Effect of Computer Simulation Integrated with Jigsaw Learning Strategy on Students’ Attitudes towards Learning Chemistry

Shimelis Kebede Kekeba; Abera Gure; Taklu Tafesse Olkaba

doi:10.1515/edu-2024-0052

Article Open Access

Effect of Computer Simulation Integrated with Jigsaw Learning Strategy on Students’ Attitudes towards Learning Chemistry

Shimelis Kebede Kekeba , Abera Gure and Taklu Tafesse Olkaba

Published/Copyright: December 6, 2024

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From the journal Open Education Studies Volume 6 Issue 1

Abstract

The objective of the study was to investigate the effect of computer simulation integrated with jigsaw learning strategy (CSIJLS) on students’ attitudes towards chemistry. Additionally, it sought to determine whether the usage of CSIJLS resulted in any changes in attitudes between male and female students. Researchers employed a quantitative research method and a quasi-experimental design. The participants consisted of three classes, with a total of 144 students aged above 18, assigned to two experimental groups and one comparison group. The researchers collected data using a chemistry attitude Likert scale test administered before and after the intervention. One-way ANOVA and independent t-tests were employed to analyse the data. The results of the one-way ANOVA indicated that there was a significant difference in attitude between the three groups after the implementation of CSIJLS among the three groups (t(141) = 93.9, p = 0.000 < 0.05), indicating that students taught through CSIJLS performed significantly better than those taught using the jigsaw learning strategy alone and conventional methods in terms of their chemistry attitude post-test results. Additionally, the independent t-test analyses revealed no significant differences in attitudes towards chemistry between male and female students when they learned through CSIJLS.

Keywords: attitude; male and female; jigsaw learning strategy; computer simulation

1 Introduction

Conventional instructional approaches have been repeatedly shown to contribute to students’ poor understanding of fundamental chemistry concepts. These conventional methods (CM) often lead to passive learning, where students merely absorb information without actively engaging with the material. Even when students appear to understand the topic, their comprehension is limited to rote repetition and superficial understanding, rather than a deep and meaningful grasp of the concepts (Kandemir, 2017; Ma, 2023; Yalçinkaya & Çetin, 2018). A common misconception among students is that science is boring and only involves memorization of facts, which can be attributed to the prevalence of teacher-centred teaching methodologies that negatively impact students’ attitudes, cognitive growth, and academic achievement in science (John, Samuel, & Zephaniah, 2017).

To address these issues, science and chemistry teachers need to consider using different, more student-centred teaching strategies, particularly when covering difficult and abstract scientific concepts (Li, Ouyang, & Xu, 2022). Several studies suggest that applying student-centred techniques, especially those that make use of contemporary information and communication technologies, can be an effective solution (Nxumalo-Dlamini & Estelle, 2022). These tools can direct student activities, help them acquire knowledge, and free up teachers’ time to work with small groups and address individual needs. Technology-based learning has been shown to improve students’ attitudes, communication, and decision-making abilities (Pumptow & Brahm, 2023; Sarwar, 2016). In student-centred classrooms, students can work together, exercise critical thinking, and come up with different ways to solve problems by using computer simulations (Başöz & Çubukçu, 2014).

Chemistry is often considered one of the most difficult subjects, particularly for students at all academic levels, with two of the most challenging topics being acids and bases (Chowdhury, Rankhumise, Simelane-Mnisi, & Mafa-Theledi, 2020; Demircioğlu, 2017; Salleh, Rauf, Saat, & Ismail, 2023). Numerous studies have investigated students’ understanding and misconceptions regarding these concepts, highlighting the difficulties they face when taught using CM (Derman & Kayacan, 2017)). To address this challenge, research suggests that integrating computer-simulated instructions and a jigsaw learning approach can significantly improve high school students’ performance and understanding of chemistry (Grazioli, Ingwerson, Santiago, Regan, & Cho, 2023; Jeong, Hmelo-Silver, & Jo, 2019). Furthermore, the use of various technologies, such as pH meters, chemical indicators, and microcomputer-based laboratories, can also influence secondary school students’ comprehension of acid, base, and pH concepts (Demelash, Andargie, & Belachew, 2024; Levy, Kim, & Wilensky, 2021). Overall, technology-based instruction approaches are more effective than CM, with a favourable effect on students’ attitudes and motivation (Chen, Wang, Kirschner, & Tsai, 2018; Yalçinkaya & Çetin, 2018).

Jigsaw learning strategy is a cooperative learning method that encourages student engagement, shared content acquisition, and mutual explanation (Slavin, 1996; Sudin, Hermawan, Rosfiani, Ristiawati, & Hasanah, 2021). It is an instructional technique that was originally developed by Aronson et al. (1978). According to a recent report by Vives, Poletti, Robert, Butera, and Huguet (2024), the jigsaw classroom involves the following steps: First, the entire classroom is divided into small groups, typically 4–6 members each, which are called “home groups” or “jigsaw groups.” Next, the academic material to be learned, such as a scientific paper, is divided by the teacher into meaningful segments. Each segment is then assigned to one student within each home group, who must individually study and become familiar with their assigned segment. Students from different home groups who were assigned the same academic segment are then brought together in “Expert groups,” where they work to master the content of their segment and discuss how to effectively teach it to their home group members. Subsequently, each student returns to their original home group and teaches the other members about the segment they specialized in, allowing the full academic material to be covered within each home group. Finally, each student is individually assessed on their knowledge and understanding of all the segments through a quiz (Figure 1).

