Hands-on learning of chemistry concepts

Introduction

Chemistry is regarded as a challenging and complex subject (Nakhleh, 1992; Gabel, 1999). This is because chemistry is filled with abstract concepts and deals mostly with submicro (particulate) entities that are difficult to represent in the real world (Griffiths & Preston, 1992). Studies have argued that the imagined world of the submicro level is among the most challenging aspects of understanding chemistry (Johnstone, 2000; Bucat & Mocerino, 2009).

Students’ issues in understanding submicro representations are said to be a source of misunderstanding in chemistry (Tasker & Dalton, 2006; Kelly et al., 2010). Studies have suggested that using particulate (submicro) drawings (Sanger, 2000) or submicro diagrams (Davidowitz et al., 2010) encourages conceptual understanding at the submicro level as these submicro drawings or diagrams enable students’ visualization of abstract chemical concepts such as stoichiometric problems (Davidowitz et al., 2010).

This brings us to one of the most important topics in chemistry, stoichiometry (Chandrasegaran et al., 2009; Davidowitz et al., 2010). Notably, stoichiometry deals with quantitative relationships through the use of chemical formulae and equations and is commonly taught in chemistry courses at the tertiary level (Evans, Yaron & Leinhardt, 2008). Stoichiometry has often been rated to be among the most difficult chemistry topics to teach (Haim, Cortón, Kocmur & Galagovsky, 2003). The usual approach to the teaching of stoichiometry is through the use of algorithmic strategies with little to no emphasis at all on conceptual understanding at the submicro level.

Studies have indicated that many students do not achieve an adequate understanding of stoichiometry-related concepts despite formal education (Gauchon & Méheut, 2007; Dahsah & Coll, 2008; Chandrasegaran et al., 2009). A notable problem inherent to the learning of stoichiometry is the tendency of students to solve stoichiometry problems in an algorithmic manner without conceptual understanding (Salta & Tzougraki, 2010).

The teaching and learning of stoichiometry does not include the use of concrete submicro models at all during classroom instruction. Instead, only “static” submicro diagrams (in PowerPoint slides) are used in conventional classrooms in the teaching of stoichiometry.

The main problem with students’ lack of conceptual understanding of stoichiometry is directly linked to students’ lack of understanding of the topic itself at the submicro level (Davidowitz et al., 2010). This shortcoming in the teaching and learning of stoichiometry at the submicro level in the researcher’s own classroom is the basis for this study.

Methods

An action research is carried out to study a specific problem (Fraenkel & Wallen, 2007). For this study, an action research was carried out to shed light on the issue, i.e., addressing students’ lack of conceptual understanding of stoichiometry at the submicro level. To do so, two cycles of lessons on stoichiometry were carried out, i.e., cycle 1 with “static” submicro representations (PowerPoint slides) to teach stoichiometry and cycle 2 with “dynamic” submicro representations. The “dynamic” submicro representations included (1) “dynamic” submicro diagrams (molecular graphics manipulated with the computer - hands-on approach) and (2) submicro models (physical molecular models manipulated by hand - hands-on approach).

“Dynamic” submicro representations functioned as supplementary methods to teach stoichiometry in addition to conventional classroom approaches (with only “static” submicro representations). The use of “dynamic” submicro representations allowed students to learn stoichiometry in a hands-on manner by playing with molecular graphics via the computer or by manipulating submicro models with their hands. The use of “dynamic” submicro representations allowed students to learn in an active manner rather than a passive manner.

The participants in this study were a cohort of pre-university (grade 12) students who were the researcher’s own students comprising eight students. This was an example of convenience sampling. These students were the only students in the researcher’s general chemistry class. The students were first briefed about the study in a specially conducted session during class (1^st week of the trimester) and once they were aware that the study would complement the course itself, all these eight students collectively volunteered to be part of the study and fully consented to the manner in which the general chemistry course as well as the research would be carried out as explained by the researcher. Full verbal consent was obtained from all eight students with the assurance that their identities and privacies would be protected. Oral consent received in this research was reported to the University’s Research Ethics Committee (REC) and an approval number was then granted. All in all, the students were aware of the purpose of the study, their important role in the study as participants and that their learning activities would constitute part of the curriculum for the subject involved. In this action research, qualitative data were collected from (1) students’ answers to structured stoichiometric questions in a stoichiometry test (included submicro drawings drawn by the students themselves), (2) individual interview sessions and (3) students’ entries into their journals. Together, these three sources of qualitative data formed the overall data collection techniques in this action research (Table 1).

