ions react in the solution. Next, the video was stopped before reaction totally finished, and students were asked to predict whether the pitch would reach zero as the former one had. After students made their prediction, we ran the video again.

Treatment in the control group

Two identical experiments of acid–base titrations as for the experimental group are demonstrated in the control group. In order to ensure the completion of the conceptual teaching of NIR within prescribed class hours, and reduce effects of other independent variables as much as possible in teaching practice, the titration demonstrations in the control group we used are all videotaped in advance, as well as the recorded MBL titrations demonstrated in the experimental group. The only difference is no conductivity measurement based on MBL in the control group. Additionally, students in the control group also need to explain what happened in the solution according to the macro phenomenon of the color change in the solution.

Procedure

Each group has two classes, and there are a total of four classes, conducted in one day. Each class has 50 minutes. In the first class, both groups of students are treated in the same way to learn the related knowledge foundation of NIR. Those concepts include electrolyte and ionization. In the second class, students in the experimental group and in the control group are treated in different ways to learn NIR as shown above. After all the instruction, both groups of students take the test on NIR.

Before our intervention, we asked in detail whether the principal and chemistry teachers were willing to participate in the project or not and explained to them the goals, methods and meanings of the study. By ethical considerations of chemistry education research regularly involving “human subjects” (Taber, 2013), and after confirming the support and consent of the headmaster and the director of the chemistry department, their chemistry teachers explained to the students what kind of activity they were going to participate in, and informed the students that they must participate in the activity voluntarily. Moreover, chemistry teachers explained to the students in advance that their test scores will be used alone and this study will not affect their achievements in chemistry. Additionally, in early adaptation with the participants, the researchers (the first author and the second author) repeated that their responses to the test will be kept confidential and will not be shared with their teacher. All students voluntarily agreed to participate in the study and signed consent forms.

By considering schoolteachers may not be familiar with teaching with MBL, and to some extent students may feel strange with their chemistry teacher's some changes in the teaching. Another important factor taken into consideration is to ‘control the teacher variable’. For example, different teachers might do nothing to direct the teaching in the comparison condition, which represents what is ‘traditional’ in that context (Taber, 2014). Additionally, teachers may feel uncomfortable being trained by the researchers. Therefore, all four classes were taught by the researcher (second author) who had several years teaching experience in middle school as a part time job and is familiar with teaching with MBL. Moreover, another researcher (first author) who designed the activities is sitting at the back of classroom to ensure the teaching followed the teaching designs for the two groups.

Measurement instrument

Three items used to measure students’ macro, micro, and symbolic representations are developed in this study. They are all open-ended forms and written in Chinese. A chemistry teacher who is a native Chinese speaker and received his bachelor's degree from an English speaking country checked the accuracy of the translation and the content validity according to the Chinese chemistry curriculum. The English version can be seen in Appendix A.

The first item is to test students’ micro presentation of the NIR. We used the titration reaction of HCl and NaOH, which is the same as the second experiment. Students were asked to draw the particles that exist in the solution before, during, and after the reaction.

The second item is to assess students’ symbolic representation. Specifically, given the net ionic equation: Ba2+ + SO42− = BaSO4, the students were asked to write balanced molecular equations. Since the given net ionic equation contained particulate information (Ba2+ and SO42−) and macro information (white precipitate, BaSO4), the item also could evaluate students’ ability to transfer among symbolic, micro, and macro levels.

The third item is to assess whether the students can perform symbolic and macro representations of the NIR according to the particulate information contained in the solutions. Specifically, students need to write a net ionic equation that may occur between those two solutions and predict the corresponding macro phenomenon based on existing particulate information of the electrolyte in the aqueous solution.

In order to ensure the content validity of items, two experts reviewed whether the items assess students’ NIR understanding in macro, micro, and symbolic representations. Two experts’ comments are in high agreement, and they think these items can be used to assess students’ representations of NIR.

