Content area
Purpose
The purpose of this study was to investigate the impact of using a jigsaw learning strategy integrated with computer simulation (JLSICS) on the academic achievement and attitudes of students, along with exploring the relationships between them in the process of learning about acids and bases.
Design/methodology/approach
The research design used in the study was quasi-experimental, using non-equivalent comparison groups for both pre- and post-tests. A quantitative approach was used to address the research problem, with three groups involved: two experimental and one comparative group. The treatment group, which received the JLSICS intervention, consisted of two intact classes, while the comparison group included one intact class. Data collection involved achievement tests and attitude scale tests on acid and base. Various statistical analyses such as one-way analysis of variance, one-way multivariate analysis of variance, Pearson product-moment correlation, mean and standard deviation were used for data analysis.
Findings
The study’s results revealed that the incorporation of the JLSICS had a beneficial influence on the academic achievement and attitudes of grade 10 chemistry students towards acid and base topics. The JLSICS approach proved to be more successful than both conventional methods and the standalone use of the jigsaw learning strategy (JLS) in terms of both achievement and attitudes. The research demonstrated a correlation between positive attitudes towards chemistry among high school students and enhanced achievement in the subject.
Research limitations/implications
The study only focused on one specific aspect of chemistry (acid and base chemistry), which restricts the applicability of the findings to other chemistry topics or subjects. In addition, the study used a quasi-experimental design with a pretest-posttest comparison group, which may introduce variables that could confound the results and restrict causal inferences.
Practical implications
This study addresses the gap in instructional interventions and provides theoretical and practical insights. It emphasizes the importance of incorporating contemporary instructional methods for policymakers, benefiting the government, society and students. By enhancing student achievement, attitudes and critical thinking skills, this approach empowers students to take charge of their learning, fostering deep understanding and analysis. Furthermore, JLSICS aids in grasping abstract chemistry concepts and has the potential to reduce costs associated with purchasing chemicals for schools. This research opens doors for similar studies in different educational settings, offering valuable insights for educators and policymakers.
Originality/value
The originality and value of this study are in its exploration of integrating the jigsaw learning strategy with computer simulations as an instructional approach in chemistry education. This research contributes to the existing literature by showing the effectiveness of JLSICS in improving students’ achievements and attitudes towards acid and base topics. It also emphasizes the importance of fostering positive attitudes towards chemistry to enhance students’ overall achievement in the subject.
Introduction
Education is a deeply social undertaking, and throughout history, the effectiveness of education has been connected to competent teachers who engage in meaningful interactions with their students. The incorporation of technology-based instruction in science education has a broad influence on the field of education and promotes the development of student-centred learning environments. By using technology-based teaching methods and information and communication technology (ICT) in the teaching and learning process, the quality and availability of education can be enhanced, along with increasing student motivation and fostering a conducive learning atmosphere (Daniels, 2012).
Despite the global emphasis on fostering students’ learning, there is a lack of use of instructional strategies that could support scientific ideas and practices among teachers, including in Ethiopia (Darling-Hammond et al., 2020). Consequently, even after receiving education, many students still possess misconceptions about science. One theory suggests that this is due to teachers primarily focusing on students’ information gain as a means to help them retain information for exams, resulting in students leaving science classes with misconceptions. This can be especially challenging in subjects like chemistry, where students are regularly required to apply scientific theories to complex computational and conceptual problems (Broman and Parchmann, 2014). Considering this, a key objective of chemistry education, as part of science education, is to understand how students learn chemistry, teach chemistry effectively and enhance learning outcomes by adopting new teaching methods that encourage students to move away from rote memorization and towards a deeper understanding and application of fundamental chemistry principles (Sewry and Paphitis, 2018).
To achieve a deeper understanding of chemistry, students need to study and comprehend the macroscopic, sub-microscopic and symbolic aspects of chemical knowledge. Whether in primary or higher education, it is essential for chemistry learners to grasp scientific concepts at all three levels and be able to integrate their knowledge effectively (Johnstone, 2009). Connecting these three levels is crucial to perceive the practical applications of chemical knowledge in everyday life. Furthermore, difficulties faced by students at one level can have an impact on their understanding of the other levels (Satriadi et al., 2019).
Various approaches have been used in both in-person and technology-driven educational settings to tackle micro-macro thinking abilities. These technological solutions possess a considerable capacity to showcase ever-changing dynamic phenomena and render the imperceptible aspects visible. Using computer simulations, one can generate and evaluate diverse representations of concrete situations, such as graphs and diagrams (Hesti, 2021). Scholars and instructors have devised technology-enriched learning platforms specifically targeting various micro- and macro-thinking skills.
Due to their inability to connect numeric representations with underlying chemical concepts, students, including those with high academic achievements, may lack a solid understanding of fundamental principles (Sukor et al., 2010). Consequently, students at various educational levels, ranging from elementary to tertiary, encounter difficulties comprehending and effectively mastering chemistry. Consequently, students face challenges in comprehending more advanced concepts that build upon these fundamental principles (Temechegn, 2014). Consequently, chemistry is commonly perceived as a challenging and intricate field of study among students, educators and instructors.
Even though learning style theories have been around for thirty years, recent studies by Akinbobola (2019) and Yesgat (2022) have revealed that the majority of teachers still use traditional teaching methods that do not take into consideration the students’ learning abilities. Instead of being focused on activities and tailored to the learners’ needs, these conventional teaching approaches are based on theory, heavily reliant on lecturing and teacher-centred. Numerous investigations have demonstrated that students perform poorly in science courses in secondary school (Ugwu and Namani, 2023). Various factors hinder the achievement of scientific instruction goals by students, including the use of uninteresting and inappropriate teaching methods by science teachers. According to these studies, teachers tend to avoid activity-based teaching strategies, which have been proven to be more effective. Instead, they rely heavily on simple yet often unsuccessful and inappropriate teaching strategies.
There have been various contemporary approaches suggested for teaching chemistry, including discovery, jigsaw strategy, cooperative learning and computer-assisted instruction (CAI). These strategies have been shown to be effective (Aljafari, 2021). To enhance the understanding of abstract and difficult topics in chemistry, the integration of a jigsaw learning strategy and computer simulation has been proposed (Ugwu and Namani, 2023). By using simulations, students can develop problem-solving skills and gain a deeper comprehension of chemistry concepts independently.
Various research studies have indicated that computer-supported jigsaw learning settings have been highly effective in promoting student interaction and fostering positive attitudes towards learning chemistry (Sung et al., 2016). In a separate study, Woodward et al. (2016) compiled findings from research studies and meta-analyses examining the impact of computer-based instruction (CBI) on student achievement and attitudes, and the results consistently favoured the use of CBI over traditional conversational instruction. Similarly, Akinsola (2007) conducted a comparative study on the achievement and attitudes of students in a secondary school chemistry course using a CAI group and a conventional teaching group. The study revealed that the CAI-based simulation-game environment group demonstrated higher achievement levels and more positive attitudes towards chemistry compared to the conventional teaching method.
The jigsaw learning strategy is an instructional method that encourages cooperative learning and active student involvement in the classroom. It entails dividing students into small groups, where each member becomes an expert on a specific topic or concept and then shares their knowledge with their peers. This collaborative learning technique has been proven to offer numerous significant advantages. According to research conducted by Slavin (2020), jigsaw learning enhances student motivation, engagement and critical thinking abilities. It also fosters positive interdependence, where students rely on one another for information, thereby promoting teamwork and collaboration. In addition, a study by Arias et al. (2020) demonstrated that jigsaw learning can reduce prejudice and improve intergroup relations as students from diverse backgrounds work together towards a shared objective.