The stoichiometry test comprised of eight structured questions for pre-university level chemistry on stoichiometry with emphasis on understanding at the submicro level. These questions were either developed with modifications from two Chemistry textbooks or were loosely based on questions used by previous researchers. The stoichiometry test contained different formats of questioning whereby students were required to either interpret submicro diagrams or relate submicro diagrams to the symbolic level or even produce their own submicro drawings.

The interview sessions were solely conducted by the researcher himself who had seven years of working experience teaching science subjects and was pursuing a Masters in Science Education at the time of the study. It must be noted that the researcher was also the only lecturer teaching general chemistry to these eight students. The interview sessions were carried out each time after the application of the stoichiometry test to probe students’ answers in their stoichiometry test. The interview sessions which were audio recorded, were open-ended and semi-structured and this allowed the researcher to probe the students’ conceptual understanding of stoichiometry at the submicro level.

As another source of data, students were also asked to record their thoughts in their journals in cycle 2 during the additional lessons phase in which “dynamic” submicro representations were used. The students’ entries into their activity journals (cycle 2 only) were meant to gather additional data and to triangulate all the sources of data. While the journal entries were written by the students after lessons with the “dynamic” submicro representations, these entries were based on guided questions.

The research flow for this action research consisted of two cycles. In cycle 1, lessons on stoichiometry were conducted with “static” submicro representations while in cycle 2, lessons on stoichiometry were conducted with “dynamic” submicro representations. After lessons with “static” submicro representations in cycle 1, students attempted stoichiometry test 1.

Interview session 1 was then carried out. Analysis of the qualitative data and reflection by the researcher followed these steps at the end of cycle 1. Upon completion of cycle 1, cycle 2 began with chemistry lessons on stoichiometry with “dynamic” submicro representations.

As students had to use submicro models followed by molecular graphics as part of the use of “dynamic” submicro representations in cycle 2, they were asked to fill in their activity journals after each of the two approaches mentioned. Once this had been completed, students attempted stoichiometry test 2. Similar to cycle 1, interview session 2 was then carried out in cycle 2.

Analysis of the qualitative data and reflection by the researcher was carried out again in cycle 2 just as it was done so in cycle 1. The sequence of steps mentioned in both cycle 1 and cycle 2 correspond to the major steps of an action research, namely “plan” (step 1) followed by “act and observe” (step 2) and followed by “reflect” (step 3). This was evident in cycle 1 and cycle 2 of this action research.

Results

Students’ responses to the questions and their drawings of submicro representations in the tests were analysed based on a dichotomous scale (‘Correct understanding’ and a ‘Lack of understanding’) manually without the use of any software. These responses were used to determine whether a change in students’ understanding had occurred between cycle 1 and cycle 2. ‘Correct understanding’ means that the question had been answered correctly without mistakes while ‘Lack of understanding’ means that the question had been answered incorrectly with mistakes. An example is provided to illustrate this dichotomy.

For example, in question 2 of the test, students drew the correct number of molecules (as submicro representations) for all reactants and products represented by the balanced chemical equation in the question (involves self-generated submicro drawings). In this given example (Figure 1), the student correctly drew two molecules of C₂H₆ which react with seven molecules of O₂ in the box for ‘before reaction’ and drew four CO₂ molecules and six H₂O molecules in the ‘after reaction’ box. Such an answer was classified as ‘Correct understanding’. Answers which deviated from this were classified as ‘Lack of understanding’.

Figure 1. Example of ‘Correct understanding’.

For the eight questions in the stoichiometry tests, questions 2-8 were related to students’ conceptual understanding at the submicro level. Table 2a (questions 2-4) and Table 2b (questions 5-8) are summaries of the results of the stoichiometry tests.

As observed in Table 2a and Table 2b, there were noticeable improvements in some students’ understanding for questions 2-8 when a comparison was made between stoichiometry test 1 and test 2. Questions 2, 3 and 4 required students to self-generate submicro drawings and positive improvements were observed for many students. For questions 5 and 6, students interpreted submicro diagrams. As seen in Table 2b, some students still demonstrated a ‘Lack of understanding’ for these two questions in test 2.