Scoring rubrics

The 51 copies of the test in total are gathered. Twenty-four students in the control group finished the test, 3 of them were absent, whereas all students of the experimental group completed the test. Then, we scored those students answers in 3 items according to the unified scoring rubrics. For example, for each item, if the answer was correct, the students scored 3, while for partially correct answers, they scored 1–2, and finally they scored 0 for incorrect answers or no answer. All scoring rubrics varied with the specific contents and evaluation criteria of the item. In order to ensure that all students can be scored objectively and avoid other unconscious biases that can occur in evaluating the tests, each grader individually examined the same 15 student responses according to the rubric, and then came to a consensus on each answer. Then, one of the scorers examined the remaining responses. Both the experimental group scores and the control group scores were obtained from using the same assessment procedure and all scores are rated in a double blind way. Scoring rubrics for item 2 are demonstrated in Table 1.
Table 1 Scoring rubrics for item 2
Scoring Performance criteria Samples
(0 point): no symbolic representations or false symbolic representations Balanced molecular equations did not conform to the net ionic equations. S9: AgNO3 + NaCl = AgCl↓ + NaNO3; Ag+ + Cl− = AgCl↓
S17: Ba(OH)2 = Ba2+ + 2OH−; NaOH = Na+ + OH−; H2SO4 = 2H+ + SO42−
(1 point): only one correct symbolic representation Only one balanced molecular equation that conforms to the net ionic equations. S21: BaCl2 + Na2SO4 = BaSO4↓ + 2NaCl; 2NaOH + CuSO4 = Cu(OH)2↓ + Na2SO4
S30: Ba(OH)2 + H2SO4 = BaSO4↓ + 2H2O
(2 points): multiple, but not exactly correct, symbolic representations Multiple balanced molecular equations that conform to the net ionic equations, but alternative frameworks exist such as confusion regarding the proper use of charge and coefficients. S10: BaCl2 + Na2SO4 = BaSO4↓ + 2NaCl; Ba(NO3)2 + H2SO4 = BaSO4↓ + HNO3; Ba(OH)2 + H2SO4 = BaSO4↓ + H2O
S49: BaCl2+ H2SO4 = BaSO4 + 2HCl; Ba(OH)2 + H2SO4 = BaSO4 + H2O; BaCl2 + Na2SO4 = BaSO4↓ + 2NaCl
(3 points): multiple completely correct symbolic representations Students write multiple balanced molecular equations that conform to the net ionic equations. S39: Ba(NO3)2 + H2SO4 = BaSO4↓ + 2HNO3; Ba(OH)2 + H2SO4 = BaSO4↓ + 2H2O; BaCl2 + H2SO4 = BaSO4↓ + 2HCl; BaCl2 + CuSO4 = BaSO4↓ + CuCl2

Results

The results of the conceptual test of the two groups are shown in Table 2. To accomplish the research goals, data from each item were analyzed for normality evidence. No item was found to have skewness or kurtosis greater than 1, which suggests good normality of the scores.
Table 2 The descriptive statistics of conceptual test of NIR
EG (N = 27) CG (N = 24) t p
M SD M SD
Note: * marks significance of p < 0.05; ** marks significance of p < 0.01; EG: experimental group, CG: control group.
T1 2.4 0.6 1.6 0.6 4.633 0.000***
T2 1.9 1.0 1.4 0.8 2.040 0.047*
T3(1) 0.8 0.4 0.7 0.5 0.870 0.389
T3(2) 1.9 0.4 1.5 0.7 2.237 0.030*
Total 6.9 1.4 5.1 1.8 3.873 0.000***

Therefore, an independent sample t-test was performed. The results show that students’ representations of NIR in the experimental group (M = 6.9, SD = 1.4) are significantly higher than those in the control group (M = 5.1, SD = 1.8), t = 3.873, p < 0.01. Significantly, a students’ ability to connect and transform among macro, micro, and symbolic levels in the experimental group was higher than the students in the control group, and there is a large effect with Cohen's d = 1.03. Specifically, a students’ micro representation of T1 in the experimental group (M = 2.4, SD = 0.6) is significantly higher than that in the control group (M = 1.6, SD = 0.6), t = 4.633, p < 0.001, with a medium effect, Cohen's d = 0.44. Students’ representation of T2 in the experimental group (M = 1.9, SD = 1.0) is significantly higher than that in the control group (M = 1.4, SD = 0.8), t = 2.040, p < 0.05, with a medium effect, Cohen's d = 0.56. Students’ representation on T3(2) in the experimental group (M = 1.9, SD = 0.4) is significantly higher than that in the control group (M = 1.5, SD = 0.7), t = 2.237, p < 0.05, with a medium effect, Cohen's d = 0.64. However, Students’ representation on T3(1) shows no significance between the experimental group (M = 0.8, SD = 0.4) and the control group (M = 0.7, SD = 0.5).

