The use of mobile technology in abductive inquiry-based teaching and learning of chemical bonding

Abstract

Continuous enhancement of mobile devices such as smartphones offers new opportunities for using these technologies in inquiry-based learning environments. Inquiry-based learning has followed deductive and inductive forms of inquiry, while the abductive form of inquiry that targets the development of higher-order thinking skills such as critical thinking is less prevalent. This study investigated the use of mobile technology in abductive-inquiry based teaching and learning of chemical bonding for grade 11 physical sciences learners in two South African schools. The study employed an explanatory sequential mixed-methods design that entailed first collecting quantitative data and then qualitative data to help explain or elaborate on the quantitative results. Two grade 11 physical sciences classes were randomly designated as the experimental and control groups in each of the two different schools. The experimental group in each school experienced activities in a laboratory using mobile technology-enhanced abductive scientific inquiry through the ‘Molecular Workbench’ web-based simulation using a mobile device, while the control group in each school experienced activities in abductive scientific inquiry in a science laboratory without using mobile learning technology. The principal findings indicated that learners within the control group displayed a significant increase in their performance to create a scientifically accurate hypothesis that is the essence of abductive inquiry, whereas for the experimental group there was no significant improvement in their hypothesis generation capacity. However, participants within the experimental group felt that their use of mobile devices created a sense of learner agency amongst themselves, developed their communication skills, made them feel responsible for their own learning, and also made learning scientific concepts more fun as opposed to what they are normally exposed to.

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Introduction

Inquiry-based learning (IBL) lays emphasis on questioning and critical thinking to aid the formation of meaning and knowledge for learners in a classroom setting (Lee, 2010). In IBL, learners are involved in hands-on activities that engage them in hypothesis generation, experimentation and evidence evaluation (de Jong, 2006). IBL supports learners’ understanding of domain-specific knowledge of scientific phenomena that they observe in the physical world (van Joolingen and Zacharia, 2009). Broadly speaking, IBL is regarded as essential to the development of future generations of scientists, as well as to the development of a scientific-literate population (Lederman et al., 2014; Osborne and Allchin, 2024).

Abductive inquiry is a specific approach to inquiry-based learning that has the potential to develop higher-order thinking skills such as critical thinking (Raholm, 2010). Whereas in other forms of inquiry deductive and inductive processes are followed when learners use a known hypothesis to validate data or generate a rule, in abductive inquiry, the hypotheses are initially unknown (Ahmed and Parsons, 2013; Brandt and Timmermans, 2021). In abductive inquiry, the learner is exposed to scientific facts, theories, or principles, which are used as rules to formulate hypotheses on the observed phenomena. The learner makes meaning of the phenomenon by applying critical thinking skills to arrive at the hypothesis of the specific phenomenon observed (Oh, 2011).

This research investigated the use of mobile learning technologies in abductive inquiry-based learning. Continuous enhancement of mobile devices such as smartphones offers new opportunities for using these technologies in a classroom. As a result, mobile technologies such as tablets, iPads, smartphones, and laptops have become increasingly popular in K-12 classrooms (Zhang, 2015). The affordances of mobile technology include portability, data gathering, communication, and contextual and active learning (Parsons et al., 2016). Mobile technologies therefore hold much potential for supporting inquiry-based learning. In abductive inquiry-based learning, research conducted by Ahmed and Parsons (2013) has demonstrated that a mobile application, ‘ThinknLearn’, enhances learner performance, critical thinking skills and a deeper understanding of science content.

The abstractness and complexity of chemical bonding leads to various misconceptions amongst learners. Some widely documented misconceptions relate to the differences between ionic bonds and covalent bonds (Barke et al., 2009), the geometry of molecules (Uyulgan et al., 2014) and intra- and intermolecular forces (Vladušić et al., 2016). Oh (2022) maintains that abductive reasoning is well-suited to the construction of conceptual models that explain phenomena. This suggests that such reasoning in inquiry could support learners in conceptualising abstract phenomena such as chemical bonding. Furthermore, research by Engelschalt et al. (2023) showed the potential role of abductive reasoning in modeling for inquiry for explaining biological phenomena as complex systems. Despite the effectiveness of inquiry-based learning in addressing learner misconceptions (Mohd Radzi et al., 2017), there is a lack of exploration of mobile applications in facilitating science inquiry learning, particularly in abductive inquiry investigations. This study investigates the use of the ‘Molecular Workbench’ web-based simulation in abductive inquiry-based learning of chemical bonding. The research is guided by the following research question:

How can the use of mobile technology in abductive inquiry-based teaching and learning enhance the learning of chemical bonding by grade 11 physical sciences learners?

Inquiry-based learning and abductive inquiry

Inquiry-based learning (IBL) lays emphasis on questioning and critical thinking to aid the formation of meaning and knowledge for learners in a classroom setting (Lee, 2010). In IBL, learners are involved in hands-on activities that engage them in hypothesis generation, experimentation and evidence evaluation (de Jong, 2006). IBL supports learners’ understanding of domain-specific knowledge of scientific phenomena that they observe in the physical world (van Joolingen and Zacharia, 2009). Broadly speaking, IBL is regarded as essential to the development of future generations of scientists, as well as to the development of a scientific-literate population (Lederman et al., 2014). There are multiple benefits in doing scientific inquiry, one of which is that it creates scientifically literate individuals who understand how scientific knowledge is brought about and accepted (Kapelari, 2015). Furthermore, having an informed understanding of scientific inquiry aids learners in developing their problem-solving, science process and critical thinking skills (Lederman et al., 2019) and may also lead to a more enhanced understanding of scientific content (Lee and Shea, 2016). By learners actively engaging in scientific enquiry, their interest in the subject matter is motivated and invigorated (Osborne, 2010).