For questions 7 and 8, students had to interpret submicro diagrams as well as self-generate submicro drawings. The comparison between stoichiometry test 1 and test 2 for these two questions indicated that only a handful of students still demonstrated a ‘Lack of understanding’ for a few questions in stoichiometry test 2. Overall, positive improvements can be observed in the participants’ answers in stoichiometry test 2 as compared to test 1.

To provide insights into the factors that may be able to explain the findings of this study, the qualitative data from the interview sessions and the students’ journal entries were analysed to provide some answers. To this end, a coding framework was used in analyzing the transcribed data from these two sources of data. From the analysis of the transcribed text from these sources of data, ‘codes’ were formed. In the next step of the analysis, the variety of ‘codes’ was grouped together to form ‘categories’ which included the grouping of related ‘codes’ in each bigger ‘category’. From ‘categories’ which can be collapsed, ‘themes’ were subsequently generated. This was done repeatedly over several rounds or cycles of grouping and categorising until the point of saturation was reached, in which a clear ‘theme’ can be discerned and defined.

As this study was carried out as an action research, building themes to provide answers will be more important than answering hypotheses (Checkland & Holwell, 1998) and thus, this theme-building approach played the central role in interpreting the data collected in this action research. Creswell (2008) wrote that themes are aggregated codes with similarities. Standout themes are important research elements that can provide answers in the analysis of the type of qualitative data collected in this study. As shown in Table 3 below, two prominent themes were evidently detected via the process described above.

As shown in Table 3, two prominent ‘themes’ were generated in this study, i.e., ‘understanding based on touch or play’ and ‘understanding based on imagination’. These ‘themes’ were generated from the transcribed interview data and the students’ journal entries. These sources of data provided contextual clues and generated plausible explanations for the improvements seen in students’ conceptual understanding of stoichiometry at the submicro level. Note: only a small portion of the actual data is shown in this article due to the word-count limitation.

Discussion

For the theme ‘understanding based on touch or play’, it is noteworthy that the use of submicro models can account for the changes in students’ conceptual understanding of stoichiometry. It was not just the visuals of the models which provided students with improved conceptual understanding at the submicro level; it was also the manipulation of the models which had fostered understanding through ‘touch’ (hands-on).

Consider this illuminating interview excerpt:

Based on the theme ‘understanding based on imagination’, it is fair to note that the manipulation of the submicro models left a discernible impact on the students’ conceptual understanding of stoichiometry.

Consider this revealing interview excerpt:

From the interview and journal transcripts obtained in this action research, the researcher was able to find contextual clues that shed light on the usefulness of “dynamic” submicro representations to help students understand stoichiometry. It suffices to say that the findings of this study are relevant to researchers studying chemistry education. This comes as researchers have long stressed that the visual mode of representation in chemistry education is under-explored (Cheng & Gilbert, 2009) while Chandrasegaran et al. (2009) had recommended that instructors try a variety of approaches including an emphasis on the changes which occur at the submicro level to enhance students’ conceptual understanding.

Conclusion

As this action research focused on only eight students and was constrained by the local context of the study (e.g., the pedagogical restrictions, etc), the findings of this study cannot be generalized and this is its primary limitation. Nonetheless, even with the small number of students, the results of this study provide evidence to the researcher’s assertion that “dynamic” submicro representations can foster conceptual understanding of stoichiometry at the submicro level. The main contribution of this action research is that it highlights the importance of focusing on the submicro level of understanding to achieve conceptual understanding in chemistry and provides an example of how this can be done via the use of a hands-on learning approach to teaching chemistry concepts such as stoichiometry.

Author contribution

The main author is the only author that had contributed to the writing of this article based on an action research.

Consent

The author had obtained full, unequivocal verbal consent from all the eight participants in this research who were students who had enrolled for the chemistry course. The researcher had taught the chemistry course in the trimester in which the action research was conducted.

Ethical Approval (EA) Number

Approval number: EA2082021 was granted by Research Ethics Committee (REC) of Technology Transfer Office (TTO).

Dataset

Dans Easy: Hands-On Learning of Chemistry Concepts. https://doi.org/10.17026/dans-2ce-u2j8 (Yew, 2021)

This project contains the following underlying data.

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).