Knowing the effects of MBL activities on students’ learning, we chose 4 students in light of the action in the classroom and performance in NIR test. The first researcher who made an observation in the classroom and the second researcher who conducted the teaching, had high consistency on the selection of the 4 students. Then we conducted an informal group interview in order to know what they experienced in the MBL activities. We interviewed them around 2 questions; whether they thought MBL helps them to learn NIR in the classroom, and how they learn using MBL activities.

Their interviews are as follows:

Researcher: Did the graph help you learn the concept of net ionic reaction in this lesson?

Student 1: Yes. Because I can see the curve keep changing on the monitor.

Student 2: Exactly, the curve is changing all the time that makes me understand that there are some changes in the solution, although we can’t observe any change of color until one point of reaction completed.

Researcher: How did you learn NIR using the graph with the curve change?

Student 1: When I saw the curve, especially the lowest points of it, it is equivalent to a boundary, and the reactants (alkalis) in the left were not neutralized completely, while the right side is the addition of the excessive titrant (acids).

Student 3: Yes, it is. For example, when the conductivity reached the lowest point, the reactants were neutralized completely. At this point, the particles in the solution were at the lowest. We can easily get to that point… where the particles varied in the solution.

Student 4: That's true. We can infer that the particles in solution vary before and after net ionic reaction from the lowest point of the curve.

Student 1: The decreasing process of the curve equals the process of reaction between ions.

Student 2: When the curve rose again, the titrant was in excess and then no reaction happened between those ions.

Student 3: The variation of curve provided us with some quantitative information, and we can clearly get to know the behavior of particles at the different phases of reaction.

The students mentioned the changing curve, especially the lowest point of the curve, which they are interested in and they thought it was useful to learn about NIR. As student 3 said, “the particles in the solution were at the lowest”, and “the particles varied in the solution”. They connected the change of the curve with particles in the solution, even though they did not “see” the particles in the solution.

The effects of MBL on macro representation

The third item requests students to describe the macro phenomenon by inferring the two solutions with particles given. Students’ macro representation in two groups can be seen by contrast in Table 3.
Table 3 Samples of students’ macro representations
Macro representations Experimental group Control group
Note:a The numbers in the table represent the numbers of students.b The third one is the correct representation. The others are the errors of macro representation.
1. None 1a (3.7%) 1 (4.2%)
2. White precipitate and blue precipitate formed in the solution synchronously. 1 (3.7%) 0
3. Blue precipitate formed in the solution. 23 (85.2%)b 16 (66.7%)
4. Precipitate formed in the solution. 2 (7.4%) 2 (8.4%)
5. The color of the solution changes from colorless to blue. 0 1 (4.2%)
6. The color of the solution changes from colorless to blue, and white precipitate formed. 0 1 (4.2%)
7. Blue precipitate formed in the solution, and the solution turns from clear to turbid. 0 1 (4.2%)
8. The solution turns from clear to turbid and blue. 0 1 (4.2%)
9. The solution turns from clear to turbid and blue, and the upper layer is colorless. 0 1 (4.2%)

It can be seen that the percentages of right macro representations in the experimental group (85.2%) is higher than the control group (66.7%), but there is no significant difference in the result of the t-test above. Students in the experimental group show fewer errors in macro representation than that of students in the control group. For example, some students in the control group thought that the color of the solution changes from colorless to blue, or a white precipitate formed in the solution.

The effects of MBL on micro representations

The first item is designed to test the students’ micro representation. The item shows the picture of titration of sodium hydroxide by hydrochloric acid. The students are asked to draw the particles in the solution before, during, and after reaction.

A totally comprehensive level in drawing the particles existing in aqueous solution before and after the reaction was shown in the experimental group. One example of a particulate representation from the experimental group can be seen in Fig. 3.


image file: c8rp00165k-f3.tif
Fig. 3 An example of students’ drawing in the experimental group.