Figure 1

Jigsaw learning strategy implementation format.

This method has proven to be effective in chemistry and has demonstrated positive impacts on student achievement, attitude, and engagement (Vives et al., 2024). However, the jigsaw learning strategy (JLS) poses challenges to its effective implementation (Vives et al., 2024). Firstly, there is a cultural challenge as students need to adapt to a new collaborative and cooperative learning environment, in addition to mastering the course content. Secondly, the jigsaw method presents a cognitive hurdle for students as they navigate this unfamiliar learning approach. Implementing the jigsaw method may also require significant time for students to become comfortable with the new way of working and the associated values and skills. Additionally, concerns have been raised about the potentially limited learning experience for students in the novice role during the jigsaw process. Furthermore, different types of sociocognitive conflicts may arise during the various stages of the jigsaw procedure, which need to be addressed. Lastly, fostering a positive error climate in the classroom is crucial to support the learning process within the jigsaw method. However, these challenges have been minimized by integrating the jigsaw method with computer simulations.

Computer simulation is an imitation of a dynamic system that combines dynamic visualization, physical laws and attributes, mathematical formulas, and problem-solving strategies using a model (Vlachopoulos & Makri, 2017). Computer simulations have the flexibility benefit of encouraging students’ active participation in higher-order thinking, problem-solving, and practised skills. As a result, computer simulations have the potential to enhance learning by making abstract concepts more tangible and engaging (Brigas, 2019). Computer simulations give students the chance to recreate elements of the actual world that would be difficult, risky, or time-consuming to accomplish in a conventional classroom setting. Time changes can be accelerated or slowed down, abstract ideas can be given a tangible form, and hidden processes can become visible in a simulation environment (Kalimullina, Tarman, & Stepanova, 2021). It gives students immediate computer feedback. Through computer simulation, students can learn complex concepts, that are often elusive or even impossible to observe in the physical world (Liu, 2019).

Computer simulation integrated with a JLS is an instructional approach that combines two powerful educational techniques to enhance learning outcomes (Schmid, Borokhovski, Bernard, Pickup, & Abrami, 2023). When computer simulation is integrated with a JLS, jigsaw learning takes on a new dimension of engagement and interactive learning (Simões, Oliveira, & Nunes, 2022). Students can use computer simulations as a tool to explore complex concepts, conduct virtual experiments, and gain hands-on experience (John et al., 2017). Each group can be assigned a different simulation scenario, and after mastering the content, they can teach their peers about their findings and insights (Adekunle, Victor, & Nwabuno, 2021). This combination of jigsaw learning and computer simulations promotes collaborative problem-solving, critical thinking, and a deeper understanding of the subject matter, while also leveraging the benefits of technology to enhance the learning experience (Elagha & Pellegrino, 2024).

Multiple investigations have revealed that integrating the JLS with computer simulations can assist students in attaining improved learning outcomes (Shen & Ho, 2020). However, most teachers continue to face difficulties in incorporating technology into their teaching approaches (Chou, 2022). There is a growing acceptance of dynamic problem-based and cooperative learning in pedagogy, which requires modifications in instructional methods to foster the advancement of 21st-century skills. The emergence of new learning technologies has made it feasible to utilize computer simulations in facilitating student learning (Johnson et al., 2015).

A study carried out in Chile sought to understand secondary school students’ attitudes toward chemistry (Manuel et al., 2022). The results of the study showed that Chilean students’ attitudes towards chemistry were neutral when students learned through computer-based instruction and conventional instruction as well as the authors found a strong link between these sentiments and the subject’s academic success. Furthermore, Edwige and Philothère (2021) study looked at secondary school students’ perceptions of chemistry’s difficulty, interest, utility, and relevance. According to their findings, male and female students had comparable opinions of chemistry (Udu, 2018a). Seba, Rowley, and Lambert (2012) found that male students in Tanzania demonstrated superior achievement in chemistry and higher levels of satisfaction than their female counterparts. Consequently, it can be inferred that boys exhibit a higher level of satisfaction with chemistry and frequently participate in more chemistry-related activities than girls (Barnea & Dori, 1999).

Research conducted on chemistry education in Ethiopian secondary schools has revealed that conventional teaching methods, such as lectures and teacher demonstrations, continue to dominate classroom activities (Njoku & Nwagbo, 2020). However, these methods have proven to be ineffective in promoting chemistry learning at the secondary school level. Consequently, students’ attitudes towards chemistry have remained poor. To address this issue, the integration of computer simulations with jigsaw learning approaches is necessary. This innovative teaching technique can help overcome the differences in learning outcomes between male and female chemistry students (Hagos & Andargie, 2022). By utilizing computer simulations and jigsaw learning, both male and female students in secondary school can improve their attitudes towards learning chemistry.

