Using case method to enrich students’ learning outcomes

Abstract

There is a need to examine the effect of using discussion cases to enrich students’ learning outcomes. A research framework was created to study this multidimensional relationship, via the instruments of interactivity, students’ time devotion and students’ engagement in order to find which factors could improve learning outcomes, including positive group interaction and individual learning performance. The findings from three cohorts of undergraduate students of the same course indicated that interactivity with peers and with the lecturers during the case discussion in classes improved emotional engagement, which in turn positively influenced positive group interaction and individual learning performance. The study also found that students’ emotional engagement was a significant factor in enriching outcomes. Although there was a lack of direct impact of interactivity on learning outcomes, there were many reasons attributed to it. The time that students devoted to the task, as a result of interactivity with the lecturer, was a significant predictor of emotional academic engagement, but it did not predict desirable learning outcomes.

Keywords

case method collaborative learning interactivity learning outcomes student engagement time case studies discussion cases

The use of discussion cases

To cope up with increasing demand from businesses of university graduates, there has been extensive research in the last few decades on effective teaching and learning methods, among which cooperative learning has come in to play. Therefore, classroom teamwork has been advocated by many as an effective way to equip fresh graduates with the capabilities of working in a team, communicating effectively, thinking critically and solving open-ended problems (Colbeck et al., 2000). As a form of active learning which aims to promote meaningful engagement of students in their courses and their classes (Hermann, 2013), cooperative learning adds value to traditional lectures, in that students are offered with chances to ‘reflect, evaluate, analyse, synthesize, and communicate on or about information presented’ (Machemer and Crawford, 2007). Through active participation in group activities, students are motivated to prepare for discussion sessions by completing the required readings and remain focused during the lectorials – a combination of lectures and tutorials (Cavanagh, 2011). In general, organizing students into smaller cooperative groups helps them to build and extend their conceptual understanding of the lecture materials, to develop a shared mental model, to receive support, encouragement and feedback from their group mates as well as to emulate outstanding group members (Johnson et al., 2007). It is more likely in this environment for students to internalize, understand and remember materials through active engagement in the learning process (Bonwell and Sutherland, 1996). In addition, the adaption of cooperative learning results in higher academic achievement than competitive and individualistic structures as well as persistence in college and positive attitude about learning (Colbeck et al., 2000; Hermann, 2013). Other advantages of cooperative learning include ‘promoting greater productivity, generating new ideas or creative solutions, and increased student ability of social perspective taking’ (Machemer and Crawford, 2007).

However, the extensive literature on small group learning also raises some criticism. For instance, cooperative learning does not have an impact on students’ motives but only on their learning behaviours, which means that it only ‘affects the doing without affecting the wanting’ (Hermann, 2013). More importantly, there are differences between teachers and students with regard to how they view the benefits of small group learning. Despite a common positive belief of teachers, students only value most what they perceive to be relevant in improving their performance in final examinations; they often have a bad experience with too large a group, too many class presentations or irrelevant tasks to the course (Hillyard et al., 2010). The skills and preferences of teachers in designing group activities are far more critical than the provision of cooperative learning opportunities alone (Van Dijk et al., 1999).

There are three broad, interrelated theoretical perspectives on how small group learning impacts academic achievement, including the motivational perspective, which emphasizes the importance of individual accountability where students encourage and help each other to achieve group goals; the affective perspective, which focuses on promoting instructor role as a facilitator of interaction among students; and the cognitive perspective which values the opportunities for students to discuss, debate and present their ideas as well as hear the perspectives of others (Springer et al., 1999). These perspectives all have a general point, in that they pay particular attention to student-centred learning where student responsibility and activity rather than teacher control and coverage of academic content are their core emphasis. The potential benefits of the student-centred approach are improving student motivation to learn, knowledge retention, depth of understanding and appreciation of the subject (Lea et al., 2003).