The student not only drew the reactants accurately, such as sodium hydroxide and hydrochloric acid, but also drew the micro process of NIR, that is, with the addition of hydrochloric acid, OH− ions in the solution of sodium hydroxide were gradually neutralized by H+ ions which were ionized by hydrochloric acid. It is also seen from Fig. 3 that the student drew the state of particles of sodium chloride in the aqueous solution precisely, indicating that he/she has understood the fact that Na+ and Cl− ions presented in solution belong to spectator ions.

Similarly, 85% of students in the experimental group draw the particles of reactants (sodium hydroxide and hydrochloric acid) and product (sodium chloride) in aqueous solution precisely, whereas, nearly half of the students of the control group thought that sodium hydroxide, hydrochloric acid and sodium chloride presented in the form of molecules in water.

Although participants knew the micro nature of reaction between sodium hydroxide and hydrochloric acid reaction, which is the process of the combination of H+ and OH− ions in the solution which produced water. However, students still demonstrated quantities of alternative conceptions in representing the particles during the different phases of reaction in water based on the analyses of their answers (see Table 4). It contained the following alternative conceptions: (i) sodium hydroxide and sodium chloride were present in the form of molecules in aqueous solution and did not draw the particles accurately which were ionized by sodium hydroxide and sodium chloride; (ii) there were two reactants of hydrochloric acid and sodium hydroxide in the solution before the reaction; (iii) water molecules in the solution before and after the reaction did not exist; (iv) spectator ions (Na+ and Cl− ions) existing in solution were neglected as well; and (v) hydrochloric acid still presented in solution when the reaction is complete, etc.

Table 4 Samples of students’ particulate representations and alternative conceptions
Samples Particulate representations Alternative conceptions
1 image file: c8rp00165k-u4.tif (1) Water molecules did not exist in solution.
(2) Sodium hydroxide and sodium chloride were present in the form of molecules in solution.
2 image file: c8rp00165k-u5.tif (1) Hydrochloric acid and sodium hydroxide existed in the solution before the reaction.
(2) Sodium hydroxide and hydrochloric acid were present in the form of molecules in solution.
(3) There was no sodium chloride in the product.
3 image file: c8rp00165k-u6.tif (1) Hydrochloric acid and sodium hydroxide existed in the solution before the reaction.
(2) Hydrochloric acid was present in the solution while no sodium chloride existed after the reaction.
(3) Water molecules did not exist in solution.

It was found that the number of alternative conceptions of particulate representation from the experimental group was much lower than the control group (see Table 5).

Table 5 Comparisons of alternative frameworks of particulate representation
Alternative conceptions Experimental group Control group
Note:a Some students may have multiple alternative conceptions, and the numbers in the table represent only the frequencies of alternative conception.
1. Sodium hydroxide was present in the form of molecules in aqueous solution. 1a (3.7%) 8 (33.3%)
2. Sodium chloride was present in the form of molecules in aqueous solution. 1 (3.7%) 7 (29.2%)
3. There are two reactants of hydrochloric acid and sodium hydroxide in the solution before the start of the reaction. 1 (3.7%) 12 (50.0%)
4. Hydrochloric acid was present in the form of molecules in aqueous solution. 0 (0%) 7 (29.2%)
5. Water molecules did not exist in solution. 12 (44.0%) 22 (91.7%)
6. No sodium chloride existed after the reaction. 3 (11.1%) 4 (16.7%)

The effects of MBL on symbolic representation

The second item provided a net ionic equation: Ba2+ + SO42− = BaSO4↓. The net ionic equation modelled the micro behavior of an ionic reaction and represented reactants and products that actually participated in a NIR (Phillips et al., 2008, p. 525). Therefore, this item aimed to examine students’ ability to connect particulate and symbolic levels.

Since the net ionic equation represented a group of chemical molecular reactions, students need to write balanced chemical equations as far as possible, which must conform to the above net ionic equation. It can be seen from Table 6 that the most popular answer is BaCl2 + H2SO4 = BaSO4↓ + 2HCl, and there were 19 and 12 students in the experimental group and in the control group, respectively, who wrote this chemical equation precisely. In addition, a number of students wrote the following balanced chemical equations: BaCl2 + Na2SO4 = BaSO4↓ + 2NaCl and Ba(OH)2 + Na2SO4 = BaSO4↓ + 2NaOH. The number of accurate chemical equations written by the experimental group was much higher than the control group. For example, 4 students in the experimental group gave the following answer: BaCl2 + CuSO4 = BaSO4↓ + CuCl2, whereas no one in the control group wrote this equation. Moreover, students in the experimental group also provided balanced chemical equations for the reaction of barium chloride with magnesium sulfate and ferrous sulfate respectively, while no one in the control group came up with such answers.