The previous 3 years’ releases of Ethiopia’s secondary school examination results by the National Educational Assessment and Testing Agency indicate a declining trend (NEAEA, 2020). The data reveals that the percentage of grade 10 students who passed chemistry with a grade over 50% was 48.1% in 2018, 46.7% in 2019, and 43.7% in 2020. Similarly, the mean score for Grade 12 students in chemistry was 42.7% in 2018, which decreased to 40.1% in 2019 and further dropped to 37.1% in 2020. These figures fall short of the minimum requirement of 50%, indicating a declining trend in achievement over time. This indicates that chemistry instruction in grades 10 and 12 is difficult, which calls for additional research. Surprisingly, there is a lack of research exploring the influence of students’ attitudes towards chemistry teaching and learning on their academic achievement, as well as the underlying causes of this decline (Fassil, Adem, Getahun, & Sileshi, 2018). Students must engage in activities that hold personal significance and importance to them to foster attitudes (Hye, Kyungun, Karynne, & Min, 2018). Consequently, Ethiopia faces challenges in attracting and motivating students to study chemistry.

There is a lack of research on the integration of the jigsaw learning technique with computer simulations in acid and base courses, even though earlier experiments conducted in Ethiopia have demonstrated success in some chemistry topics. Ethiopia uses very little computer-assisted instruction while teaching secondary school chemistry. Furthermore, there is an absence of studies contrasting computer-assisted learning with conventional instruction approaches in Ethiopian secondary school chemistry classrooms (Yesgat, 2022). Likewise, the impact of integrating computer simulations with the jigsaw learning technique on students’ attitudes towards chemistry is not well supported by empirical data (Tefera, Atnafu, & Michael, 2021). It’s uncertain if computer simulation integrated with jigsaw learning strategy (CSIJLS) has a positive or negative impact on Ethiopian secondary school students’ attitudes towards chemistry. The study investigated how computer simulations integrated with a JLS affected students’ attitudes towards chemistry in general, and towards the topic of acids and bases in particular, at Jimma Secondary School.

2 Specific Objectives of the Study

The specific objectives of the study are to:

examine the effect of CSIJLS on secondary school students’ attitudes towards learning acid and base.
assess the impact of CSIJLS on the attitudes of male and female secondary school students about the study of acid and base.

2.1 Research Hypotheses

The researcher developed two distinct null hypotheses to address the aforementioned objectives:

H01: There is no statistically significant difference in the perspectives of secondary school students who learned acid and base concepts through CSIJLS compared to those who learned through JLS alone and CM of instruction and tested using a one-way ANOVA.
H02: There is no statistically significant difference in the attitudes of male and female secondary school students when teaching acid and base concepts utilizing CSIJLS and tested using an independent samples t-test.

3 Research Methods and Materials

3.1 Research Method and Design

This study used a quantitative research method to assess the effect of CSIJLS on students’ attitudes towards learning chemistry. The research design used in this study was quasi-experimental. The design included a comparison group (CG) and two treatment groups to meet the pre-test and post-test standards for every treatment group. Students in experimental group two (EG2) were taught exclusively using the JLS alone, while students in experimental group one (EG1) got instruction using CSIJLS. Conversely, students in the CG received instruction through CM. Chemistry Attitude Likert Scale Test (CALST) was the data-gathering tool used in this investigation. Pre-test and post-test CG tests were used to collect quantitative data. Each group completed a pre-test before the intervention to assess the dependent variables’ starting state. After eight weeks of exposure to the independent factors, any changes were evaluated with a post-test. The post-test allowed the researchers to determine how the treatment affected the students’ attitudes, while the pre-test was used to guarantee comparability between the groups before to treatment.

3.2 Sampling Techniques

The research focused on students in the 10^th grade due to the fact that the chemistry textbook utilized at that grade level served as the basis for the chosen topic of the intervention. One secondary school from Jimma town with superior facilities – a computer laboratory, in particular, and a highly competent and experienced chemistry teacher was employed. Three classes from the chosen school were selected using a simple random sampling method to create the experimental and CGs. One of these classes was placed in the CG, while the other two were placed in the experimental groups (EGs). To guarantee that the class was divided into appropriate groups, the stratified random selection technique was used. The study had 144 grade 10 students in total, 60 of whom were male and 84 of whom were female having average ages above 18. The CG and the EG were chosen from distinct shifts to avoid any information contamination.

3.3 Procedures for the Interventions

Training was provided to teachers and students in the targeted groups within the allotted time frame. At the beginning, the researcher gave a summary of the objectives of the study, the methods for carrying out the steps, the tasks to be finished during the therapy, and the treatment strategy. Teachers and students participated in a 10-day programme designed by the researchers, with teachers spending 3 h each day and students attending two.

The teacher-focused training aimed to provide teachers with the knowledge and skills needed to effectively implement the jigsaw classroom method. This included understanding the principles of the jigsaw approach, learning strategies to facilitate small group discussions and collaborative learning, and offering guidance to students throughout the process. Additionally, teachers were trained on their specific roles in the jigsaw classroom, such as delivering clear instructions, monitoring group work, and assessing both individual and group contributions.