The jigsaw learning strategy consists of four stages (Sudin et al., 2021). In the first stage (introduction), the class is divided into diverse “home” groups comprising three to seven students. The teacher provides a brief overview of the topic and explains how it will be divided into subtopics. Moving on to stage two (focused exploration), each member of a home group selects a specific subtopic. Students who have chosen the same subtopic then convene in “jigsaw groups” to study the material and prepare to present it to their home groups. Stage three (reporting and reshaping) entails students returning to their home groups, sharing their findings and beginning to refine their understanding of the topic. Finally, stage four (integration and evaluation) involves students synthesizing their learning to produce the final piece of work. Stages three and four offer students the chance to teach their newly acquired knowledge to their home group peers and learn from the material presented by other group members (Cochon et al., 2023) (refer to Figure 1). This method has proven to be effective in chemistry and has demonstrated positive impacts on student achievement, attitude and engagement.
Computer simulations have the flexibility and 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 (Ramasundaram et al., 2005). 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 (Apkan, 2002). 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).
The integration of computer simulation with a jigsaw learning strategy has been proven to be an effective tool in helping students comprehend the complex topics of the chemistry subject. According to Schmid et al. (2023), this approach enables students to visualize difficult and abstract concepts, thereby enhancing their understanding. Compared to other instructional techniques, computer simulation integrated with a jigsaw learning strategy offers numerous advantages. One notable benefit is that students become more actively engaged and motivated when using this approach. In addition, this method is highly flexible, as both students and teachers have significant control over its implementation. Moreover, it provides students with a closer connection to real-world experiences (Elagha and Pellegrino, 2024).
Multiple investigations have revealed that integrating the jigsaw learning strategy with computer simulations can assist students in attaining improved learning outcomes (Shen and 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 use computer simulations in facilitating student learning (Johnson et al., 2015).
Conventional methods of education that focus on transmitting knowledge and using active techniques have limitations when it comes to effectively explaining abstract and complex ideas (Scardamalia and Bereiter, 2014). These shortcomings suggest the potential value of computer-assisted technologies, which can present chemistry concepts clearly and understandably. Through computer simulations, students can gain a deep understanding of acid and base phenomena that may be difficult to grasp through other means (Supriadi et al., 2024). Consequently, there is a growing recognition of the need for new teaching approaches to enhance science and chemistry education in Ethiopia (Aweke et al., 2017). These approaches should take into account both the expectations of current students and the latest advancements in technology.
The reason behind students’ low achievement and negative attitudes towards chemistry can be attributed to teachers’ failure to use innovative teaching methods in the modern classroom (Ugwuanyi and Okeke, 2020). Adopting the use of JLSICS as a teaching and learning instruction in chemistry has been suggested as a contemporary approach that could potentially enhance students’ achievement and attitudes (Nipyrakis et al., 2023).
Theoretical framework
The integration of the jigsaw learning strategy with computer simulations in the context of chemistry education for Grade 10 students can be supported by theories such as the cognitive load theory, social learning theory and correlation theory (Chen et al., 2023).
According to cognitive load theory, learning is influenced by the cognitive load imposed on learners’ working memory (Elsayed et al., 2023). When students engage in complex tasks, such as understanding chemistry concepts, their cognitive load can become overwhelmed, leading to decreased achievement and negative attitudes. By integrating the jigsaw learning strategy, which involves cooperative learning and dividing students into expert groups, with computer simulations, the cognitive load can be optimized. The jigsaw strategy allows students to become experts on specific topics and then share their knowledge with their peers, promoting active engagement and reducing cognitive load. Computer simulations can enhance the learning experience by providing visualizations and interactive experiences, making abstract concepts more concrete and easier to understand. This integration can positively impact students’ achievement and attitude in chemistry, as it reduces cognitive load and enhances the learning process.
According to social learning theory, learning occurs through observation, imitation and modelling of others’ behaviour (Dong et al., 2024). When students collaborate in a jigsaw learning environment, they have the opportunity to observe and learn from their peers’ expertise. By integrating computer simulations, students can interact with virtual models and simulations, observing the behaviour and outcomes of chemical reactions (Bhatt et al., 2024). This observational learning can lead to increased achievement and positive attitudes towards chemistry. Furthermore, the jigsaw strategy promotes social interaction and cooperation, fostering a supportive and collaborative learning environment (Buelow et al., 2024). This social aspect of learning can enhance motivation, engagement and positive attitudes towards chemistry, leading to improved achievement.
The correlation theory also plays a role in this context. It suggests that there is a relationship between students’ attitudes and their academic achievement (Berg et al., 2023). When students have positive attitudes towards a subject like chemistry, they are more likely to engage in learning activities and invest effort, leading to higher achievement (Westreich et al., 2018). By integrating the jigsaw learning strategy with computer simulations, students’ attitudes can be positively influenced. The collaborative and interactive nature of the jigsaw strategy, combined with the engaging and immersive experiences provided by computer simulations, can promote a positive learning environment and increase students’ interest and motivation in chemistry. This, in turn, can lead to improved achievement outcomes. The correlation between students’ attitudes and achievement in chemistry can be explored through quantitative measures, such as surveys and assessments, to determine the strength and direction of the relationship (Perera et al., 2023).
By reducing cognitive load, promoting social interaction and observational learning and fostering positive attitudes, this approach has the potential to enhance students’ learning experiences and outcomes in chemistry (Shambare and Simuja, 2022).
Several studies have highlighted that a jigsaw learning strategy integrated with computer simulations has a significant impact on students' learning outcomes (Şar et al., 2017). However, secondary school teachers in developing countries like Ethiopia, including chemistry teachers, have made limited efforts to integrate this innovative teaching methodology into their instructional practices (Gabler and Ufer, 2024).
The findings of a survey conducted in Ethiopia among students, teachers and school administrators reveal that the current level of ICT integration in education is inadequate. According to the study, teachers either rarely use ICT in the classroom or do not use it at all, except when teaching it as a subject (Whalen et al., 2024). Furthermore, less than 20% of teachers reported using ICT for science lesson planning or in-class instruction. In the Oromia region of Ethiopia, none of the secondary schools examined incorporated ICT for classroom instruction (Bati and Workneh, 2021).
Despite the high enrolment rates, significant curriculum reforms and other initiatives, academic achievement in Ethiopia has remained low across all education levels, as evidenced by student scores on the Ethiopian Third National Learning Assessments (ETNLA). The majority of secondary school learners lack the necessary knowledge, attitudes and skills required. Consequently, these students face challenges in transitioning to the workforce upon completing grades 10 and 12 (NEAEA, 2020).
The percentage of students who have successfully passed the chemistry examinations in both grades 10 and 12 national examinations (Dermentzi, 2024; NEAEA, 2020) has consistently remained below 50%. For example, in 2017, the percentage of grade 10 students who passed chemistry at the credit level was 49.1%, which further declined to 47.9% in 2018 and 46.7% in 2019. These figures fall short of the minimum requirement of 50%, indicating a declining trend in achievement over time. Similarly, the mean score for Grade 12 students in chemistry was 42.7% in 2017, which decreased to 40.1% in 2018 and further dropped to 37.1% in 2019. These scores do not meet the secondary school-level requirements set by the Ethiopian Ministry of Education for progression to the next grade level. In addition, secondary school students in Ethiopia struggle with chemistry, as their grade averages are below the B grade required for admission to most university fields (Sadler et al., 2024). This highlights the ongoing challenge of maintaining educational quality in Ethiopia.