Abductive inquiry is a specific approach to inquiry-based learning that has the potential to develop higher-order thinking skills such as critical thinking (Raholm, 2010). Whereas in other forms of inquiry where deductive and inductive processes are followed when learners use a known hypothesis to validate data or generate a rule, in abductive inquiry, the hypotheses are initially unknown (Ahmed and Parsons, 2013). In abductive inquiry, the learner is exposed to scientific facts, theories, or principles, which are used as rules to formulate hypotheses on the observed phenomena. The learner makes meaning of the phenomenon by applying critical thinking skills to arrive at the hypothesis of the specific phenomenon observed (Oh, 2011).

The abductive inquiry model (AIM) is a specific model that can be adopted in classrooms when participating in abductive inquiry activities (Oh, 2011). The AIM comprises four essential elements, which are illustrated in the flowchart in Fig. 1 below.

Fig. 1 Abductive inquiry model (adapted from Oh, 2011, p. 413).

During the exploration phase, learners observe data of the phenomenon experienced and attempt to find a scientifically relevant way to explain the observed data. This is then followed by the examination phase, which consists of learners examining relevant scientific rules, which may enable them to provide a relevant explanation for the phenomenon under investigation. In the selection phase, learners reassess all previously formulated hypotheses and then select the one which based on scientific theory seems most plausible. In the case that learners find any flaws in this specific phase they may then revert back to any of the previous phases, namely the exploration or examination phase, to eradicate and replace this flaw or problem with a more refined explanation of the observed phenomenon (Ahmad, 2012). Hence, the first three phases of the AIM model are not linearly defined but are distinctly iterative in nature (Oh, 2011), where the hypotheses are repeatedly modified and then explained through a process of continuous development. Subsequently this results in the explanation being the final phase of this AIM model. In this phase, the learner elaborates on reasons for the specific phenomenon observed making use of the scientific rule and hypothesis selected by the learner in the previous phases of the model.

According to Johnson (2000), abduction forms part of the first phase of scientific inquiry as it leads to a deeper insight into the phenomenon that has been observed and can be correlated to existing theory to suggest possible new comprehension of the observed phenomenon. Subsequently, this new abducted insight can be interpreted and verified using deductive scientific inquiry to determine whether the new understanding may be accepted scientifically. Inductive scientific inquiry can then be used to determine if the new understanding is consistent with the existing scientific knowledge being taught in class (Raholm, 2010).

Moreover, to further elaborate on the relationship amongst the three different inquiry-based reasoning approaches, what follows is an example shown in Table 1 of the effect polarity has on the boiling points of alcohol and water at room temperature, and a description of how these three different inquiry-based reasoning approaches will consider the inquiry in terms of rule, case and result.

Table 1 Comparison of abductive, inductive, and deductive approaches to the effect of polarity on the boiling points of alcohol and water at room temperature

Deduction:	1. Rule (condition or suggestion): alcohol is less polar than water.	2. Case (hypothesis): a less polar substance has lower boiling points than polar substances.	3. Result (observation): alcohol evaporates faster than water at room temperature.
Induction:	1. Case (hypothesis): a less polar substance has lower boiling points than polar substances.	2. Result (observation): alcohol evaporates faster than water at room temperature.	3. Rule (condition or suggestion): alcohol is less polar than water.
Abduction:	1. Rule (condition or suggestion): alcohol is less polar than water.	2. Result (observation): alcohol evaporates faster than water at room temperature.	3. Case (hypothesis): a less polar substance has a lower boiling point than polar substances.

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In addition to this, abductive inquiry when applied in the classroom has also been found to not only develop a learner's critical thinking capability through inference formation but to further develop scientific skills such as problem-solving (Ahmad, 2012; Ahmed and Parsons, 2013). Research on abductive inquiry such as that conducted by Ahmed and Parsons (2013) provides some insight into its ability to enhance learners’ understanding of abstract and complex scientific concepts through inference formation. At the time of conducting this research, limited (if any) research had been done on the effects of applying abductive inquiry into South African science classrooms. Furthermore, there is a lack of research worldwide on this topic. This invites further investigation into its possible role in enhancing learners’ understanding of a topic such as chemical bonding, which has proven to be a difficult concept to grasp for learners.

Chemical bonding

In South African schools, chemical bonding is a concept in the subject physical sciences that is taught to learners in grades 10 and 11. The abstractness and complexity of this concept make it difficult for high school learners to comprehend; therefore, it has been found that undergraduates still struggle with this content as it has not been mastered at a high school level (Meltafina et al., 2019). The concept of chemical bonding has furthermore been identified as a threshold concept by many researchers. A threshold concept according to Entwistle (2008) is a concept that is similar to a gate opening, which has the possibility of opening up new ways of thinking and transformative ways of understanding a concept. In addition, less understanding of threshold concepts often leads to the formation of misconceptions (Meltafina et al., 2019). Misconceptions are rife in chemical bonding, and they relate to the differences between ionic bonds and covalent bonds (Barke et al., 2009), the mole concept, ideal gas law, the periodic table (Park, 2015), electron configuration and properties of atoms, metals, non-metals and metalloids (Meltafina et al., 2019).