Table 6 Correct balanced chemical equations provided by students in item 2
Balanced chemical equations Experimental group Control group
Note:a Some students may have multiple answers, and the numbers in the table represent only the frequencies of answers.
1. BaCl2 + H2SO4 = BaSO4↓ + 2HCl 19a (70.4%) 12 (50.0%)
2. BaCl2 + Na2SO4 = BaSO4↓ + 2NaCl 8 (29.6%) 10 (45.8%)
3. Ba(OH)2 + Na2SO4 = BaSO4↓ + 2NaOH 7 (25.9%) 6 (25.0%)
4. Ba(NO3)2 + H2SO4 = BaSO4↓ + 2HNO3 6 (22.2%) 4 (16.7%)
5. BaCl2 + K2SO4 = BaSO4 ↓ + 2KCl 4 (14.8%) 1 (4.2%)
6. BaCl2 + CaSO4 = BaSO4↓ + CaCl2 3 (11.1%) 1 (4.2%)
7. Ba(OH)2 + CaSO4 = BaSO4↓ + Ca(OH)2 1 (3.7%) 1 (4.2%)
8. Ba(NO3)2 + Na2SO4 = BaSO4↓ + 2NaNO3 1 (3.7%) 1 (4.2%)
9. BaCl2 + CuSO4 = BaSO4↓ + CuCl2 4 (14.8%) 0 (0%)
10. BaCl2 + MgSO4 = BaSO4↓ + MgCl2 1 (3.7%) 0 (0%)
11. BaCl2 + FeSO4 = BaSO4↓ + FeCl2 1 (3.7%) 0 (0%)

According to the information contained in this item, the balanced net ionic equation written by students must contain the following information: only Ba2+ and SO42− ions were involved in the reaction and all other ions were spectator ions. Although some equations written by the experimental group do not meet the requirements of the item (such as reactions of barium hydroxide and copper sulfate, barium chloride and silver sulfate, barium carbonate and sodium sulfate), Ba2+ and SO42− ions did actually participate in these reactions and other ions actually took part in the process of these reactions as well.

However, most answers given by the control group were totally wrong and completely did not conform to the net ionic equation above, for example: Ba(OH)2 = Ba2+ + 2OH−, indicating that he/she was confused about the concept of ionization equation and net ionic equations. Some students in the control group even provided balanced chemical equations such as reactions of silver nitrate and hydrochloric acid, sodium hydroxide and copper sulfate and so on. Nevertheless, ions in all those equations did not comply with the second item's requirements, in particular, no Ba2+ and SO42− ions presented in those reactions at all, which meant that these answers were completely wrong.

In addition, the experimental group came up with a total of 11 different categories of correct answers and each student, on average, offered 2 correct chemical equations, whereas, the eight different categories of the correct answers were given in the control group and they provided 1.5 equations per person. In other words, through the analysis of the net ionic equation, the students in the experimental group presented more balanced chemical equations, which conformed to the given net ionic equation. That is, the experimental group established those more extensive connections among symbolic, particulate and macro information, indicating that their abilities of understanding and writing net ionic equation was significantly better than that of the control group.

The second question of the third item mainly examined students’ symbolic representation, which meant students needed to write a NIR precisely according to the existing particulate information.

Specifically, students were required to determine the ions: OH− and Cu2+ ions that actually participated in the reaction while Na+ and SO42− ions are spectator ions, and then students need to use correct symbols (such as subscript, chemical formula and charges, etc.) to represent the ions that actually participated in net ionic equations. The results indicate that 25 or 14 students in the experimental or control groups, respectively, provided the correct net ionic equation (see Table 7). It can be found that one student in the control group wrote totally wrong net ionic equations such as: Cu2+ + SO42− = CuSO4, indicating that he/she constructed a wrong connection between the symbolic, micro and the macro levels, as the reactant, SO42− ions do not participate in this reaction and they belongs to spectator ions.