In Ethiopia, the use of CAIs for teaching chemistry at the secondary school level is not widespread. In addition, there is a lack of comprehensive research comparing conventional teaching methods and jigsaw learning strategies integrated with computer simulations in this context (Yesgat, 2022). Similarly, there is limited empirical evidence on the impact of JLSICS on students’ academic achievement and attitude towards chemistry (Tefera et al., 2021). Furthermore, no research has been conducted on the relationship between students’ attitudes and their chemistry achievement in secondary school. Hence, this research aims to examine the impact of JLSICS on the academic achievement and learning attitudes of students regarding acid and base, as well as chemistry in general, at Jimma secondary school.
It specifically aims to examine the effect of JLSICS on the correlation of students’ achievement and attitudes in learning acid and base.
The researcher developed two study questions to achieve these objectives:
Related literature
Over the last thirty years, educators and educational professionals have been striving to incorporate technology into the classroom (Ziphorah, 2014). This endeavour involves integrating technology into everyday classroom tasks and educational activities. Technologies such as computers, tablets, smartphones, digital cameras, social media platforms, software and the Internet are just a few examples of the tools used. Students who possess the ability to integrate technology can choose from a variety of tools to enhance their learning, analyse and synthesize information and present their work in a formal setting (Wang and Degol, 2014).
Technology should be integrated into the classroom environment and made easily accessible like any other educational material (Barrett et al., 2015). Moreover, the utilization of technology has transformed how educators disseminate information by enabling them to interact with a broader spectrum of students and evaluate their comprehension through various means (Davies et al., 2013). Instruction that relies on technology not only enhances students’ understanding and knowledge but also fosters collaborative learning, heightens students' enthusiasm for acquiring knowledge and cultivates their problem-solving aptitude (Blau et al., 2020).
Education experts propose that incorporating technology into the classroom is advantageous for both teachers and students. One benefit is that technology can boost student motivation and equip them with vital learning skills (Mayer and Estrella, 2014). The ability to effectively use technology for retrieving information is crucial for successful integration in educational environments. Hence, it is imperative to ensure the availability of technology and other essential classroom resources. A proficient educator who integrates technology into the classroom focuses on using these tools to facilitate learning rather than placing undue pressure on students to learn. As a result, technology not only facilitates knowledge acquisition but also makes the learning process more manageable.
The study of chemistry is crucial to the advancement of science (Sampath, 2016). However, misconceptions that endure throughout their academic careers result from students’ frequent inability to grasp basic chemistry ideas (Sözbilir et al., 2010). Teachers are generally aware that inadequate use of technology in the classroom and poor teaching methods are the main causes of the difficulties students encounter when learning chemistry (Woldamanuel et al., 2014).
When science courses like chemistry are taught using conventional teaching methods, students could end up learning scientific facts but not understanding the underlying ideas (Radović et al., 2024). As a result, pupils might find it difficult to understand scientific concepts, which could have an impact on their academic achievement, cognitive growth and memory retention in chemistry classes. It is often acknowledged that resolving misunderstandings requires more than just using conventional teaching methods (Bhukuvhani et al., 2012).
Literature has shown that integrating computer simulations and jigsaw cooperative learning strategies enhances students’ communication skills, academic achievement, team efficacy and academic scores (Bati and Workneh, 2021). It has been suggested to implement this approach in classroom settings to enhance students’ learning outcomes and attitudes. Therefore, teaching abstract chemistry concepts with the jigsaw cooperative learning method integrated with computer simulations in more tangible and comprehensible forms can improve students’ achievement and attitudes towards the subject (Hernandez and Burrows, 2021). This method has proven to be effective in chemistry and has demonstrated positive impacts on student attitudes and engagement.
Furthermore, some studies showed that students who learn through jigsaw cooperative groups with CBI perform better than those who learn through individual or conventional classroom settings (Amosa and Mudasiru, 2017). Musengimana et al. (2021) studied the impact of CAI on students’ achievement in learning acid and base. They found that students who learned through CAI exhibited better achievement than those who learned through the conventional methods. Similarly, Yesgat et al.(2022) and Sahil et al.(2019) investigated the effects of combining the jigsaw technique with computer simulations on students’ achievement and attitudes. Their findings indicated that students who learned through the jigsaw technique combined with computer simulations performed better than those who learned through conventional-based learning methods in terms of achievement and attitudes.
While research conducted in Western countries has shown that integrating technology to higher levels of student achievement compared to conventional classroom learning (Vieira and Pedro, 2023), it cannot be assumed that the same levels of achievement would be replicated in the developing world, like Ethiopia, due to the distinct contextual factors at play.
One of the barriers hindering the improvement of educational systems through technology is the difficulty in implementing educational modifications (Ertmer and Newby, 2012). However, it is not feasible to expect that incorporating technology into learning processes only requires enhancing instructors’ digital technology skills without considering its pedagogical implications (Crossley and McNamara, 2016). Instead, teachers should use effective pedagogical strategies, including using technology to convey concepts and pedagogy, as well as supporting students who struggle with understanding (Eun et al., 2017). Educators studying technologically enhanced instructional techniques should also possess a strong understanding of instructional design, pedagogy and hardware and software knowledge.
Research materials and methods
This study used a quantitative research method, which used numbers to measure the effects of JLSICS on students’ achievement and attitudes towards learning acid and base to test specific hypotheses and theories by collecting and analysing numerical data. The study used a quasi-experimental design with a pre-test, post-test and a nonequivalent comparison group (CG). This design is effective for establishing causal relationships (Davison et al., 2022). Since each treatment group needs its pretest and posttest, the design also includes a CG and two treatment groups. According to the study’s design, students in experimental group one (EG1) were taught using JLSICS. Students in the CG were taught through CM, while students in experimental group two (EG2) were taught with JLS alone (refer to Table 1).
Table 1 illustrates a quasi-experimental design involving a pre-test-post-test CG. This particular design is frequently used in research to assess the impact of an intervention or treatment by analysing the pre- and post-test outcomes of JLSICS, JLS alone and CM groups.
Participants of the study
The participants of the study were Grade 10 students. One secondary school with better infrastructure, specifically a computer laboratory, was selected for quasi-experimentation. In addition, a chemistry teacher with ample experience in teaching grade 10 students was selected from the same school. To form the experimental and CGs, a simple random sampling technique was used to select three intact classes from the selected school. Two of these classes were assigned to the experimental groups, while one class was assigned to the CG. The stratified random sampling technique was used to ensure proper group formation within the class. A total of 144 grade ten students with ages older than 18 years old, consisting of 60 males and 84 females, participated in the study. To prevent any information contamination, the experimental and CGs were selected from different shifts.
Variables of the study
The study focused on three independent variables: JLSICS, CM and JLS. Students’ achievement and attitude scores from acid and base tests were considered as the dependent variables in the study.
Instruments for gathering data
The data for this research were gathered through the implementation of the acid and base achievement and attitude Likert scale tests.
Chemistry achievement test
A student’s academic achievement is assessed by a test. The test was comprised of 30 multiple-choice questions. Each question in the CAT had five possible answers, with each question being worth one point. Both the experimental and CGs took the CAT as both a pre-test and a post-test.