The student-focused training for the jigsaw classroom likely emphasized the development of social and collaborative skills essential for success in that learning environment. This has included instruction on effective communication within small groups, actively listening to and building upon peers’ ideas, taking responsibility for one’s assigned “expert” topic, and contributing meaningfully to the group’s collective learning. Additionally, the training has covered strategies for time management, task organization, and conflict resolution within the jigsaw teams.

Following the training, students in the CG and EG took a chemistry attitude Likert scale pre-test covering grade ten concepts, notably acid and base. Based on their significance for understanding other chemistry topics and the challenges that students typically encounter in comprehending them, these topics were chosen (Olakanmi, 2018). The pre-test was designed to collect data regarding the students’ attitudes before the intervention.

After that, the lessons were taught to the students in EG1 using CSIJLS, while the lessons were presented to the students in EG2 using JLS alone. Students shared their materials with other group members and participated in discussions when using the JLS. Computer simulations were used to teach the subjects, and students engaged in lively debates with one another.

The lessons were typically taught through cooperative group work, which promoted student discussion. The teacher separated the class into smaller groups based on a variety of criteria, including emotional traits, academic standing, gender, and more. Seven groups, each with four to six students, were consequently created. Students were encouraged to actively participate in the class by being given time to contemplate before answering questions that tested their higher-order thinking abilities. The teacher used a variety of strategies to help the students in the classroom complete the jigsaw learning process, including talks, assistance, monitoring, observation, quizzes, and oral questions (Anuar, Bachok, & Pop, 2021).

In the current study, a range of technological tools were used, including laptops, desktop computers, whiteboards, microphones, computer simulations and smartphones. Applications like Telegram, PowerPoint, Internet access, and LCD were also used to facilitate jigsaw learning techniques both within and outside of the classroom. PowerPoint was utilized by the teacher to establish the objectives of the course and to support both individual and group work. The objectives of the class were shown by using an LCD to show tasks on a desktop computer. Enough time was set out for both individual and group conversations concerning the current activities. The CG, on the other hand, received learning using CM, particularly the chalk-talk method or lecture method, which is the most common form of instruction in Ethiopia (Tafere, Afework, & Yalew, 2018).

For eight weeks, the experimental and CGs received instruction three times a week for 40 min each. After the intervention, a post-test using the chemistry attitude Likert scale test was given to each group. This test served as an equivalent of the pre-test. For every group, a separate assessment was made of how the dependent variables had changed between the pre-test and post-test. Under the researcher’s guidance and support, the chemistry teacher imparted the topics.

4 Procedures for Each Strategy

4.1 Jigsaw Learning Strategy

The jigsaw method is a cooperative learning technique that divides students into small “home groups” of around four to six members. These home groups are designed to be diverse, with a mix of genders and academic levels represented within each group.

In the first step, each student in the home group is assigned a specific responsibility or topic to focus on. All students begin by gaining a fundamental understanding of the core concepts, in this case, the concepts of acids and bases. Next, the teacher divides the lesson content into five or six distinct parts, with each part assigned to a different member of the home group. Students then become “experts” on their assigned material by thoroughly researching and studying the available resources. The students then transition from their home groups to “expert groups.” In the expert groups, individuals who were assigned the same content from different home groups convene to discuss, share their materials, and plan how to effectively teach this information to their original home group members. Finally, the students return to their home groups and take turns teaching or presenting what they learned in the expert group. If any difficulties, misconceptions, or doubts arise during the teaching process, the teacher provides clarification by summarizing the key points. Following the group work activity, the students completed an individual quiz to assess their understanding of the concepts.

4.2 Jigsaw Learning Strategy Integrated with Computer Simulations

The jigsaw learning method incorporates computer simulations to engage students in collaborative learning. Students are divided into small, diverse groups of four to six members.

Initially, all students study and comprehend the concepts of acids and bases. The teacher then divides the lesson material into four or six segments and assigns each portion to a different member of the home group. The students then convene within their home groups to discuss and deliberate on the assigned tasks, utilizing computer simulations obtained from freely accessible online sources. The computer provides information and exhibits simulations on the subjects, enabling the students to engage with and respond to the content. This approach encourages collaborative learning and allows students to contribute their unique perspectives and understandings to the group discussion.

The learning activity involved a jigsaw approach where students first worked within their home groups, each member specializing in a designated task by utilizing computer simulations and other resources. Once the home group learning was complete, the members moved into expert groups, which consisted of individuals from different home groups who had been assigned the same material segment. In the expert groups, the participants engaged in discussions and exchanged their understanding of the content. The team members then returned to their home groups to share and present what they had learned in the expert group discussions. Each home group then summarized the presentations made by its members. If any difficulties or misunderstandings arose, the teacher offered clarification through the use of computer simulations. After the group work activity, the students completed an individual quiz to assess their understanding of the concepts.