Table 7 Net ionic equations provided by students
Net ionic equations Experimental group Control group Alternative conceptions
Note:a The numbers in the table represent only the numbers of students.
1. Cu2+ + 2OH− = Cu(OH)2↓ 25a (92.6%) 14 (58.3%) None
2. Cu2+ + 2OH− = Cu(OH)↓ 1 (3.7%) 1 (4.2%) No subscripts
3. Cu2+ + OH− = Cu(OH)2↓ 1 (3.7%) 4 (16.7%) Unbalanced
4. Cu2+ + OH− = Cu(OH)2 0 (0%) 1 (4.2%) Unbalanced, no precipitation symbol (↓)
5. Cu2+ + OH− = Cu(OH) 0 (0%) 1 (4.2%) Unbalanced, no subscripts, no precipitation symbol (↓)
6. Cu2+ + SO42− = CuSO4 0 (0%) 1 (4.2%) SO42− participated in NIR
7. Cu2+ + 2OH+ = Cu(OH)2↓ 0 (0%) 2 (8.4%) Confusion with the use of charge of hydroxide ion.

Discussions

This study aims to explore the effects of MBL on students’ macro, micro, and symbolic representations of NIR. In the experimental group, the students not only observed the experimental phenomena in the process of NIR, but also observed the changing curves produced by MBL. While in the control group, the students only observed the experimental phenomena. In accordance with the results of the independent sample t-test and size of the two groups’ significant performance on the NIR test, it can be seen that there is no effect of MBL on the students’ macro representation, medium effects of MBL on students’ micro representation, and medium effects of MBL on students’ symbolic representation.

In this study, there is an assumption that the conductivity curves produced by MBL would help students to connect macro representations with micro representations. The ions are micro particles in solution and so are abstract for students to imagine how they change in the process of NIR. In other words, students cannot see any change of ions due to the abstract nature of the micro world. As Struck and Yerrick (2010) pointed out, MBL expands a person's visual experience of seeing the physical phenomenon change. The ions change, a micro area, is transformed to a conductivity change by MBL with graphs that belong to macro areas. By analyzing the information of the curve in the graph, students constructed the connection between the changes of conductivity and changes of ions, that is, students connected the macro representation with the micro representation. Specifically, in the first experiment, students found the decreasing pitch until it arrived at the lowest point (see Fig. 2a), which agreed with their predictions. They explained that the value of conductivity went down to nearly zero because there are no ions in the solution and a kind of insoluble salt is produced through the NIR. When they saw the increasing pitch beginning with the lowest point, students explained the conductivity increased because of new ions, which were produced by the excess sulfuric acid dropped into the solution. In the second experiment, students explained that not all the ions reacted as in the first experiment by observing the lowest point in the curve above 0 (see Fig. 2b). Moreover, the effects of the curve produced by MBL on students’ learning were proven by the follow-up interview. Students mentioned the curves and lowest points, and summarized that the decrease in conductivity on the left side of the inflection point is due to the reaction between the ions in the solution, while the right side of the inflection point indicates that the reactant (titrant) is in an excessive state. As many researchers have reported that students have difficulty analyzing the behaviors of the particles in solution only by observing the macro experimental phenomena (Ebenezer, 2001; Laugier and Dumon, 2004; Çalýk et al., 2005; Chiu, 2007; Daubenmire, 2014), the conductivity curve produced by MBL provide a bridge for students to analyze the changes of the ions by observing the macro experimental phenomenon.

Students in the experimental group also constructed more symbolic representations than those students had in the control group. As Wruck (1996) pointed out that the writing skill of net ionic equations is also an important part for students to learn NIR, the writing of net ionic equations is considered as the symbolic representation in this study. Although many students had learned the methods and skills of balancing equations in a previous chemistry class, providing correct coefficients and subscripts for the chemical molecule equations did not mean that students actually understood the particulate behaviors or macro information contained in the equations (Nyachwaya et al., 2014; Lin et al., 2016). The results demonstrated that more students in the experimental group wrote correct net ionic equations than the students in the control group. In our study, we did not tell students the methods and skills of balancing equations in the experimental group. However, in the control group, we provide students with the method of writing net ionic equations according to the textbook students used in their school. The method consists of four steps: (a) writing a balanced chemical equation, (b) demolishing matters that can completely ionize in solution, (c) removing the same ions on both sides of the equation, (d) checking whether the two sides are balanced or not. As students in the experimental group constructed more symbolic representations than that of students in the control group, we infer that MBL helped students construct the connection between symbolic representations and their micro representations.