Chemistry attitude Likert scale test
The researcher modified the questionnaire based on existing studies (Duckworth, 2010; Mahdi, 2018; Tesfaye, 2012) to assess the students’ attitudes towards learning chemistry, specifically the concepts of acid and base, before and after being exposed to JLSICS, JLS alone and CM. The questionnaire consisted of 20 statements, and the participants responded using a five-point scale ranging from strongly agree (5) to strongly disagree (1). The questionnaire was administered to both the experimental and CGs before and after the intervention.
Validity and reliability of the instruments
Two experts in measurement and evaluation, as well as two experts in chemistry education, along with experienced secondary school chemistry teachers who had been teaching for over 15 years, reviewed and analysed the instrument used to collect data for the chemistry achievement and attitude tests in terms of face and content validity. Based on the feedback received, the instruments were revised. To assess the instruments, a separate school that was not included in the study’s sample of 36 11th-grade students was used. The internal consistency of the achievement test items was evaluated using a reliability coefficient. The reliability coefficient of the chemistry achievement test (CAT) was estimated to be approximately 0.78 using the Kuder-Richardson formula 20 (K-R20). In addition, the reliability coefficient of the chemistry attitude Likert scale test, calculated using Cronbach’s alpha, was determined to be 0.928, indicating good reliabilities (Demircioğlu and Mustafa, 2014).
Treatment procedure
We selected three sections randomly and formed two groups for treatment and one group for comparison. The teachers and students in the treatment groups received training during this time. Initially, we gave a concise overview of the objectives of the study, the strategies for implementing the procedure, the tasks to be completed during the treatment and the schedule for the treatment. The researchers supervised a ten-day program, with teachers spending three hours per day and students spending two hours per day in the program.
After completing the training, a pretest was administered to evaluate the chemistry achievement test and the attitude towards acid and base. This was conducted in three sections taught by the same chemistry teacher, followed by the implementation of the intervention. Each group received the same presentation on acid and base concepts, which were selected due to their widespread presence in daily life and their significance in understanding other chemistry topics (Anuar et al., 2021). During eight weeks, both the experimental and CGs received three 40-minute classroom sessions per week. The time dedicated to learning was equal for both groups. However, the experimental groups focused on using JLSICS, as well as using the JLS alone, to improve students’ attitudes and achievement when learning acid and base concepts.
To foster discussion among students, lessons were typically conducted using collaborative group work. The teacher formed groups based on factors such as gender, academic standing and emotional traits, resulting in seven groups consisting of approximately five to six students each. During the lesson, students were provided with thinking time before answering questions that assessed their advanced cognitive abilities and motivated them to participate. Various teaching methods, including jigsaw learning techniques, were used by the teacher, such as discussions, guidance, monitoring, observations, quizzes, oral inquiries, think-pair sharing, presentations and summarization.
In this study, various technologies were used, including a whiteboard, a laptop, a smartphone, a microphone and desktop computers. These tools were used in conjunction with applications such as Telegram, PowerPoint, LCD and Internet connectivity. The main objective of incorporating these technologies and software was to support jigsaw learning techniques both inside and outside of the classroom. The teacher not only worked individually and in groups but also used PowerPoint to create the course objectives. In addition, the teacher used an LCD to display tasks on a computer desktop while explaining the lesson’s goals. During this time, the teacher allowed sufficient time for both individual and group discussions regarding the relevant tasks.
At the end of each lesson, the teacher and researcher evaluated JLSICS. The teacher had the option to seek assistance from the researcher for any implementation issues and to receive feedback on improving the intervention. Following the treatment period, a post-test including an achievement test and an attitude test was administered to compare with the students’ pre-achievement and attitude scores.
Methods of data analysis
To determine if there were any significant differences in achievement and attitude between the two treatments and a CG, a one-way ANOVA was conducted. It is a statistical analysis used to determine if there are significant differences among the means of three or more independent groups. By comparing the variability within each group to the variability across groups, ANOVA helps determine if any observed differences are likely due to chance or actual disparities between the groups. The effects of independent variables on both achievement and attitude were simultaneously assessed using one-way MANOVA statistics. Furthermore, the relationship between achievement and attitude was evaluated using the Pearson product-moment correlation coefficient. Before the study, the necessary assumptions were examined and tested, including variance-covariance homogeneity, homogeneity of variances and univariate and multivariate normality. Mahalanobis distance values were calculated to identify extreme values in terms of multivariate normality. The analysis was performed using the Statistical Package for Social Sciences (SPSS) software application version 26.
Results
Analysis of quantitative pre-test results among groups
One-way ANOVA was used to compare the mean scores of the pretest, taking into account the information gathered from the pre-administration of the achievement and attitude tests for the three groups involved: two experimental groups and one CG.
The researchers implemented a one-way ANOVA to determine if there was a significant difference between the groups based on their two dependent pre-tests. Before analysing the pre-test results, the researchers confirmed that the assumptions of normality and homogeneity of variance for ANOVA were met. The allowable limits for skewness and kurtosis in the pretest data were displayed in Table 2, suggesting that the data had a reasonable distribution. Table 3 presented the results of the Levene test, which showed no statistical significance for all dependent variables, pre-achievement and pre-attitude tests and other assumptions of ANOVA such as homogeneity of variance. This indicated that there was no violation of the ANOVA assumptions.
The Shapiro-Wilk test results indicated that the test scores were evenly distributed among the groups as shown in Table 2. Table 2 demonstrates that the test scores for pre-achievement, pre-attitude, post-achievement and post-attitude were not statistically significant (p >0.05), confirming that the data followed a normal distribution. The skewness and Kurtosis z-values for the pre-post test results were generally normally distributed and the Shapiro-Wilk p-values were not statistically significant (p >0.05) (refer to Table 2). This implies that the data met the assumptions of a normal distribution.
Table 3 presents the results of the Levene’s Test for four tests: pre-achievement test (p = 0.595), pre-attitude test (p = 0.136), post-achievement test (p = 0.100) and post-attitude test (p = 0.180). The findings show that there were no noteworthy variations across any group, indicating that the homogeneity of variance assumption is confirmed (p > 0.05).
Tables 4 and 5 present the statistical data analysed for each group. Table 4 specifically displays the variations in mean and standard deviation for the pretest, considering the two dependent variables being studied. The descriptive statistics indicate that the mean values for both achievement and attitude were similar among research groups.
The mean scores of the groups for both achievement test scores and attitude test scores, as indicated by descriptive statistics for the pretest, were similar (refer to Table 4). The JLSICS group had a mean score of 4.56 for achievement test scores and 2.35 for attitude test scores. The JLS group had a mean score of 4.35 for achievement test scores and 2.30 for attitude test scores. The CM group had a mean score of 4.73 for achievement test scores and 2.21 for attitude test scores. These show that among instruction groups, there is a correlation between the mean scores for achievement and attitude. It indicates that those with higher achievements also tend to show more positive attitudes, whereas individuals with lower achievements show less positive attitudes.
The ANOVA findings indicated that there was no significant difference in the mean scores between the treatment and CGs as indicated in Table 5. For the achievement test, the F-value was 0.668 with a p-value of 0.514, and for the attitude test, the F-value was 0.583 with a p-value of 0.560. These results suggest that the groups performed similarly on both the achievement and attitude tests (refer to Table 5). It is noteworthy that before the intervention, the groups were nearly identical according to the ANOVA results mentioned earlier. This implies that there were no noticeable disparities in the attitudes or learning achievements among the three groups before implementing JLSICS.