Drawing on the foundation of student-centred learning approach, the use of discussion cases aims to tie the course to the real world, the student’s future career and personal lives (Floyd et al., 2009). Using discussion cases, students are presented with stories and are put in the role of decision makers. A case ranges from a simple narrative to more complex, detailed reports of a real-world situation or problem, which is designed for students to evaluate, conceptualize, discuss, apply and solidify concepts and theories learnt in class (Kunselman and Johnson, 2004). It fosters a meaningful and active learning environment through which students can enhance their critical thinking skills (Yalçınkaya et al., 2012), learn how to listen to classmates and how to express their own ideas (Carlson and Schodt, 1995). An advantage of using discussion cases is that it releases students from memorization or passive learning, and focuses on their self-discovery to form their own perspective as well as on their collaboration to be exposed to other perspectives, which plays an important role in clarification and consolidation of ideas (Bennett, 2010).

The core philosophy of discussion cases lies in its promotion of interactivity. Interactivity, in turn, is framed within social interdependence theory which studies how individuals interact in a particular situation to affect outcomes (Hermann, 2013). According to this theory, when students share similar goals or when their individual goals are positively dependent on group’s goals, they tend to enhance promotive interaction by giving each other feedback, reasoning, different points of views with the aim of helping each other to reach the goals (Johnson and Johnson, 1989). In the context of this pedagogy, there are two types of interactivities: (1) interactivity with peers (IWP) through peer discussions, instructions and participation in the classroom and (2) interactivity with lecturers (IWL) through feedback, assessment of students’ understanding of course concepts, identification of students’ problems and formation of relationships. Both types of interactivities increase students’ engagement and active collaborative learning, which, then, contributes to learning performance (Blasco-Arcas et al., 2013; Havnes, 2008; Kember and Leung, 2005; Kettle, 2011; Ullah and Wilson, 2007; Zepke and Leach, 2010). Interactivity has three central elements, including user control (on the content or tools of the media), direction of communication (two-way communication, responsiveness, exchange) and time (timely feedback, speed of communication), which overlap and interact with each other to create a multidimensional construct of interactivity (McMillan and Hwang, 2002). In discussion cases, these elements of interactivity are reflected in such a way that students have more active control in their learning process when performing the roles of analysts and problem solvers, who would deliver two-way communication among each other as well as between them and the faculty in an immediate manner in order to arrive at the final outcome (King, 1992).

Discussion cases are found to fulfil three dimensions of interactivity, and foster learning outcomes and interactions in many ways. It is hypothesized that both forms of interactivity as a result of using discussion cases will positively predict learning performance and group interactions, directly and via the mediators of student time and engagement discussed below.

Hypothesis 1a/1b: IWP/IWL as a result of using discussion cases increases students’ positive group interaction (PGI).

Hypothesis 2a/2b: IWP/IWL as a result of using pedagogical discussion cases increases students’ individual learning performance.

Interactivity and student time

There is a proportion of class time that is devoted to (1) lecture, (2) class discussion and (3) group work, with the latter two playing an important role in improving engagement between students, course and faculty (Gasiewski et al., 2012). In order to effectively benefit from discussion cases, students have to actively interact with their peers and teachers solving complex and detailed problems with full of real-world nuances and unpredictability. Detailed and directive assignments as well as high cognitive level of learning activities, as part of the requirements when solving pedagogical cases, are found to increase student studying time (Masui et al., 2012). In some cases, the use of social media such as Twitter was found to extend discussions beyond the class, maximizing time on task and leading to more engagement due to its interactive aspect (prompt feedback, two-way conversations), increasing students’ sense of connection and academic engagement (Junco et al., 2011). A similar interactive environment can also be found in the application of discussion cases, which is also expected to provide the same rewards. Therefore, it is hypothesized that the high level of interactivity resulting from discussions and group work in the discussion cases will increase the time that students devote to the task.

Hypothesis 3a/3b: IWP/IWL as a result of using discussion cases increases the time that students devote to this.