Molecular workbench

Molecular workbench is a software developed by the Concord Consortium, and offers free and open source visual and interactive simulations to explore concepts in physics, chemistry, biology, biotechnology, and nanotechnology. Empirical studies on the use of chemistry simulations on MW show that they support learners in conceptualizing chemistry concepts such as the gas laws and a better understanding of electronegativity. Furthermore, computer simulations such as MW, when combined with learning by means of inquiry, may further lead to enhancing a learners’ scientific thinking skills (Fermann et al., 2000). In addition to this, more recent work indicates that these simulations aid in increasing the critical thinking abilities of high school students when learning chemical equilibrium (Saputra et al., 2023). MW, due to its aforementioned affordances of aiding in the general understanding of Chemistry concepts, was selected as a simulation tool for the purpose of the current study. In addition, MW offers mobile-friendly interactive simulations.

In light of the above, this study intended to determine the efficacy of mobile technology in teaching learners chemical bonding in an abductive inquiry-based learning environment in a South African schooling context. Accordingly, the following main research question was posed to drive the inquiry:

How can the use of mobile technology in abductive inquiry-based teaching and learning enhance the learning of chemical bonding by grade 11 physical sciences learners?

In addressing this question, the following objectives were set:

1. To compare the conceptual understandings of chemical bonding of the experimental and control groups upon experiencing abductive inquiry-based activities.

2. To compare the hypothesis generation competency of the experimental and control groups upon experiencing abductive inquiry-based activities.

3. To determine the perceptions of the experimental group learners and teachers on mobile technology-enhanced abductive inquiry-based learning.

Method

This study employed a mixed method research design, which typically consists of the mixing of quantitative and qualitative data in an individual study (Creswell and Poth, 2018). More specifically, the type of mixed method design employed in this study is an explanatory sequential mixed methods design that entailed first collecting quantitative data and then qualitative data to help explain or elaborate on the quantitative results (Creswell and Plano Clark, 2011). In adopting a pragmatic worldview, the mixing of quantitative and qualitative methods allows for a more fruitful interpretation of findings, providing “the best opportunities for answering important research questions” (Johnson and Onwuegbuzie, 2004, p. 16). According to Creswell and Clark (2017), this type of approach aids in making the quantitative data clear, by providing a deeper explanation of the quantitative results, subsequently leading to a deeper understanding of the findings.

Participants

Two schools (school 1 and school 2) were purposefully chosen according to the availability of laboratory resources and learners having access to mobile technologies such as tablets and iPads to be able to run the online web-based Molecular Workbench application. The sample for this specific study consisted of two grade 11 Chemistry classes at each of two public schools. At each school, the classes were randomly assigned as the control and experimental groups. In this way, the class structure of both classes remained intact. The control group was comprised of 47 learners (22 boys and 25 girls), and the experimental group consisted of 48 learners (23 boys and 25 girls). The learners were in the 16–17 age bracket. The quintile system in South African public schools classifies schools into five groups, from the poorest (Quintile 1) to the least poor (Quintile 5). Due to South Africa's broad incongruence between rich and poor (Spaull and Kotze, 2015), this quintile system is aimed to resolve inequalities in access to education due to financial constraints (Ogbonnaya and Awuah, 2019). The system determines that schools serving impoverished communities should receive more funding. The two schools that formed the focus of this study were classified as quintile 5. They were situated in middle class communities, and were well resourced. Both schools had access to WiFi connectivity to eradicate the additional need for learners to use their own mobile data for the purpose of the study. The benefit of such schools is that their learners often have more than adequate additional educational learning resources at home and school (Penn, 2018).

Ethical considerations

This study met the ethics requirements for human subjects research, and was approved by the Faculty of Education Research Ethics Committee at the authors’ university. The confidentiality of participants was protected. Their participation in this study was voluntary. They could withdraw their consent to participate in the project at any time during the project. All data collected was anonymous and only the researchers have access to the data that is being securely stored for no longer than 2 years after publication of research reports, or papers. Thereafter, all collected data will be destroyed.

Classroom procedure

The experimental and control classes at each school conducted hands-on abductive inquiry activities. In total, they performed five experiments based on intermolecular forces, in groups consisting of 4–6 learners. These experiments required learners to investigate and explain chemical phenomena related to intermolecular forces. For example, in one of the activities learners investigated through a practical activity the boiling points of polar and non-polar molecules. Based on this investigation, the groups submitted a scientific report. While both groups experienced inquiry-based learning in this manner, the experimental group learners were afforded the opportunity to do a simulated abductive scientific inquiry on Molecular Workbench online web-based application using their mobile devices. Fig. 2 is a screenshot of the simulated activity being done by learners on Molecular Workbench. Due to this being an abductive inquiry-based learning activity, upon observing a phenomenon, the learners responded to questions in their workbooks on the phenomenon they had observed.