Analysing the treatment effects on achievement post-test scores of students
To assess the impact of the intervention on students’ academic achievement, a one-way ANOVA was used. The participants were divided into three groups: JLSICS, JLS alone and CM. To determine if there were any significant differences between the groups on their three dependent post-test, we used one-way ANOVA. Before evaluating the results of the post-tests, we checked if the assumptions for using ANOVA were met. The skewness and kurtosis z-values of the post-test data for the three dependent variables were within acceptable limits (refer to Table 2), indicating that the data distribution was approximately normal. Homogeneity of variance for the dependent variables post-achievement and post-attitude was confirmed by Levene’s test (refer to Table 3). This ensured that there was no variation in the populations from which the samples were taken, allowing us to use ANOVA.
The tables below present the post-test mean and standard deviation of the three groups based on achievement test scores.
Table 6 displays the results of the descriptive statistics for the Post-test for achievement test scores. There were significant differences in the mean scores of the groups among the research groups (M = 19.26 for the JLSICS group, M = 15.18 for the JLS group and M = 13.68 for the CM group). These differences indicate that group JLSICS performed the best on the post-test achievement scores, followed by group JLS, and then group CM.
The results of the achievement test indicated a significant difference among the three group levels: F (2, 141) = 86.167, p < 0.05, η2 = 0.55, as shown in Table 7. This significant finding concludes that the achievement levels of the three groups differ significantly from each other. According to Cohen’s (1992) criteria for evaluating effect size, the difference between the means and the effect size was large (refer to Table 7), indicating a strong and significant association between the variables being studied, emphasising the findings’ applicability and usefulness.
To determine if the uneven variance or sample size was acceptable, post hoc comparisons were conducted to examine the differences between group means using the Tukey HSD test, as presented in Table 8. The results of the test revealed significant differences between the mean scores of students in the CM group (M = 13.68, SD = 1.98, p < 0.05) and the JLSICS group (M = 19.26, SD = 1.92). Similarly, there was also a statistically significant difference observed between the groups that received JLS alone (M = 15.18, SD = 2.55, p = 0.000) and JLSICS (M = 19.26, SD = 1.92). Likewise, the data in Table 8 illustrates a significant difference between students in the JLS alone group (M = 15.18, SD = 2.55) and the CM group (M = 13.68, SD = 1.98, p = . 007). The finding indicates that the JLSICS group’s instructional strategy was superior in improving student learning and accomplishment, as seen by their higher mean scores in comparison to the JLS alone and CM groups.
Figure 2’s bar graph also indicates that the utilization of JLS alone, as well as JLSICS groups, increased the students’ achievement test scores. In contrast, the CG, who received CM instruction, showed minimal improvement. As a result, the students who used the JLSICS strategy outperformed those who used JLS alone and CM on achievement tests (refer to Figure 2).
Analysing treatment effects on attitude post-test scores in students
To investigate whether JLSICS could have a positive impact on students’ attitudes, the researcher used one-way ANOVA to evaluate students’ post-test outcomes on the chemistry attitude test. Before conducting the one-way ANOVA analysis, the researcher assessed the assumptions of normality and homogeneity of variance. The results indicated that the outcome variable was approximately normally distributed (refer to Table 2), and Levene’s Test [F(2, 141) = 1.736, p = 0.180] supported the assumption of equal variances (refer to Table 3). The findings of the one-way ANOVA are summarized, and the descriptive data results are presented in Tables 9, 10 and 11.
The descriptive test results for each of the three student groups’ attitudes are shown in Table 9. In terms of mean test scores for student attitude, the CG performed the lowest (M = 2.45, SD = 0.60), whereas the JLSICS 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.
The results of the one-way ANOVA indicated a statistically significant difference [F (2, 141) = 93.81, p < 0.001, ղ2 = 0.51] in the post-test mean scores of the attitude test among the three groups (refer to Table 10). It is worth noting that according to standards established by Cohen(1992), large effect sizes are associated with statistically significant effects, which denotes a significant or meaningful difference between the groups.
After conducting the ANOVA, three post hoc tests were performed to assess the mean differences between the three groups as indicated in Table 11. The results showed a statistically significant difference between the JLS lone group (mean = 2.96, SD = 0.46, p < 0.001) and the JLSICS group (mean = 3.84, SD = 0.46). Similarly, there was a statistically significant difference between the JLSICS group and the CM group (mean = 2.45, SD = 0.60, p < 0.001), showing the potential effectiveness of JLSICS’s possibly more successful teaching strategy for improving students’ attitudes. Furthermore, Table 11 demonstrates statistically significant differences between the JLS alone and CM groups. Despite JLSICS showing more improvement compared to JLS, the bar graph representing attitude test scores also indicated that students using either the jigsaw learning strategy alone or in conjunction with computer simulations achieved better results (refer to Figure 3). However, the CG, who received instruction through CM, only made limited progress. Therefore, both the JLSICS and JLS groups performed better than the CM group in terms of attitude test scores (refer to Figure 3).
Analysis of the combination and correlation between achievement and attitude post-test scores
The primary objective of this study was to ascertain whether there was a significant difference in the mean scores for achievement and attitude tests between the groups. The researcher used a one-way MANOVA to examine the impact of groups on the entire set of dependent variables. To avoid biasing the type 1 error rate in subsequent post hoc and ANOVA analyses, a preliminary MANOVA was conducted on the means. Before the MANOVA, a Pearson correlation analysis was performed on the dependent variables to test the hypothesis that there would be a moderate level of correlation. Given the strong pattern of correlations (r = 0.51, p = 0.007) observed between the achievement and attitude test scores, a MANOVA was recommended. Multivariate normality was used to compute the Mahalanobis distance and identify outliers. The calculated maximum Mahalanobis distance for the two dependent variables (scores on the achievement and attitude tests) was 7.24, which falls below the critical point (11.82). No unusual combinations of achievement and attitude test scores were found in the distribution of the dependent variables.
After examining the distribution of multivariate normality, it was found that all combinations of the dependent variables and the means of each dependent variable in each group were approximately normally distributed, as demonstrated in Table 4. The p-value of 0.460, or p > 0.005, was associated with the M value of 12.34 from Box’s test and was determined to be not statistically significant (Mertler and Vannatta Reinhart, 2020). It was assumed that the covariance matrices of the groups in the MANOVA analysis were comparable, based on Box’s test, which suggests equal variances. Therefore, Wilk’s lambda was chosen as the test statistic, and the results of the MANOVA analysis can be seen in Tables 12, 13, 14 and 15.
To determine whether there were variations in the combination of achievement and attitude test scores among the three groups, the researcher used a one-way multivariate analysis of variance (MANOVA). The MANOVA effect was deemed statistically significant, as evidenced by Wilk’s Lambda = 0.288, F (2, 141) = 60.336 b, p < 0.001. This significant F value signifies that there are notable distinctions between the intervention groups in terms of a linear combination of the attitude and achievement test outcomes. Table 13 displays that the multivariate partial η2 = 0.46 explains 46% of the multivariate variation in the dependent variables attributed to the group factor. To further analyse the results of the achievement and attitude tests, two one-way ANOVAs were performed following the MANOVA.
The researcher could use a standard Bonferroni technique to test each ANOVA at a significance level of 0.025 (0.05 / 2) to prevent Type I errors. The results of the achievement and attitude tests for the three groups showed significant differences, as indicated by subsequent univariate ANOVAs: F (2,141) = 86.17, p < 0.001, partial ղ2 = 0.55 and F (2,141) = 74.68, p < 0.001, partial ղ2 = 0.51, respectively (refer to Table 15). The JLSICS group had higher levels of achievement (M = 19.2593, SD = 1.92486) and attitude (M = 3.7685, SD = 0.41981) compared to the JLS alone group, which had achievement (M = 15.1837, SD = 2.55501) and attitude (M = 3.0000, SD = 0.47434) scores that were lower, according to the mean score test. In addition, the mean scores for attitude and achievement of the JLS alone group were both higher than those of the CM group (refer to Table 12).