5 Conventional Methods

A teacher-centred approach in which the teacher plays the primary role in the learning process,

delivering lessons through CM like lectures, question-and-answer activities, and demonstrations. This approach lacks active student involvement in learning the concepts.

5.1 Integration of Computer Simulations and Jigsaw Learning Strategy (Designed)

The following strategy can be used to incorporate computer simulations and the JLS into the teaching and learning process (Indah, Sholihah, Septiani, & Rejekiningsih, 2020; Septiani, Paidi, & Darussyamsu, 2020):

Introducing the topic: Begin the lesson by providing an overview of the topic to be explored through computer simulations. Explain the main concepts and learning objectives to the students.

Dividing into jigsaw groups: Divide the students into small jigsaw groups, ensuring each group has an equal number of students. Assign each student in the group a specific aspect or subtopic related to the main topic.

Expert learning: Allocate time for individual research and learning. Each student becomes an expert in their assigned aspect by exploring relevant resources, including computer simulations. Encourage students to take notes and gather information to prepare for teaching their group members.

Simulation exploration: Provide access to computer simulations related to the topic. Allow students to interact with the simulations, manipulate parameters, and observe the outcomes. Encourage them to explore different scenarios and observe the effects on the system being simulated.

Jigsaw group discussions: Bring the jigsaw groups back together. In their groups, students take turns sharing their expertise by teaching their assigned aspect to their group members. This can be done through presentations, discussions, or demonstrations. Encourage active participation and questioning within the groups.

Collaborative learning: After each student has shared their expertise, a collaborative discussion was facilitated where students integrate their knowledge and discuss the connections between different aspects of the topic, encouraging critical thinking, problem-solving, and the application of concepts learned through simulations.

Summarization and reflection: Conclude the lesson by summarizing the key points covered and encouraging students to reflect on their learning experiences. Ask them to identify any challenges faced and discuss how the simulations and JLS helped in understanding the topic better.

6 Data Analysis Method

The data were gathered, arranged, and then analysed in the light of the research hypothesis. First, the data were checked for homogeneity and normalcy to make sure the presumptions weren’t broken. In the current investigation, a one-way ANOVA was used to assess if there was a significant difference in attitudes between the experimental and comparison groups (EG1, EG2, and CG) before and after the intervention. Furthermore, when using CSIJLS for learning, an independent t-test was utilized to determine whether male and female students’ attitudes towards chemistry differed noticeably. Version 26 of the Statistical Package for Social Sciences software was used to do the analysis. To guarantee dependable inferences from the data, every statistical test was assessed for significance at a threshold of alpha of 0.05.

6.1 Validity and Reliability of the Instrument

Chemistry college teachers examined the test’s content validity, whereas English high school teachers judged the test’s validity in terms of expression. The 36 eleventh-grade students who made up the sample did not attend the school where the research instrument was used. The investigators chose to administer the pilot test in grade 11 because the participants had previously studied in grade 10. Cronbach’s alpha was used to calculate the reliability coefficient of the Likert scale tests for chemistry attitudes, and the result was 0.928. Because the chemistry attitude test had a reliability coefficient greater than 0.70, the instrument was therefore considered appropriate for the research.

7 Findings

7.1 Intervention Groups Analysis of Pre-Test Results

The study was conducted with the understanding that the intervention groups it would use would be comparable. A one-way ANOVA was used to examine the pre-test averages for the two EGs and one CG using information gathered from the pre-attitude test. Tables 1 and 2 provide the statistical results for each group that were analysed.

Table 1

Result of students’ pre-test scores in each group’s attitudes

Dependent variable	Group	N	Mean	SD
Pre-attitude test	CSIJLS	54	2.35	0.72
	JLS	49	2.30	0.62
	CM	41	2.21	0.57
	Total	144	2.29	0.64

Table 2

One-way ANOVA result table for attitude test pre-test scores

Dependent variables	Source	Total squares	Df	MS	F	Sig.
Pre-attitude test	Between groups	0.487	2	0.243	0.583	0.560
	Within groups	58.899	141	0.418
	Total	59.385	143

Based on the pre-test results for the attitude test in Table 1, the descriptive statistics showed that the groups’ mean scores were nearly identical for every research group. The CSIJLS group’s mean score was 2.35, the JLS group’s was 2.30, and the CM group’s was 2.21 (Table 1). This indicates that the results for these groups were comparable.

Following the analysis of the descriptive statistics, a one-way ANOVA was performed to ascertain whether the groups’ three dependent pre-test scores differed significantly from one another. Before evaluating the pre-test outcomes, the ANOVA suppositions were verified. The pre-test data’s skewness and kurtosis z-values for each of the three dependent variables were determined to be within allowable bounds, suggesting that the distribution of the data was roughly normal. Furthermore, the homogeneity of variance for the pre-attitude dependent variables was verified by Levene’s test, guaranteeing that the populations from which the samples were drawn did not differ. This made it possible to employ ANOVA appropriately.