Fig. 2 Screenshot from the mobile simulation a learner used during the inquiry activity.

Fig. 3 and 4 below depict phases of data collection for the experimental and control groups, respectively.

Fig. 3 Flowchart of phases of data collection for the experimental group.

Fig. 4 Flowchart of phases of data collection for the control group.

Data collection and analysis

The chemical bonding diagnostic test (CBDT) that was developed and validated by Jang (2004) was administered as a pre-test and post-test to participants. The test comprised two-tier multiple-choice items that test understanding of chemical bonding. The test was structured into two sections. Section A of the test itself comprised 13 two-tier multiple-choice questions totaling two marks for each question. In total, Section A of the test was counted out of 26 marks. Here is an example of such an item:

A diamond has a high melting point and high boiling point. This information suggests that the bonds in diamond are:

I. Weak

II. Strong

Reason

A diamond is a covalent molecular solid.

B. A diamond is a macromolecule composed of covalently bonded atoms.

C. A diamond is a covalent network solid (continuous covalent lattice) composed of covalent bonded molecules.

D. A large amount of energy is required to break the intermolecular forces in the diamond.

Section B consisted of 3 open-ended hypothesis generating questions to explain their selected answer. They selected an appropriate interpretation of their answer from multiple options provided and they finally came up with their own hypothesis of the phenomenon observed based on the data they had collected from the experiments. Importantly, this explanation had to be based on existing scientific knowledge.

Here is an example of such an item:

	Boiling point as measured	Circle possible intermolecular force present	Referring to boiling points provide a reason (hypothesis) for your answer
Beaker A (water)	100 °C	(A) Hydrogen bond
		(B) Ionic bond
		(C) Dipole–dipole

These hypothesis generating questions were marked as follows; ‘0’ for a wrong (or no) hypothesis; ‘0.5’ for a correct hypothesis but a wrong explanation to phenomenon observed; ‘1’ for a correct hypothesis with its explanation correct and relating to existing scientific knowledge. This made Section B questions add up to a total of 3 marks, subsequently making the grand total of the question paper to be out of 29 marks (26 marks from Section A, and 3 marks from Section B). The internal reliability of results was established by computing Cronbach's alpha (Tavakol and Dennick, 2011).

Lesson observations were conducted and recorded. This observation data from the experimental classes describes the use of mobile technology in enhancing abductive inquiry-based learning. After the experimental group of learners had experienced the activities, semi-structured focus group interviews were conducted with them to establish their perceptions of using the mobile application when doing abductive scientific inquiry. Three focus groups of 5 learners each were randomly drawn from each class. The interviews were recorded and later transcribed.

Two paired sample t-tests were conducted to compare pre- and post-test means for the control and experimental groups. An independent sample t-test was used to investigate the learning performance differences between the two groups in conceptual understanding of chemical bonding and hypothesis generation. The interview data were coded and classified, a process that involves reading through transcripts in order to have a comprehensive overview (Saldana, 2009). The codes emerged inductively from the data. After coding all the data, the codes sharing the same meaning were grouped together into sub-themes, and eventually these sub-themes were grouped together into themes (Saldana, 2009). Similarly, the lesson observation data was analyzed inductively to generate themes on learner engagement and teacher support during the activities. A codebook was developed and then shared with another researcher who applied the codebook in independently coding the interview data. Cohen's kappa was calculated to assess the inter-rater reliability in coding between the research and the second coder (Miles and Huberman, 1994).

The qualitative data analysis followed the codes-to-theory model as streamlined by Saldana (2015) in Fig. 5 below.

Fig. 5 Codes-to-theory model for qualitative data analysis (Saldana, 2015, p. 14).

In addition to this, an excerpt from the researcher developed codebook of how the interviews were analysed inductively following the above-mentioned analysis strategy is indicated in Table 2 below:

Table 2 Excerpt from researcher developed codebook

Codes	Code description	Excerpts from the interview	Category	Theme
“Physics was a challenge”	Describing the challenges learners faced prior to intervention	“physics was like, a challenge to me because my teacher had to explain it to me and I had to find other classmates to explain it to me, so I could understand much better”	Physics was a challenge	Learner agency
Learner collaboration	Learners positive attitude towards learning through collaborating with their peers	“I find other classmates to explain it to me, so I could understand much better”
		“I could go ask for help from the teacher or the learner around me”	Learning through collaboration
“You grasp it quicker”	Mobile technologies display information in such a summative matter, that it allows one to easily grasp knowledge and know what is expected of them.	“it gives you exact information and you grasp it quicker because you are on your phone when you forget then you are like oh let me go back and check again so it's quicker and easier to access than normal.”	Enhances learners retention and understanding.
“It helped me understand”	Learners find the mobile assisted abductive inquiry lessons aided their understanding of how chemicals bond	“It, it did quite help me understand how they bonded. They created a clear and yah, a clear picture of how, it, it's done compared to theory.”

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This study met the ethics requirements for human subjects research, and was approved by the Faculty of Education Research Ethics Committee at the authors’ university.

Results

Research objective 1 that compared the conceptual understandings of chemical bonding of the experimental and control groups upon experiencing abductive inquiry-based activities is addressed below.