The tests measuring the effect between subjects did not identify the specific group that differed from the others, although they did demonstrate a statistically significant difference in average scores between groups in all combinations of dependent variables. Therefore, post hoc multiple comparisons were conducted to determine if the group was indeed different from the others. To avoid Type I errors, the researcher had previously conducted univariate ANOVAs at an alpha level of 0.025. To maintain consistency with his conclusion, he should also control the probability of making one or more Type I errors when conducting numerous pairwise comparisons for the dependent variables at the same alpha level of 0.025. The Bonferroni procedure was also used to correct for Type I errors in pairwise comparisons of achievement and attitude test results. Each comparison was evaluated at the alpha level for the ANOVAs divided by the number of intervention groups, resulting in an evaluation at an alpha level of 0.0083 (0.025 / 3 = 0.0083).
There was a statistically significant difference in the mean scores of the post-achievement test between each pair of groups (p < 0.0083). Similarly, the posthoc multiple comparison results showed a statistically significant difference in the mean scores of the post-attitude test between every pair of groups (p < 0.0083). The impact was almost consistently linear in each case. According to Cohen’s (1992) parameters, Table 14 indicates that achievement had the largest effect, with an average Cohen’s value of r equal to 0.55, which is a more significant impact. Essentially, the JLSICS group performed better than the JLS alone group in terms of mean learning outcome scores, while the JLS group outperformed the CM group in terms of mean learning result scores (refer to Figure 4).
Discussions
After analysing the results, it was evident that the students in the experimental group (JLSICS) experienced a notable improvement in both their attitude and achievement. The findings also revealed significant differences in the means between the three groups: JLSICS group vs CM group, JLSICS group vs JLS alone group and JLS alone group vs CM group. Therefore, when compared to students using CM, both the JLSICS and JLS methods demonstrated better performance in terms of achievement and attitude test scores. This indicates that JLSICS has a greater impact on enhancing students’ achievement and attitude in comparison to JLS alone and CM. The use of JLSICS likely provided a more interactive, collaborative and engaging learning experience for the students, allowing them to visualize and manipulate concepts related to acid and base, which in turn enhanced their understanding, attitudes and academic achievement.
Moreover, the experimental group showed a notable disparity in their pre-test and post test scores, with the post-test scores being higher. These results reinforce the effectiveness of combining a jigsaw learning strategy with computer simulations in improving the academic achievement and attitude test scores of secondary school students. Moreover, the JLSICS promotes a cooperative environment, where students engage in teamwork to tackle challenges, which probably nurtured a favourable attitude towards the study of chemistry.
The results of this research align with the findings of Gambari (2020), who investigated the impact of computer-supported jigsaw learning strategy on the academic achievement of high school students in chemistry. Gamabri found that students who learned chemistry through a computer-supported jigsaw learning strategy performed better than those who received conventional instruction. Furthermore, the study showed that students who learned chemistry using JLSICS outperformed those who learned it using CM. When taught using JLSICS, students are allowed to actively engage in the learning process, which promotes deeper understanding and peer interaction. As a result, students can acquire insightful knowledge and new perspectives that help them become more adept at solving problems (Bórquez-Sánchez, 2024).
The findings of this research are also consistent with the results of previous studies conducted by Benest (2017) and Riordan et al. (2024) investigated the impact of computer simulations and cooperative learning strategies on students’ achievement and attitudes towards chemistry to the separation of matter. These studies discovered that the combination of computer simulations and cooperative learning strategies led to improvements in students’ achievement and attitudes towards chemistry. The students’ learning experience was probably made more dynamic and interesting by the use of JLSICS, which helped them to visualise and manipulate concepts. Moreover, Eunice (2015) used computer-based instructions (CBI) to assist students in making connections between micro, sub-micro and symbolic representations, aiming to enhance their attitude towards learning chemistry and CBI exhibited more positive attitudes towards learning chemistry than CM. Students’ comprehension of the relationships between micro, sub-micro and symbolic representations in chemistry was aided by this dynamic and interactive learning method, by attracting students’ interest and encouraging involvement, which resulted in a deeper understanding of the subject (Caballero et al., 2024).
In addition, the findings of this research are in line with those of Jamil and Mahmud (2019) and Nortvig et al.(2018), found a strong correlation between academic achievement and students’ attitudes towards science. The results of the study also support the findings of Alodwan and Almosa, (2018) and Costabile et al. (2024), stating that attitude significantly influences student achievement. Likewise, they correspond to Watsisi's (2022) findings, which demonstrated that individuals with higher academic achievement possess more positive attitudes towards mathematics compared to those with lower academic achievement. This shows that combining cutting-edge educational techniques with computer simulations can create a more productive and interesting learning environment, which improves students’ attitudes and academic achievement.
However, the findings of this current study contrast with the results of Lukitasari et al.(2019) and Soares (2015), as both studies concluded that there were no notable disparities in students’ achievement or attitude when comparing CBI to CM. In addition, the present result contradicts the findings of Olakanmi (2018), who determined that there is no beneficial correlation between attitudes and academic achievement in chemistry among secondary school students, indicating that some students perform in chemistry despite having less positive attitudes towards the topic.
Conclusions
This study shows that JLSICS and JLS alone enhance students’ achievements and attitudes towards learning acid and base concepts in grade 10 chemistry. Notably, there were notable differences in achievement and attitudes among the groups, with JLSICS surpassing both CM and JLS alone. This study further illustrates that JLSICS and JLS improve students’ achievement and attitudes towards the teaching and learning of acid and base topics in grade 10 chemistry. Implementing the JLSICS in chemistry classes has the potential to enhance students’ attitudes, interests and academic achievement in the subject. Attitudes and achievement exhibit a positive correlation, with attitudes impacting achievement and achievement influencing attitudes.
Implications
The integration of the jigsaw learning strategy with computer simulations is an instructional approach that focuses on students, equipping them with the necessary chemistry knowledge and skills for the modern era. However, the absence of this approach in instructional interventions highlights the significance of this study in offering both theoretical and practical insights to address this gap. From a policy perspective, this research could serve as a reminder of the importance of incorporating contemporary instructional methods for policymakers at various levels, benefiting the government, society and students alike. By enhancing student achievement, attitudes, retention, problem-solving and critical thinking skills, this approach enables students to take charge of their learning, fostering a deeper understanding and analysis through motivation provided by the jigsaw learning strategy integrated with computer simulations. Moreover, the use of computers to simulate experiments and create teaching resources not only aids in grasping abstract chemistry concepts but also has the potential to reduce the costs associated with purchasing chemicals for schools. In addition, this study could pave the way for similar research in different school environments and educational settings, offering valuable insights for educators and policymakers alike.
Barriers to using technology in the classroom
There are multiple obstacles to the use of technology in classrooms in Ethiopia. These obstacles can be divided into challenges related to infrastructure, limited resources and socio-cultural factors. Here are some common obstacles:
Insufficient Infrastructure: The lack of reliable electricity, internet connectivity and technology infrastructure can hinder the integration of technology in classrooms. Many schools in rural areas of developing countries, including Ethiopia, lack the basic infrastructure required for technology use.