Time, emotional academic engagement, PGI and learning performance

The time and energy students devote to educationally purposeful activities are critical features of student engagement (Kuh et al., 2008). Students who perceive their learning experiences to be authentic reported feeling more engaged. There are five characteristics of authentic learning experience: (1) personal relevance to students; (2) be situated in context that best represents ‘real-life’ settings; (3) provide autonomous and independent learning, scaffold students through the ways of thinking and practising the subject area; (4) use of open-ended, complex cases; and (5) treat students as valid participants of knowledge construction (McCune, 2009). Discussion cases fulfil these requirements as an authentic learning experience.

Students who spent more time with tutors both in class and out of class reported significantly higher levels of engagement (Gasiewski et al., 2012). The amount of time spent on studying was a strong predictor of performance on learning outcome regarding knowledge, while using an example strongly predicted performance on learning outcome regarding application (Fisher and Ford, 1998). Additionally, time devoted to study-related activities was positively correlated with grades for almost all students, and therefore had a significant effect on academic performance (Grave, 2011; Masui et al., 2012; Nonis and Hudson, 2006). In the use of discussion cases, students spend time working on complex practical problems and feel engaged due to the narrative nature of the case as well as the collaborative efforts they feel in discussion. The more time they spend on a case, either individually or collaboratively with others, the more likely students become emotionally engaged which subsequently fosters PGIs (Khosa and Volet, 2013; Linnenbrink-Garcia et al., 2011), and hence impact their learning performance. But student time on task might not be the only factor; GPA could improve with hours, but it depends on the study approach (Kember et al., 1996). Based on this, it is hypothesized that the time that students devote to discussion cases increases their emotional academic engagement, PGI and positive learning performances.

Hypothesis 4: Time spent on discussion cases increases students’ emotional academic engagement.

Hypothesis 5a/5b: Time spent on discussion cases has a positive effect on their group interaction/individual performance.

Interactivity and emotional engagement (EAE)

EAE refers to (1) feeling excited about learning new concepts, (2) feeling the collaboration among other students, (3) feeling their hard work is reflected in the grades and (4) feeling motivated to try hard on assignments and examinations (Gasiewski et al., 2012). In the same study, students who were provided a collaborative and active learning environment were found to be excited about learning new concepts and were more likely to report significantly higher levels of engagement. Additionally, an engaging professor and peer interactions played an important role in improving engagement and interest in class (Honkimaki et al., 2004; Moran and Gonyea, 2003), while engaged students sought more interactions with lecturers and tutors outside class time. This shows a cyclical relationship between interactivity and engagement: interactivity breeds engagement, and engaged students will do more to seek interactions.

Course-related interactions were found to be positively related with student engagement (Umbach and Wawrzynski, 2005). With its focus on promoting the interaction among students, discussion cases via case knowledge and case perception significantly increased students’ EAE (Nkhoma et al., 2014). Once students immerse into the world of a case, they are more likely to get excited, motivated and emotionally engaged. Discussion cases create an environment where students benefit from active involvement with their learning, and lecturers have a renewed interest in course material, and consequently both sides achieve a higher level of enthusiasm (Kunselman and Johnson, 2004). Cooperative learning and active engagement help students to improve their understanding of course content and maintain their interests during the sessions (Cavanagh, 2011). However, student effort was in fact the strong predictor of learning, and faculty interactions served to boost that effort (Kunselman and Johnson, 2004). Some students might not learn from group interactions when members do not describe their work in detail, or their learning progress is not synchronized (Webb, 1989). Faculty–student interaction encourages students to devote their greater efforts to educational activities (Kuh and Hu, 2001). Due to mixed results, in this study, it is hypothesized that both types of interactivities, whether with peers or with lecturers, will increase emotional academic engagement.

Hypothesis 6a/6b: IWP/IWL as a result of using discussion cases increases students’ EAE.