Effect of abductive inquiry on conceptual understanding of chemical bonding

In order to determine the conceptual understanding and hypothesis-generating competency of learners within the control and experimental groups, quantitative data were collected by means of an adapted version of the CBDT developed by Jang (2004). Below, the descriptive and inferential statistics of the data collected are presented (Table 3). The data collected from both schools have been combined to constitute the control and experimental groups.

Table 3 Descriptive and inferential statistics on conceptual understanding for control and experimental groups

Groups	N	Min	Max	Mean	sd	df	t	p
Control group pre-test	47	5	26	12.04	4.95614	46	2.333	0.588
Control group post-test	47	4	21	14.34	4.20288
Experimental group pre-test	48	6	24	13.67	4.37271	47	0.067	0.947
Experimental group post-test	48	8	23	13.63	3.82392

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As indicated in Table 3, the descriptive statistics indicate that the control group scored a pre-test mean score of 12.04 while the experimental group scored a pre-test mean score of 13.67, showing a mean score difference of 1.63 between the control and experimental group. This not being a major difference in pre-test mean score results, made the two groups comparable when investigating the effect of the intervention. The post-test results show that the control group increased in their conceptual understanding of chemical bonding, whilst the experimental group had a slight decrease in their conceptual understanding of chemical bonding. The increase for the control group learners had a pre-test-post-test mean difference of 2.3 while the experimental group showed a slight decrease of 0.04.

In order to determine if these changes in conceptual understanding were of any significance, two paired samples t-tests were conducted to compare pre- and post-test means for the experimental and control groups (see Table 3). The test showed that although there may have been an increase in performance for the control group (t(47) = 2.33, p > 0.05), this improvement proved not to be significant. Moreover, no significant improvement in performance for the experimental group was noticed (t(48) = 0.067, p > 0.05).

Research objective 2 that compared the hypothesis generation competency of the experimental and control groups upon experiencing abductive inquiry-based activities is addressed below.

Effect of abductive inquiry on the hypothesis generation capability of the experimental groups and control groups

Table 4 shows the descriptive and inferential statistics on the hypothesis generation capability of both groups.

Table 4 Descriptive and inferential statistics on hypothesis generation capability for control and experimental groups

Groups	N	Min	Max	Mean	sd	df	t	p
Control group pre-test	47	0	3	0.85	1.08305	46	4.939	0.03
Control group post-test	47	0	3	1.96	1.08262
Experimental group pre-test	48	0	3	1.43	1.19318	47	0.260	0.796
Experimental group post-test	48	0	3	1.46	1.10042

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It is evident from the above table that although there was an improvement in the hypothesis generation capability of the experimental group, such an improvement was not significant t(47) = 0.260, (p > 0.05). However, there was a significant improvement in the control group's performance for this capability, t(46) = 4.939, (p < 0.05). This is indicative of control group learners having attained an increase in their ability to generate an accurate hypothesis, from an observed phenomenon.

In order to determine to what extent this insignificant increase was accurately spread throughout the sample population, the effect size of each sample recorded and analysed had to be measured. Cohen's d is a commonly accepted measure of the effect size a significant or insignificant increase might have on the entire population (Cohen, 1988). Hence, determining the effect size of this insignificant decrease in learners’ marks was obtained by calculating Cohen's d using this formula:

d = 1.03

An effect size of d = 1.03, was noted, indicative of the increase in the control group's mean score results having a large degree of practical significance on the overall data, indicating an increase in learners correct hypothesis generating capability as significant for the control group.

Research objective 3 that determined the perceptions of the experimental group learners and teachers on mobile technology-enhanced abductive inquiry-based learning is addressed below.

Perceptions of the experimental group learners and teachers on mobile technology-enhanced abductive inquiry-based learning

Findings of the focus group interviews with experiment group learners and interviews with teachers are now presented. The interviews provide some insight into the lack of significant improvement in the performance of these learners. These interviews were coded, similar codes were grouped into categories and then overlapping categories were grouped into themes inductively. The emerging themes are elaborated upon with supporting interview data.

Theme 1 Teacher and learner uncertainty in using mobile devices.

It was evident that both teachers and learners had limited experience in using mobile devices for science learning purposes. This is regarded as a factor that may have impacted the results for the experimental group learners. Contributing to this uncertainty was a lack of digital learning skills amongst learners. This is evident in the following interview excerpt.

I have terrible IT literacy ‘cause I never really did computers, growing up or in school we didn’t really do computers, if we did more of a uhm computer class from Grade one just teaching us the basic IT languages and foreign languages they may be, but, my IT skills and phone skills of technology, overall, I’m not very good at it.

The learners felt that their lack of digital knowledge hampered their opportunity to fully learn from the abductive inquiry activities. This was supported by both teachers who believed that a factor that could have hampered the experimental learner group's abductive inquiry experience was because of their lack of exposure to mobile devices due to their socio-economic circumstances. He expressed this view as follows:

The disadvantage is not all the learners have mobile phones, so it might disadvantage them. In terms of they might not know how to use it properly, others might not even have those phones in class.

Because now we’ve got disadvantaged schools, really, we’re still using chalkboards, so now it would take some time for those to be integrated, so we would need learners to have iPad for example.