Limited Resources: Developing countries often face limitations in the availability of computers, tablets and other technology devices. Schools may struggle to provide an adequate number of devices for students, making it difficult to effectively implement technology-based learning.
High Costs: The cost associated with acquiring and maintaining technology devices, software and internet connectivity can be a major barrier for schools with limited financial resources. This makes it challenging to invest in and sustain technology initiatives.
Lack of Teacher Training and Support: Many teachers in developing countries, including Ethiopia, may not have received the necessary training and professional development opportunities to effectively integrate technology into their teaching practices. The absence of ongoing technical support and training programs can impede teachers’ ability to use technology effectively.
Limited Content Relevance: The availability of locally relevant educational content and software can be limited. The lack of digital educational resources and applications that align with the local curriculum can hamper the integration of technology in the classroom.
Language Barriers: In countries like Ethiopia, where there are diverse linguistic communities, the lack of educational technology resources in local languages can limit their usefulness and accessibility in the classroom.
Cultural Perceptions and Attitudes: Socio-cultural factors, such as prevailing attitudes towards technology or resistance to change, can influence the adoption of technology in the classroom.
These barriers pose significant challenges to the effective use of technology in Ethiopian classrooms. Addressing these obstacles requires a comprehensive approach that includes improving infrastructure, providing adequate resources, offering teacher training and support, developing localized educational content and addressing cultural perceptions and attitudes towards technology.
Limitations and future research directions
The study only focused on one specific aspect of chemistry (acid and base chemistry), which restricts the applicability of the findings to other chemistry topics or subjects. Subsequent research could explore the effectiveness of JLSICS in different areas of chemistry or other scientific fields. In addition, the study used a quasi-experimental design with a pretest-posttest CG, which may introduce variables that could confound the results and restrict causal inferences. Future research could use a randomized controlled trial design to establish a stronger causal relationship between the intervention and students’ attitudes towards chemistry.
Acknowlegement
The authors would like to thank the teachers and students of Jimma Secondary Schools, Jimma University and Fitche College of Teachers Education for their invaluable contributions in terms of information and resource support.
Declarations.
Ethical Approval: All procedures performed in studies involving human participants followed the ethical standards of institutional and national research committees. Therefore, approval to conduct the research was accepted by the university’s institutional review board, and ethical guidelines were followed in conducting this study.
Informed Consent: All individual participants involved in the study provided informed consent.
Statement Regarding Research Involving Human Participants and/or Animals: This study entailed the involvement of human subjects and was conducted in accordance with ethical standards, which encompassed the principles of informed consent and approval from an ethics committee.
Consent to Participate: Consent was obtained from all individual participants involved in the study after ensuring that they were fully informed. To protect their privacy, participants’ names will not be linked to any publication or presentation that uses the data and research collected. Instead, the authors used codes to identify participants. Disclosure of identifiable information will only occur if required by law or with the written consent of the participant. Participants participated in the study voluntarily and had the option to withdraw at any time.
Consent to Publish: The authors hereby affirm that the participants in the human research have given their consent for the publication of the details in the journal and article.
Funding: This editorial has not received financial support from any funding organizations.
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.
Declaration of Conflicting Interests: The authors declare no conflicting and competing interests.
Availability of Data and Materials: The authors confirm that the results of this study are available in the article and its supplementary material and raw data can be obtained from the corresponding author upon reasonable request.
Public Interest Statement: The research examines the impact of integrating the jigsaw learning strategy with computer simulations (JLSICS) on the academic progress of 10th-grade students in complex chemistry topics like acid and base. The results of the study indicated that instruction based on JLSICS led to a significant improvement in the academic achievement and attitudes of 10th-grade students in Jimma, compared to conventional teaching methods, as observed through a quasi-experimental research design. However, no interaction was found between the type of instruction and gender or achievement levels. This study contributes to the existing knowledge by shifting the focus from teacher-centred instructional methods to student-centred methods to assess academic achievement and attitude as learning effects. In addition, it suggests that the Ethiopian educational system should consider implementing JLSICS instructional methods in secondary school chemistry classrooms and laboratories.
Figure 1.Jigsaw learning strategy implementation setup
Figure 2.The impact of intervention on achievement for JLSICS, JLS and CM groups
Figure 3.The effect of intervention on attitude for JLSICS, JLS and CM groups
Figure 4.Achievement and attitude correlation among JLSICS, JLS and CM
Table 1.
Pre-test-post-test-comparison group quasi-experimental design
| Groups | Pre-test | Treatment | Post-test |
|---|---|---|---|
| Treatment group-1 (JLSICS) | O1 | X1 | O2 |
| Treatment group-2 (JLS alone) | O1 | X2 | O2 |
| Comparison group (CM) | O1 | – | O2 |
Notes: Where: O1 is the pre-test, O2 is the post-test; X is the treatment in that X1 is with the instructional strategy of “JLSICS” and X2 is with “JLS alone”
Table 2.
Three groups of students’ pre-post achievement and attitude assessments using normal distribution analysis
| Dependent variable | Group type | Test of normality | |||||||
|---|---|---|---|---|---|---|---|---|---|
| N | Skewness | SE | z-value | Kurtosis | SE | z-value | Sig. | ||
| Pre-achievement test | JLSICS | 54 | 0.313 | 0.325 | 0.963 | −0.001 | 0.639 | 0.002 | 0.104 |
| JLS | 49 | −0.150 | 0.340 | −0.441 | 0.112 | 0.668 | 0.168 | 0.069 | |
| CM | 41 | −0.051 | 0.369 | −0.138 | −0.397 | 0.724 | −0.548 | 0.113 | |
| Pre-attitude test | JLSICS | 54 | −0.043 | 0.325 | −0.132 | −0.849 | 0.639 | −1.329 | 0.239 |
| JLS | 49 | 0.103 | 0.340 | 0.298 | 0.146 | 0.668 | 0.219 | 0.229 | |
| CM | 41 | 0.044 | 0.369 | 0.119 | −0.624 | 0.724 | −0.862 | 0.831 | |
| Post-achievement test | JLSICS | 54 | −0.120 | 0.325 | −0.369 | −0.484 | 0.639 | −0.757 | 0.138 |
| JLS | 49 | −0.245 | 0.340 | −0.721 | −0.482 | 0.668 | −0.721 | 0.237 | |
| CM | 41 | 0.141 | 0.369 | 0.382 | −0.386 | 0.724 | −0.533 | 0.234 | |
| Post-attitude test | JLSICS | 54 | −0.078 | 0.325 | −0.24 | −0.695 | 0.639 | −1.088 | 0.472 |
| JLS | 49 | −0.105 | 0.340 | −0.003 | 0.284 | 0.668 | 0.425 | 0.737 | |
| CM | 41 | 0.071 | 0.369 | 0.000 | −0.272 | 0.724 | −0.376 | 0.821 | |
Table 3.
Test results for students’ attitudes and achievement across the three groups using Levene’s test of homogeneity of variances
| Dependent variables | Levene’s statistic | df1 | df2 | Sig. |
|---|---|---|---|---|
| Pre-test achievement | 0.521 | 2 | 141 | 0.595 |
| Pre-test attitude | 2.022 | 2 | 141 | 0.136 |
| Post-test achievement | 2.377 | 2 | 141 | 0.100 |
| Post-test attitude | 1.736 | 2 | 141 | 0.180 |
Table 4.