Emotionally engaged students are likely to be excited about learning new concepts, enthusiastic about the collaboration with other students and motivated to get involved and contribute, so they have a positive state of mind. Students’ positive affect such as happiness or excitement fostered PGIs (Linnenbrink-Garcia et al., 2011). It is hypothesized that emotional academic engagement will have a positive effect on group interactions.

Engagement and learning performance

It has been established that engagement significantly and positively predicted performance (Blasco-Arcas et al., 2013; Carini et al., 2006; Kunselman and Johnson, 2004; Popil, 2011). There are positive correlations between the four forms of engagement (skills engagement, EAE, participation/interaction engagement, performance engagement) and learning performance (Handelsman et al., 2005). Performance engagement was linked to external learning outcomes such as better grades, while EAE was found to enhance intrinsic outcomes, such as feeling engaged in class or having learning orientation as opposed to performance orientation. Discussion cases, through open and interactive discussions, were among the most effective methods to encourage critical thinking in business education (McEwen, 1994). On the other hand, a study found an insignificant relationship between learning engagement and learning outcomes (both group and individual outcomes) since the discussion and analysis were not graded (Nkhoma et al., 2014). Therefore, due to mixed results, it would be worthwhile to examine the effect of EAE on learning outcomes.

Hypothesis 7a/7b: Students’ EAE as a result of using discussion cases has a positive effect on group interaction/individual performance.

The relationship between affect and PGIs is cyclical (Linnenbrink-Garcia et al., 2011). Positive affect creates PGIs and vice versa. They make students feel excited about learning, more engaged, and consequently impacts performance (Blasco-Arcas et al., 2013; Gasiewski et al., 2012). Also, PGIs encourage students to have more fun in groups and participate actively, increasing their participation/interaction engagement, which was a significant predictor of final examination grades (Handelsman et al., 2005). Working in groups helps students realize that their learning is meaningful. Group discussions help them explore, discuss, debate about concepts and expand their learning beyond the classroom, and apply them into real-world contexts (Xiao et al., 2008). Therefore, it is hypothesized that PGIs impact learning performance in general.

Hypothesis 8: PGI as a result of using discussion cases impacts individual learning performance.

There is a need to examine the influence of IWP and IWL in the use of discussion cases on individual learning performance and PGIs via student time and their EAE. The research framework as shown in Figure 1 is proposed.

Figure 1.

Research model.

Materials and methods

Students were encouraged to answer a web-based questionnaire at the end of their Information Systems course in an international university in Vietnam. Participation was voluntary, anonymous and unrelated to their assessment grades. Students had the right to opt in or out, even if they did not complete the questionnaire at all. The questionnaire took approximately 5–10 minutes to fill in. Three hundred questionnaires were sent out to three cohorts of students in the 2014 academic year who were mainly Vietnamese with 3% international students, which included Australian, Korean and Germany. One hundred and fifty-four valid questionnaires were received with the effective response rate of almost 40%.

The research model was assessed using partial least squares (PLS) technique. SmartPLS 2.0 (Ringle et al., 2005) was used to assess the research model. PLS is a least-squares regression-based technique that can analyse structural models with multiple-item constructs, and direct, indirect and mediating paths. PLS provides all the necessary output to assess the measurement and structural models, including loadings between items and constructs, standardized regression coefficients between constructs (path coefficients), R² values for dependent constructs. Bootstrapping procedure with the resample of 200 was applied to provide the standard error and the t-statistics of the path coefficients. PLS was used to assess the research model since the aim was to assess the following four relationships specifically:

the impacts of IWP and IWL on student report of time devoted to task (STD);

the impacts of IWP, IWL and STD on EAE;

the impacts of IWP, IWL, STD and EAE on the PGI; and

the impacts of IWP, IWL, STD, EAE and PGI on the individual performance.

PLS is considered as a robust estimation method with respect to the distributional assumptions regarding the underlying data and tests of normality.