Allied to learner uncertainly is the concern that due to learners having different skill sets, some would feel intimidated. This was highlighted below by a teacher:

Fear could hinder you know when you are not used to these things and doing practical in front of others. It's not easy, so because now one person had to be hands-on, and the others were just helping there so the person could have easily got intimidated by other fellow learners.

At the same time, the teachers also expressed uncertainty of their own digital skills. This is underlined below:

My knowledge is very limited to be honest and I still want to learn how can we use the technology of ICT to convey about learning?

Theme 2 Learners being distracted by mobile devices.

Mr Smith believed that learners were distracted by the devices and the unlimited WiFi that was made available to them. As a result, they were not focused on the task. This is reflected in the following interview excerpt:

Now what could have hindered them, is when they see Wi-Fi, they think that no, this is free data. Can I just check something else?”

This concern was also echoed by learners. For example, one learner stated this as follows:

The negative impact is that the learners might use it for other purposes as, such as like, uh playing games, WhatsApping, Instagram, social media and things like that, instead of using them to learn stuff like this.

However, while learner and teacher uncertainty, and learner distraction due to mobile devices are recognized as factors that impacted the results, there was also recognition of the potential of mobile devices as learning tools. Learner agency was one of the positives that emerged. A learner referred to this agency as follows:

It allows a child or a learner to be able to do it on their own.

Allied to agency, learners also appear to have more confidence in their learning due to the affordance allowed by mobile devices in accessing information from the website that was used. This is shown below:

In my opinion, the amount of information, I was shown on the website…. would… prepare me for a test… I would actually be able to apply the knowledge, that little knowledge, I got from that website…Uhm apply all those things that I’ve learnt and all those things that I’ve got from the website, I could possibly pass the test.

Discussion

This study investigated how the use of mobile technology in abductive inquiry-based teaching and learning enhances the learning of chemical bonding by grade 11 physical sciences learners. In investigating this question, the experimental group of learners was engaged in activities in mobile technology-enhanced abductive scientific inquiry in a laboratory, and the control group experienced activities in abductive scientific inquiry, without using mobile learning technology.

Research objective 1 sought to compare the conceptual understandings of chemical bonding of the experimental and control groups upon experiencing abductive inquiry-based activities. The results showed that the control group did not yield a significant increase in their conceptual understanding of chemical bonding; furthermore, the experimental group had a small decrease in their conceptual understanding of chemical bonding. This finding suggests that abductive inquiry alone does not support the conceptual understanding of chemical bonding, and the inclusion of mobile technologies has proven to have little to no effect on enhancing the conceptualisation of chemical bonding concepts. Although the literature abounds with the affordances of mobile learning technology for science learning, this finding is not unusual. For example, other studies show that there was no such difference in students’ learning performance with and without using a mobile device. The use of mobile devices during learning has been associated with cognitive distractions, reducing students' ability to focus on complex scientific concepts (Rosen et al., 2013).

Furthermore, multitasking, often facilitated by mobile devices, can lead to reduced comprehension (Wood et al., 2012). While there is compelling evidence for the use of mobile devices in the classroom, research on the use of mobile technology in education by Frohberg et al. (2009) that categorized 102 mobile-learning projects showed that mobile devices have been used primarily as a sort of reinforcement tool to stimulate motivation and strengthen engagement, and secondarily as a content-delivery tool. Few projects have used mobile devices to assist with constructive thinking or reflection. There is therefore a need for more research such as the current study to investigate the impact of mobile devices on higher-order cognitive outcomes.

However, when engaging the control group in abductive inquiry-based activities, a significant increase in overall learner conceptualisation was noted for this group in both schools (t(47) = 2.33, p < 0.05). This finding on the effect of abductive inquiry on conceptual understanding aligns well with a study conducted by Oh (2022), which concludes that abductive reasoning is well-suited to the construction of models that explain phenomena in earth sciences.

Research objective 2 compared the hypothesis generation competency of the experimental and control groups upon experiencing abductive inquiry-based activities. The findings from the study established that abductive scientific inquiry allowed learners to develop a more scientifically accurate hypothesis and explain scientific phenomena observed in hands-on science experiments done in class. The increase in scores for the experimental group was very small and not significant, however a significant increase in the control group accurate hypothesis generation was noted, with the mean score of hypothesis generation for the post-test (m = 1.96, s = 1.08) much higher than the mean score for the pre-test (m = 0.85, s = 1.08). The results obtained from a two-tailed paired samples t-test indicated a significant increase in learners’ accurate hypothesis generation capability within the control group (t(47) = 4.94, p < 0.05).

The findings in addressing research objective 3 on the perceptions of the experimental group in using these devices for abductive inquiry are now discussed. Interviews conducted with learners within the experimental group revealed that their readiness to use mobile devices to support abductive inquiry came across as a major limitation in hampering them from fully experiencing the benefits that abductive scientific inquiry may bring. This explained the insignificant improvement in scores of the experimental group in comparison to the control group's increase in performance of chemical bonding and hypothesis generation competency. The distracting nature of technology on learners’ academic conceptualisation of knowledge is also supported by the literature (Dontre, 2021). While both teachers and learners were of the view that although the inclusion of mobile devices in such a lesson makes learning more fun, saves time and gives learners a sense of responsibility for their own learning, learners first needed to be exposed and become more accustomed to using such devices for inquiry-based learning. Learners’ exposure to technology for the purposes of science learning is therefore an issue that will need to be addressed if such technology can be better exploited for learning gains. This is supported by other studies that found that aside from teacher development, learners need to be trained to use technology within a learning environment (Carstens et al., 2021).