A statistically significant difference in the mean scores between the treatment and CGs were also revealed by the ANOVA analysis. According to Table 2, the attitude test results showed that the groups’ scores were comparable, as evidenced by F(2,141) = 0.583, p = 0.560. The ANOVA results provide evidence that the groups were almost identical before the intervention, which is an important finding. For this reason, before applying CSIJLS, there was no discernible difference in the attitudes of the three groups. For assessing the treatment’s (CSIJLS) effects after the intervention, this result provides a strong basis.

8 Treatment Effects on Attitude Post-Test Scores in Students

Investigating the possible effects of integrating the computer simulations with the jigsaw learning approach on students’ attitudes, the researcher performed a one-way ANOVA analysis on the post-test data of the chemistry attitude Likert scale test. The researcher evaluated the assumptions of normality and homogeneity of variance before conducting the one-way ANOVA. Levene’s Test indicated the variances were presumed to be identical. Additionally, the outcome variable’s results were found to be approximately normally distributed. The descriptive data are provided in Tables 3–5, together with a summary of the one-way ANOVA results.

Table 3

Outcomes descriptive statistics of the three groups’ scores on attitude

Group type	Scores for attitude
Group type	N	M	SD
CSIJLS	54	3.8426	0.45602
JLS	49	2.9592	0.45913
CM	41	2.4524	0.60466
Total	144	3.1462	0.76369

Table 4

ANOVA Summary table for attitude means across three groups based on test scores

Source	Total squares	Df	MS	F	Sig.	η ²
Between groups	47.635	2	23.818	93.900	0.000	0.51
Within groups	35.765	141	0.254
Total	83.400	143

Table 5

Tukey HSD multiple comparisons of JLSICS, JLS, and CM groups on students’ attitude

(I) Group of students	(J) group of students	MD (I–J)	SE	Sig.	95% CI
(I) Group of students	(J) group of students	MD (I–J)	SE	Sig.	Lower limit	Upper limit
CSIJLS	CM	1.39015*	0.11300	0.000	1.1197	1.6606
CSIJLS	JLS	0.88341*	0.09029	0.000	0.6686	1.0982
JLS	CM	0.50674*	0.11498	0.000	0.2317	0.7818
JLS	CSIJLS	−0.88341*	0.09029	0.000	−1.0982	−0.6686
CM	JLS	−0.50674*	0.11498	0.000	−0.7818	−0.2317
CM	CSIJLS	−1.39015*	0.11300	0.000	−1.6606	−1.1197

*The mean variation is considered statistically significant at the 0.05 level.

The descriptive test results for each of the three student groups’ attitudes are shown in Table 3. In terms of mean test scores for student attitude, the CG performed the lowest (M = 2.45, SD = 0.60), whereas the CSIJLS group performed best (M = 3.84, SD = 0.46). This indicates that the JLSICS group exhibited the most positive attitude, followed by the JLS alone group, while the CM group exhibited the least positive attitude.

Table 4 shows the attitude test post-test mean scores for each of the three groups revealed a statistically significant difference (F(2, 141) = 93.81, p < 0.001, η ² = 0.51), according to the one-way ANOVA results. Standards state that statistically significant effects are large in terms of effect sizes (Cohen, 1992). These results indicate that the JLSICS significantly affects the attitudes of students, and the effect size gives an objective indication of how much of an impact it has.

Table 5 displays the mean differences between the three groups, three post hoc tests were carried out following the completion of a statistically significant ANOVA. The JLS alone group (M = 2.96, SD = 0.46, p < 0.001) and the CSIJLS group (M = 3.84, SD = 0.46) differed significantly. Furthermore, a statistically significant distinction was seen between the CSIJLS and CM groups (M = 2.45, SD = 0.60, p < 0.001). Moreover, statistically significant differences were also shown between the JLS alone and CM groups (Table 5). Consequently, the first hypothesis (H1) was disproved, stating that there would be no appreciable distinction in the attitudes of students taught CSIJLS as opposed to JLS alone and CM.

The bar graph illustrating the attitude test results also revealed that, although CSIJLS improved more than JLS alone, students in both the CSIJLS and the JLS alone groups observed an improvement in their achievement (Figure 2). The comparative group, on the other hand, which was taught using CM, had less improvement. As a result, in terms of attitude test scores, the CSIJLS and JLS alone groups scored better than the CM group (Figure 2).

Figure 2

The effect of intervention on attitude for CSIJLS, JLS, and CM groups.

An independent t-test was employed to analyse the data and see whether there was a significant difference in the attitude mean scores between male and female students who employed the CSIJLS. Table 6 presents the results of the analysis.

Table 6

Post-attitude test using an independent t-test for both male and female students in the JLSICS group

Variable	Gender	N	Df	Mean	SD	t-value	p-value
	Male	27	52	3.78	0.483
CAST						0.241	0.163
	Female	27		3.75	0.354

The analytical findings are displayed in Table 6, which demonstrates that there was no significant difference in the attitudes of male and female students as a result of the CSIJLS impact (A t(52) = 0.241, p = 0.163, p > 0.05). This indicates that among students who were taught using CSIJLS, there was no discernible difference in attitudes between male and female students. Male and female students have participated and engaged in the classroom equally as a result of the teaching strategy used inside the CSIJLS framework, which created an inclusive and encouraging environment. As a result, the second hypothesis was approved. It can be concluded that there was no discernible difference in the attitudes of male and female students when both genders were taught using CSIJLS (Figure 3).