First, the measurement model in PLS is assessed in terms of item loadings, internal consistency and discriminant validity. For construct validity, item loadings and internal consistencies greater than 0.7 (in some cases, 0.5 for item loadings) are considered adequate (Fornell and Larcker, 1981; Hair et al., 2010). For discriminant validity, item loadings on their own construct should be higher than those on other constructs, and the average variance shared between each construct and its measures should be greater than the average variance shared between the construct and other construct (the squared root of average variance extracted (AVE) of each construct is greater than all the correlation coefficients with other constructs).

Second, structural model and hypotheses are tested by examining the standardized path coefficients. The explained variance in the dependent constructs (R² values) is assessed as an indication of the overall predictive power of the model.

Results

The research model was estimated and tested using PLS, a structural equation modeling technique. SmartPLS Version 2.0 (Ringle et al., 2005) was used for estimating the model, and bootstrapping resampling method (200 resamples) was used to obtain the t-statistics for path coefficient hypothesis testing.

Measurement model

Before the structural model is estimated, a measurement model is checked for reliability and validity. The test of a measurement model includes the estimation of internal consistency and the convergent and discriminant validity of the instrument items. Cronbach’s alpha coefficient is used to examine the reliability of survey instrument. The value of alpha ranges from 0 (unreliable) to 1 (perfectly reliable). A value greater than 0.7 is optimum. However, a value greater than 0.5 is acceptable, but that lower than 0.35 must be rejected (Hair et al., 2006). A table, available from the authors, lists the survey scales and their internal consistency reliabilities. All constructs have Cronbach’s alphas greater than 0.7 that show high reliability of the constructs in the model. Convergent validity is adequate when constructs have an AVE of at least 0.5. For discriminant validity, the square root of AVE for each construct should be greater than the correlation coefficients between the particular constructs and any other construct (Chin, 1998). A table listing the correlations of the latent variables and the square root of AVE on the diagonal is available from the authors. In all cases, the square root of AVE for each construct is larger than 0.5 and larger than all the correlation coefficients with other constructs. This shows a high convergent validity of the constructs in the model. Construct validity was further examined by using factor-loading analysis. Items with factor loadings below 0.3 among all factors are to be deleted. The matrix of loadings and cross loadings of the remaining items is presented in a table available from the authors. All factor loadings are greater than 0.3. As a result, there is no item to be deleted from the model.

Structural model

The estimation of the structural model includes the estimation of the path coefficients and the R² values. Path coefficients indicate the impacts of the independent variables on the dependent variable, while R² values represent the amount of variance explained by the independent variables or the overall explanatory power of the model. Together, the R² and the path coefficients (loadings and significance) indicate how well the data support the hypothesized model. The path coefficients from the PLS analysis are shown in Figure 2.

Figure 2.

Structural model results.

Bootstrapping resampling method (with the resamples of 200) was used to generate the standard errors and the t-statistics, and a table available from the authors presents the model path coefficients and the t-statistics of estimated coefficients. Overall, the estimated model explained for about 17.9%, 38.4%, 34.8% and 45.6% of the variance in student’s report of time, EAE, PGI and individual performance, respectively. This is considered as relatively good explanatory power of the estimated model, especially for the individual performance, which is the most important research question in the study.

At the significant level of 5%, according to the results, for the first relationship defined above, interaction with peers and interaction with lecturers significantly impacted STD. In the second relationship, interaction with peers, interaction with lecturers and student report of time spent were significant and positively influenced EAE. In the third relationship, the impacts of interaction with peers, interaction with lecturers and student report of time spent on PGI were not statistically significant. Only EAE was statistically significant affecting the PGI. Finally, in the fourth relationship, EAE and PGI, these significantly influenced individual performance. The remaining three factors, including interaction with lecturers, interaction with peers and student report of time, did not significantly affect individual performance.