An unexpected result in this study is that there is no effect of MBL on students’ macro representation. One possible reason is that we used videos in both groups, in which students watched the same experimental phenomena on the video in the two groups. As Russell et al. (2004) pointed out, although MBL is a handheld device that easily collects and displays data in real-time based on its probe ware, it is always uncontrollable in practical teaching. It is difficult to use MBL for class demonstrations within a limited class hour, and it requires teachers to control and adjust the rhythm of demonstration strictly (Russell et al., 2004; Davies et al., 2012; Tortosa, 2012). It is also proven that the curves produced by MBL need rigorous operator and solution conditions in this study. The first author tried several times to get the graphs shown in Fig. 2. That is one reason we used the video in the experimental group. To reduce the effects of real experiments on students’ representation in the control group, we also use a video in that group.

Conclusions and implications

In this study, the graphs produced by MBL were used to help students learn NIR by representing the changes of electrical conductivity of the solution. The study reveals that the MBL has a medium effect on students’ micro and symbolic representation. However, there is no effect of MBL on students’ macro representation.

MBL helps students to construct connections between macro and micro representation in learning NIR. The micro representation of ion change in NIR is difficult for students to represent due to its abstract nature. Using MBL, the abstract ion change can be visible through the graph of conductivity change. We conclude that students built more micro representations about ion change through observation and explanation of the graphs of conductivity change than micro representations built not using the MBL. Writing of net ionic equations is considered to be the symbolic representation in this study. Students constructed more symbolic representations of ionic equations using MBL than when told the rules of how to write an ionic equation. We also make a tentative conclusion that MBL promoted students built some connections among macro, micro, and symbolic representations because the macro representation and micro representation are connected by the graphs produced by MBL, but this conclusion still needs further evidence.

Prerecorded videos of MBL activities in this study had positive effects on students learning NIR. Presentations of videos in science education resolved the problems that real MBL activities had (Jennings et al., 2007; Struck and Yerrick, 2010), and also promote students’ discussion about the phenomenon (Watters and Diezmann, 2007). MBL has other kinds of probe wares such as pH. We only use one kind of MBL in this study in order to help students learn the NIR concept, which is a conductive sensor. Whether other chemistry topics are suitable for using MBL in teaching practice is not clear. In comparison to watching a video of MBL activities, whether practical MBL activities have other effects on students’ macro representation needs further study such as student interest. Moreover, the comparison between effects of real MBL and the effects of real experiments on the macro, micro, and symbolic representation need further research.

This study still has some limitations. Firstly, we only focused on the qualitative information interpretation in the graphs, no calculations of the values of the conductivity and the concentration of the ions in the solution. Secondly, we used the video to replace real MBL equipment. The pre-recorded experimental videos may not be effective in promoting students’ experimental capacities and skills compared to conventional practical work. Although we found that MBL had an effect on students’ macro, micro, and symbolic representations in learning NIR, the mechanism of how graphs promote students’ transformation between different representations are still not clear. Thirdly, we only chose 4 students who acted positively and actively in the classroom, the disadvantages and difficulties of MBL on other students’ conceptual learning were missed in this study.

Conflicts of interest

There are no conflicts to declare.

Appendix A

The test of NIR

Q1: As the following picture shows, container A contains sodium hydroxide solution and phenolphthalein indicator, HCl solution is titrated in until the reaction is complete. Please draw the particles that exist in the solution before, during, and after reaction using the following icons. Use simple words to explain your reasoning.
image file: c8rp00165k-u7.tif

Q2: A reaction can be represented with the following net ionic equation: Ba2+ + SO42− = BaSO4↓. What are the chemical reactions that you can think of from this net ionic equation? Please write the balanced chemical equations of these reactions.

Q3: As the following picture demonstrates, mix the solutions in container A and container B (water molecules in solution are not given), and try to describe the experimental phenomena after mixing the two solutions, and use the net ionic equation to represent the chemical reaction that may occur.

image file: c8rp00165k-u8.tif

Acknowledgements

The authors thank all research participants in this study for providing their time and responses. The authors also thank the instructors for their support.

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