Figure 3

Bar graphs mean of attitude test scores by three groups by gender showing three types of instructions.

The data are shown in Table 7 and show that the mean gain scores for male and female students were 1.30 and 1.54, respectively. These results imply that both groups benefited from CSIJLS.

Table 7

The JLSICS group yielded distinct mean gain outcomes for the attitudes of male and female students

Group	Pre-test	Post-test	Main gain score
Male	2.48	3.78	1.30
Female	2.21	3.75	1.54

9 Discussions

A considerable difference in the students’ attitudes was found by analysing the data for the first hypothesis, with a preference for the students in the EG1. When compared to students who were taught using CM, those who were taught utilizing the CSIJLS and JLS alone methods demonstrated greater scores on attitude tests. However, students who used the CSIJLS demonstrated noticeably more positive attitudes than students who used the JLS alone or CM of instruction.

The results of this study are consistent with the studies by Gambari and Yusuf (2017), Patrick and Ochuko (2010) and Sung, Chang, and Liu (2016) showed that the inclusion of interactive components in CSI and CSIJLS approaches can increase student attitudes, engagement, and understanding of the subject matter. These findings are further supported by the work of Sung et al. (2016), which has demonstrated the value of CSIJLS strategies in encouraging student engagement and developing positive attitudes towards learning chemistry. Additionally, the studies by Barnard and Thompson (2015) and Tüysüz (2010) found that the computer-assisted learning approach improved students’ perceptions of chemistry and matter separation. This is likely because CSIJLS encourages peer cooperation and engagement, making it easier for students to participate in class discussions and share their expertise, viewpoints, and problem-solving strategies. This cooperative learning environment fosters a friendly and inclusive classroom culture, where students feel comfortable sharing their ideas and asking questions. The findings of this study also confirm the findings of Aytekin and Fahme (2017) and Jabeen and Afzal (2020), who found that when students were taught using computer-based instructions, their attitudes towards learning acid and base concepts were higher than those taught using CM. This is because the computer-based instructions helped students integrate the micro, sub-micro, and symbolic representations of chemistry, which can enhance their understanding of the subject matter. Similarly, the collaborative and cooperative aspect of CSIJLS enables students to work together, share knowledge, and create a thorough grasp of chemistry as a group, which eventually improves academic results and fosters attitudes of students towards the topic.

The most recent results show that there was no discernible difference in the attitudes of students who were taught chemistry using computer-supported methods versus those who were taught using conventional approaches (Oladejo, Nwaboku, Okebukola, & Ademola, 2023; Udu, 2018b), which is in contrast to this research findings.

On the other hand, when a computer simulation was integrated with JLS to teach chemistry to students, the results of the study done for hypothesis two showed that there was no discernible difference in the attitudes of male and female students. In the same way, there was no discernible difference (p < 0.05) in the EG’s post-test mean CALST scores. Furthermore, when teaching utilizing CSIJLS, there was no difference in the attitudes of male and female students, according to the results of the ANOVA. The lack of statistically significant differences in the attitudes of male and female students towards chemistry indicates that CSIJLS was equally successful in encouraging positive attitudes in both genders towards the acid and base.

The present study’s outcomes are consistent with previous research findings such as Adhikari (2020), Babagana, Yaki, and Idris (2016), Shekhar and Devi (2018), and Wagbara (2021) found no statistically significant differences between the average scores of male and female students who learned biology or chemistry through computer-supported interactive learning systems. These findings indicate that the teaching strategies employed in the computer-supported interactive learning systems are successful in encouraging inclusivity and gender equality in the classroom. Similarly, the content and methods of instruction used in CSIJLS most likely create an atmosphere that motivates all students, regardless of gender, to become engaged, have positive attitudes, and be interested in the material. As a result, CSIJLS appears to create a more equal learning environment in which gender-based differences in participation and attitudes are reduced, leading to improved learning outcomes for all students.

In contrast, the study by Szymanowicz and Furnham (2019) found that when given computer-based instruction, male students had a more positive attitude towards chemistry and thought it would be more beneficial for their male peers than female peers. This suggests that there may be deeply ingrained gender preconceptions and biases that shape people’s perceptions of certain subjects, such as chemistry, which have historically had a greater representation of men in the field.

Following the CSIJLS intervention, the results demonstrated that there was no appreciable change in the attitudes of male and female students in the EG. With its welcoming and supportive learning atmosphere, CSIJLS lessens the likelihood of gender prejudices or preconceptions that would otherwise influence attitudes. Moreover, the use of computer simulations as a teaching tool contributed to the neutralization of gender-based differences. CSIJLS offers an engaging and interactive learning environment that transcends conventional gender stereotypes, allowing male and female students to simultaneously actively participate and independently explore issues. Moreover, it offers a path forward to granting male and female students equal access to educational opportunities and creating a level playing field where all students can develop positive engaged attitudes towards chemistry.