Result of the students’ pre-test results of group achievement and attitudes
| Dependent variable | Group | N | Mean | SD |
|---|---|---|---|---|
| Pre-achievement test | JLSICS | 54 | 4.56 | 1.66 |
| JLS | 49 | 4.35 | 1.45 | |
| CM | 41 | 4.73 | 1.63 | |
| Total | 144 | 4.53 | 1.58 | |
| Pre-attitude test | JLSICS | 54 | 2.35 | 0.72 |
| JLS | 49 | 2.30 | 0.62 | |
| CM | 41 | 2.21 | 0.57 | |
| Total | 144 | 2.29 | 0.64 |
Table 5.
Result of one-way ANOVA of achievement and attitude pre-test scores
| Dependent variables | Source | Sum of squares | Df | MS | F | Sig. |
|---|---|---|---|---|---|---|
| Pre-achievement test | Between groups | 3.342 | 2 | 1.671 | 0.668 | 0.514 |
| Within groups | 352.484 | 141 | 2.500 | |||
| Total | 355.826 | 143 | ||||
| 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 |
Table 6.
Results descriptive statistics of the three groups’ post-test achievement scores
| Group type | Scores for achievement | ||
|---|---|---|---|
| N | M | SD | |
| JLSICS | 54 | 19.26 | 1.92 |
| JLS | 49 | 15.18 | 2.55 |
| CM | 41 | 13.68 | 1.98 |
| Total | 144 | 16.28 | 3.22 |
Table 7.
Results of one-way ANOVA on the posttest achievement means for each of the three groups
| Source | Sum of squares | Df | MS | F | Sig. | ղ2 |
|---|---|---|---|---|---|---|
| Between groups | 814.731 | 2 | 407.366 | 86.167 | 0.000 | 0.55 |
| Within groups | 666.595 | 141 | 4.728 | |||
| Total | 355.826 | 143 |
Table 8.
Tukey HSD of JLSICS, JLS and CM groups’ multiple comparisons on students’ achievement test results
| (I) group of students | (J) group of students | MD (I-J) | SE | Sig. | 95% CI | |
|---|---|---|---|---|---|---|
| Lower |
Upper |
|||||
| JLSICS | CM | 5.57633* | 0.40530 | 0.000 | 4.6095 | 6.5432 |
| JLS | 4.07559* | 0.44926 | 0.000 | 3.0047 | 5.1465 | |
| JLS | CM | 1.50075* | 0.47842 | 0.007 | 0.3601 | 2.6414 |
| JLSICS | −4.07559* | 0.44926 | 0.000 | −5.1465 | −3.0047 | |
| CM | JLS | −1.50075* | 0.47842 | 0.007 | −2.6414 | −0.3601 |
| JLSICS | −5.57633* | 0.40530 | 0.000 | −6.5432 | −4.6095 | |
Note:The mean difference (MD) is statistically siginficant at the P< 0.05 level
Footnote: MD(I-J): Mean difference between group of students(Intervention - control), SE: Standard Error, Sig.: Significance or P- Value, 95% CI: Confidence Interval
Table 9.
Results descriptive statistics of the three groups’ scores on attitude
| Group type | Scores for attitude | ||
|---|---|---|---|
| N | M | SD | |
| JLSICS | 54 | 3.8426 | 0.45602 |
| JLS | 49 | 2.9592 | 0.45913 |
| CM | 41 | 2.4524 | 0.60466 |
| Total | 144 | 3.1462 | 0.76369 |
Table 10.
ANOVA summary table for attitude means across three groups based on test results
| Source | Sum of squares | Df | MS | F | Sig. | ղ2 |
|---|---|---|---|---|---|---|
| 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 11.
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 | |
|---|---|---|---|---|---|---|
| Lower |
Upper |
|||||
| JLSICS | CM | 1.39015* | 0.11300 | 0.000 | 1.1197 | 1.6606 |
| JLS | 0.88341* | 0.09029 | 0.000 | 0.6686 | 1.0982 | |
| JLS | CM | 0.50674* | 0.11498 | 0.000 | 0.2317 | 0.7818 |
| JLSICS | −0.88341* | 0.09029 | 0.000 | −1.0982 | −0.6686 | |
| CM | JLS | −0.50674* | 0.11498 | 0.000 | −0.7818 | −0.2317 |
| JLSICS | −1.39015* | 0.11300 | 0.000 | −1.6606 | −1.1197 | |
Note: *The mean variation is considered statistically significant at the 0.05 level
Table 12.
Group means and standard deviations for attitude and achievement test results
| Group | Achievement test scores | Attitude test score | ||
|---|---|---|---|---|
| M | SD | M | SD | |
| JLSICS | 19.2593 | 1.92486 | 3.7685 | 0.41981 |
| JLS | 15.1837 | 2.55501 | 3.0000 | 0.47434 |
| CM | 13.6829 | 1.98039 | 2.5256 | 0.62231 |
Table 13.
Tests for achievement and attitude multivariate by group disparities in results
| Effect | Wilk’s lambda value | F | Sig. | ղ2 |
|---|---|---|---|---|
| Wilks’ lambda | 0.288 | 60.336b | 0.000 | 0.46 |
Notes:a. Design: Intercept + Group; b. Exact statistic; c. The statistical measure provides a lower constraint on the significance level based on an upper bound on F; d. computed using alpha = 0.025
Table 14.
ANOVA Summary table for individual achievement and attitude test results across groups
| Source | Dependent variable | Type III SS | df | MS | F | Sig | ղ2 |
|---|---|---|---|---|---|---|---|
| Group | Achievement test scores | 814.731 | 2 | 407.366 | 86.167 | 0.000 | 0.550 |
| Attitude test scores | 37.744 | 2 | 18.872 | 74.680 | 0.000 | 0.514 |
Notes:(a) R squared = 0.550 (adjusted R squared = 0.544); (b) R squared = 0.514 (adjusted R squared = 0.508); (c) computed using alpha = 0.025
Table 15.
Post hoc outcomes for combined achievement and attitude test results by group differences
| Dependent variable | (I) group of students | (J) group of students | MD (I-J) | SI | Sig. | 95% CI | |
|---|---|---|---|---|---|---|---|
| Lower limit | Upper limit | ||||||
| Achievement test scores | JLSICS | CM | 5.5763* | 0.45040 | 0.000 | 4.5095 | 6.6432 |
| JLS | 4.0756* | 0.42899 | 0.000 | 3.0594 | 5.0917 | ||
| JLS | CM | 1.5007* | 0.46021 | 0.004 | 0.4106 | 2.5908 | |
| JLSICS | −4.0756* | 0.42899 | 0.000 | −5.0917 | −3.0594 | ||
| CM | JLS | −1.5007* | 0.46021 | 0.004 | −2.5908 | −0.4106 | |
| JLSICS | −5.5763* | 0.45040 | 0.000 | −6.6432 | −4.5095 | ||
| Attitude test scores | JLSICS | CM | 1.2429* | 0.10413 | 0.000 | 0.9963 | 1.4896 |
| JLS | 0.7685* | 0.09918 | 0.000 | 0.5336 | 1.0035 | ||
| JLS | CM | 0.4744* | 0.10640 | 0.000 | 0.2224 | 0.7264 | |
| JLSICS | −0.7685* | 0.09918 | 0.000 | −1.0035 | −0.5336 | ||
| CM | JLS | −0.4744* | 0.10640 | 0.000 | −0.7264 | −0.2224 | |
| JLSICS | −1.2429* | 0.10413 | 0.000 | −1.4896 | −0.9963 | ||
Notes: Based on observed means;
Mean Square (Error) is the error word 0.253;
*The 0.0083 level indicates that the mean difference is substantial
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