Implications for teaching and research

The finding that mobile devices did not significantly improve hypothesis generation competency for the experimental group suggests that teachers might consider balancing the use of technology with more hands-on methods until students are more accustomed to using these devices effectively in a scientific context. The study highlights the importance of learner readiness when integrating mobile devices into abductive inquiry. Teachers should ensure that students are comfortable and familiar with using mobile devices in the classroom before expecting them to be beneficial for scientific inquiry.

Further studies could focus on the longitudinal effects of mobile technology in abductive inquiry, examining how long-term exposure and consistent use of devices impact learners' scientific reasoning and engagement.

Limitations of the study

An incumbent aspect of the study was that learners needed to have ample time to complete the intervention to inquire and explore as they were learning. However, the two weeks that the researcher spent at each of the schools did not allow the learners enough time to do so. Often times, the period would be over and learners had to re-start the activity the following day which impeded their learning. It is suggested that if such an activity is done in the future, learners should first be trained on how to use mobile technologies and how to use laboratory apparatus (allowing learners to become more confident in using them). This should save time once learners start with mobile technology-assisted inquiry activities. Training on this should, however, not only be given to learners but to the teachers of these learners as they themselves in some cases might lack the needed ICT skills to scaffold learners.

Conclusion

This study investigated the use of mobile technology in abductive inquiry-based teaching and learning of chemical bonding by grade 11 physical sciences learners. Hence, the major finding of the study was that abductive scientific inquiry allowed learners to develop a more scientifically accurate hypothesis and explain scientific phenomena observed in hands-on science experiments done in class.

Moreover, the inclusion of mobile devices in abductive inquiry appeared to not show a similar result. Learner readiness to engage in mobile technology-assisted abductive inquiry activities appeared to play a major role in the results obtained by the learners’ experimental groups. While studies in other countries show a high level of readiness to use mobile devices among science learners (e.g.Rahmat et al., 2023), more research is needed on the readiness of learners, especially in the African context where socio-economic factors are prevalent, to use mobile devices in science learning. Integrating mobile technology in science teaching, especially in the context of inquiry-based learning, can offer numerous benefits. However, ensuring that teachers are ready to effectively integrate mobile technology into their teaching practices is essential. There is a need for more targeted professional development of teachers that will address their needs to optimally utilise these technologies.

Data availability

Data collected for this research are collected from human participants, and due to ethical confidentiality reasons are not available.

Conflicts of interest

There are no conflicts to declare.

Appendix

A Chemical bonding diagnostics test

The chemical bonding diagnostic test. This is an instrument developed to test your understanding of chemical bonding concepts. The results of this test will have no effect on your grade 11 physical science marks. Answer to the best of your knowledge. Thank you.

Directions: this test consists of two sections. Section A consists of 13 pairs of questions which examine your knowledge of chemical bonding. Each question has two parts: a multiple-choice answer followed by a multiple-choice reason. On the answer sheet provided, please circle one answer from both the answer and reason sections of each question.

Section B consists of a table that you must complete.

1. The most important particle in chemical bonding is:

I. Proton in the outer shell.

II. All electrons

III. Electron in the outer shell

Reason

A. The chemical bonding is due to the proton transfer

B. The chemical bonding is due to all electrons transfer

C. The chemical bonding is due to all electrons loss

D. The chemical bonding is due to the electron transfer in the outer shell

2. The state of electrons for an ionic bonding is:

I. Electron sharing

II. Electron transfer

III. Electron destroying

Reason

A. Electrons of atoms will be shared with the same number of electrons.

B. Electrons of atoms will be entirely transferred to other atoms

C. Electrons of atoms will be entirely destroyed

D. Electrons of atoms will be entirely divided into other atoms

3. The state of ionic compounds at room temperature is:

I. Gas

II. Liquid

III. Solid

Reason

A. Ionic compounds will have a strong lattice force

B. Ionic compounds will have a strong ionic bonding force

C. Ionic compounds will have a large size ion

D. Ionic compounds will have a large ionic charge

4. Sodium chloride, NaCl, exists as a molecule.

I. True

II. False

Reason

A. The sodium atom shares a pair of electrons with the chlorine atom to form a simple molecule.

B. After donating its valence electron to the chlorine atom, the sodium ion forms a molecule with the chloride ion.

C. Sodium chloride exists as a lattice consisting of sodium ions and chloride ions.

D. Sodium chloride exists as a lattice consisting of covalently bonded sodium and chlorine atoms.

5. A diamond has a high melting point and high boiling point. This information suggests that the bonds in diamond are:

I. Weak

II. Strong

Reason

A. A diamond is a covalent molecular solid.

B. A diamond is a macromolecule composed of covalently bonded atoms.

C. A diamond is a covalent network solid (continuous covalent lattice) composed of covalent bonded molecules.

D. A large amount of energy is required to break the intermolecular forces in the diamond.

6. Element C (electronic configuration 1S² 2S² 2p⁶ 3S² 3p⁶ 4S²) and element E (electronic configuration 1S² 2S² 2p⁵) react to form an ionic compound, CE

I. True

II. False

Reason

A. An atom of C will share one pair of electrons with each atom of E to form a covalent molecule, CE₂.

B. A macromolecule consists of covalently bonded atoms of C and E.

C. Atoms of C will each lose two electrons and twice as many atoms of E will each gain one electron to form an ionic compound CE₂.