10 Conclusions

CSIJLS was preferred over conventional teaching methods and the JLS alone, indicating significant differences in the groups’ attitudes. This study shows that students’ attitudes towards the teaching and learning of chemistry are improved by the CSIJLS and JLS after eight weeks of learning. Students were able to develop a positive attitude towards learning chemistry despite the difficulty of microscopic and submicroscopic particles in the subject because of the integration of computer simulations and the jigsaw learning technique. CSIJLS implementation has the potential to improve students’ attitudes towards chemistry, as well as their interest in and achievement in the subject. When teaching chemistry using CSIJLS, students’ attitudes are unaffected by their gender and treated both male and female students equally. Consequently, it is encouraged that chemistry teachers use CSIJLS as a teaching method in their courses. It is also suggested that teachers use it in their classes to encourage students to have a good attitude towards chemistry.

Acknowledgements

We express our gratitude to the students who participated in this study. We are also grateful to the school administration and teachers for their help, encouragement, and direction.

Funding information: The authors state no funding involved.
Author contributions: Shimelis Kebede Kekeba: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Writing-original draft, Writing–review and editing. Abera Gure: Conceptualization, Methodology, Validation, Investigation, Supervision, Writing–review and editing. Teklu Tafesse Olkaba: Conceptualization, Methodology, Validation, Investigation, Supervision, Writing-review and editing.
Conflict of interest: The authors state no conflict of interest.
Consideration of ethical issues: When gathering, evaluating, and disseminating data, the researcher took into account the following ethical considerations: To carry out the study, the researcher needs management approval at the school. The researcher then sought informed consent from the research participants after explaining the objectives of the study to them. Chemistry teachers and enrolled students were given a consent form that detailed the objectives and procedures of the study. It was made very clear on the permission form that taking part in the study was entirely optional. Finally, the researcher promised to protect the privacy of the information gathered from study participants.
Data availability statement: Data can be provided upon request.

Appendix

Chemistry Attitude Likert Scale Test

Title: Effects of Computer Simulations Integrated with Jigsaw Learning Strategy on Students’ Attitudes Learning Chemistry.

Instruction: Dear students, I am conducting empirical research. The purpose of this questionnaire is to collect data on the title indicated above. Dear respondents, since the reliability of this study depends on the objectivity of your response, you are kindly requested to offer your response based on factual and genuine information.

Thank you in advance for your willingness and kind cooperation!

The researcher.

Direction: 1. Do not write your name.

2. Put a tick mark (✓) on the space provided.

3. Respond to all questions precisely and genuinely.

4. Jigsaw Learning Strategy Integrated with Computer Simulations (JLSICS)

Section 1 : Background Information

1.1. Name of school_____________________________________

1.2. Student’s code ____________

1.3. Sex: Male [ ] Female [ ]

1.4. Age ____________________

1.5. Grade _____ Section ______

Section 1 : Items

Table A1 contains a list of statements related to the title indicated above. Please read them carefully and give a proper response to each statement. Put a tick mark (√) on one of the options you want to show your agreement using the scale below.

Table A1

Chemistry attitude Likert scale test

No	To what extent would you agree or disagree with the following statements	1	2	3	4	5
1	Learning chemistry lessons through JLSICS is encouraging me
2	I am effective in learning all lessons of chemistry through JLSICS
3	I get good chemistry results when I learn it through JLSICS
4	Learning chemistry through JLSICS is enjoyable and attractive for me
5	I am more interested in learning chemistry after using JLSICS
6	Learning chemistry through JLSICS is useful but time-wasting for me
7	Learning chemistry through a JLSICS is easy and fun for me
8	Learning the difficult and complex concepts of chemistry through JLSICS is clear and understandable for me
9	I like JLSICS because it reduces the abstract concepts of chemistry
10	Learning through JLSICS increases my feelings towards chemistry
11	JLSICS is important for me to be successful in chemistry lessons
12	I like JLSICS because it saves my time and energy in learning chemistry
13	JLSICS increases my motivation to learn chemistry
14	I am not comfortable learning chemistry using JLSICS
15	Learning through JLSICS increases my confidence to participate actively in class
16	Learning chemistry through JLSICS encourages me to communicate more with my classmates and teacher
17	I pay more attention when a JLSICS is used in teaching and learning chemistry
18	I like JLSICS because it enhances my achievement in chemistry
19	I acquire essential chemistry concepts easily through JLSICS
20	Learning chemistry through JLSICS enables me to retain the main points easily

The above attitude Likert scale questions are adapted from: https://www.researchgate.net/publication/305650994 and Mahdi (2018).

Scale: 5 = Strongly Agree; 4 = Agree; 3 = Neutral; 2 = Disagree; 1 = Strongly Disagree

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Received: 2024-05-03

Revised: 2024-08-09

Accepted: 2024-11-14

Published Online: 2024-12-06

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

https://doi.org/10.1515/edu-2024-0052

Keywords for this article

attitude; male and female; jigsaw learning strategy; computer simulation

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