D. An atom of C will lose one electron to an atom of E to form an ionic compound CE

7. An atom of element A has two electrons in its outermost shell while an atom of element B has five electrons in its outermost shell. When A reacts with B, the compound will be:

I. Covalent

II. Ionic

8. Water (H₂O) and hydrogen sulfide (H₂S) have similar chemical formula and structures. At room temperature, water is a liquid and hydrogen sulfide is a gas. This difference in state is due to:

I. Intermolecular forces between molecules

II. Intramolecular forces within molecules

Reason

A. The difference in the forces attracting water molecules and those attracting hydrogen sulfide molecules is due to the difference in the strength of the O–H and the S–H covalent bonds.

B. The bonds in hydrogen sulfide are easily broken whereas those in water are not.

C. The hydrogen sulfide molecules are closer to each other, leading to greater attraction between molecules.

D. The forces between water molecules are stronger than those between hydrogen sulfide molecules.

9. Which of the following best represents the position of the shared electron pair in the HF molecule

(I) H:F

(II) H:F

Reason

A. Non-bonding electrons influence the position of the bonding or shared electron pair.

B. As hydrogen and fluorine form a covalent bond, the electron pair must be centrally located.

C. Fluorine has a stronger attraction for the shared electron pair.

D. Fluorine is the larger of the two atoms and hence exerts greater control over the shared electron pair.

10. The density of water is maximum at the temperature of 4 °C.

I. True

II. False

Reason

A. The mass of water is a maximum at the temperature of 4 °C.

B. The volume of water is maximum at the temperature of 4 °C.

C. The portion of hydrogen bonding of water is maximum at the temperature of 4 °C.

D. Mass and volume of water are maximum at the temperature of 4 °C.

11. The kind of bonding to make the water molecule (H₂O) is:

I. Ionic bonding

II. Polar covalent bonding

III. Non-polar covalent bonding

Reason

A. Hydrogen loses an electron to be the hydrogen ion H⁺.

B. Oxygen gains two electrons to be the oxygen ion O²⁻.

C. Hydrogen and oxygen share electrons equally.

D. The electronegativity of oxygen is larger than that of hydrogen.

12. Iodine crystals I₂(s) are soluble in ethanol CH₃CH₂OH(l)

(I) True

(II) False

A. Hydrogen bonds between ethanol molecules are much stronger than the dispersion forces between iodine molecules.

B. Hydrogen bonds between ethanol molecules and the covalent bonds between iodine molecules are of comparable strength.

C. Iodine molecules and the ethanol molecules are non-polar and “like dissolves like”.

D. London/dispersion forces between ethanol molecules and iodine molecules are of comparable strength and “like dissolves like”.

13. The strongest metallic bonding exists in:

I. Mercury

II. Sodium

III. Copper

Reason

A. The state of metal is a liquid.

B. It is easy to be a (+) ion after losing electrons.

C. There are many free electrons moving between metal ions.

D. There exists a big charge in metal ions.

Section B: Complete the following table

	Boiling point as measured	Circle possible intermolecular force present	Referring to boiling points provide a reason (hypothesis) for your answer
Beaker A (water)	100 °C	(D) Hydrogen bond
		(E) Ionic bond
		(F) Dipole–dipole
Beaker B (ethanol)	64.7 °C	(A) Hydrogen bond
		(B) Ionic bond
		(C) Dipole–dipole
Beaker C (NaCl)	1.413 °C	(A) Hydrogen bond
		(B) Ionic bond
		(C) Dipole–dipole

The chemical bonding diagnostic test answer sheet. * Grade: 11 (Group A. Group B)

Question	Answer	Reason
Q1	I	II	III	A	B	C	D
Q2	I	II	III	A	B	C	D
Q3	I	II	III	A	B	C	D
Q4	I	II		A	B	C	D
Q5	I	II		A	B	C	D
Q6	I	II		A	B	C	D
Q7	I	II		A	B	C	D
Q8	I	II		A	B	C	D
Q9	I	II		A	B	C	D
Q10	I	II		A	B	C	D
Q11	I	II	III	A	B	C	D
Q12	I	II		A	B	C	D
Q13	I	II	III	A	B	C	D

Section B: Complete table

	Boiling point as measured	Circle possible intermolecular force present	Referring to boiling points provide a reason (hypothesis) for your answer
Beaker A (water)	100 °C	(G) Hydrogen bond
		(H) Ionic bond
		(I) Dipole–dipole
Beaker B (ethanol)	64.7 °C	(D) Hydrogen bond
		(E) Ionic bond
		(F) Dipole–dipole
Beaker C (NaCl)	1.413 °C	(D) Hydrogen bond
		(E) Ionic bond
		(F) Dipole–dipole

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