The total effects

The total effects of the independent variables on the model-dependent variables are presented in tables available from the authors. The total effect of other variables on the student report of time includes the direct effects from interaction with peers (0.019) and interaction with lecturers (0.410). The total effect of other variables on the EAE includes the direct effects from interaction with peers (0.343), interaction with lecturers (0.327) and student report of time (0.155), and the indirect effect from interaction with peers (0.343 × 0.155) and interaction with lecturers (0.327 × 0.155) through student report of time. The total effect of other variables on the PGI includes the direct effects from interaction with peers (0.160) and interaction with lecturers (−0.026) while the indirect effect from interaction with peers through student report of time (0.019 × –0.137) and through EAE (0.341 × 0.540) and interaction with lecturers (0.327 × 0.155) through student report of time, as well as, the indirect effect from interaction with lecturers through student report of time (0.410 × –0.137) and through EAE (0.263 × 0.540).

The total effect of other variables on the individual learning performance includes the direct effects from interaction with peers (−0.082), interaction with lecturers (0.170), PGI (0.271), EAE (0.446) and student report of time (−0.058). The indirect effect includes that from interaction with peers through PGI (0.160 × 0.271), through student report of time (0.019 × –0.058), through student report of time and then EAE (0.019 × 0.155 × 0.446), through student report of time and then PGI (0.019 × –0.137 × 0.271), through student report of time then EAE, and then PGI (0.019 × 0.155 × 0.540 × 0.271), through EAE (0.341 × 0.446), through EAE and then PGI (0.341 × 0.540 × 0.271).

The indirect effect includes that from IWL through EAE (0.263 × 0.446), through EAE and then PGI (0.263 × 0.540 × 0.271), through student report of time (0.411 × –0.058), through student report of time and then EAE (0.411 × 0.155 × 0.446), through student report of time and then PGI (0.411 × –0.137 × 0.271), through PGI (−0.026 × 0.271). The hypothesis testing results are presented in Table 1.

Table 1.

Results of hypothesis testing.

Hypotheses	Description	Result
H1a (IWP → PGI)	IWP as a result of using discussion cases increases students’ PGI.	Not supported
H1b (IWP → ILP)	IWP as a result of using discussion cases increases students’ individual performance.	Not supported
H2a (IWL → PGI)	IWL as a result of using discussion cases increases students’ PGI.	Not supported
H2b (IWL → ILP)	IWL as a result of using discussion cases increases students’ individual performance.	Not supported
H3a (IWP → STD)	IWP as a result of using discussion cases increases student time spent.	Not supported
H3b (IWL → STD)	IWL as a result of using discussion cases increases student time spent.	Supported
H4 (STD → EAE)	Student time spent on discussion cases increases their EAE.	Supported
H5a (STD → PGI)	Student time spent on discussion cases has a positive effect on their group interaction.	Not supported
H5b (STD → ILP)	Student time spent on discussion cases has a positive effect on individual performance.	Not supported
H6a (IWP → EAE)	IWP as a result of using discussion cases increases students’ EAE.	Supported
H6b (IWL → EAE)	IWL as a result of using discussion cases increases students’ EAE.	Supported
H7a (EAE → PGI)	Students’ EAE as a result of using discussion cases has a positive effect on group interaction.	Supported
H7b (EAE → ILP)	Students’ EAE as a result of using discussion cases has a positive effect on individual performance.	Supported
H8 (PGI → ILP)	PGI as a result of using discussion cases improves individual performance.	Supported

IWP: interactivity with peers; IWL: interactivity with the lecturer; PGI: positive group interaction; STD: student report of time devoted to task; EAE: emotional engagement.

Discussion

The impact of IWP, IWL and student time on EAE

EAE stands out from the research as a significant element with the greatest impact. Both IWP and IWL as a result of using the discussion cases increased EAE. This finding is consistent with research as discussed in the literature where IWP and IWL create a meaningful and engaging learning environment. Interaction is especially beneficial to students with motivational problems. It is recommended to use discussion cases in courses considered ‘dull’ or difficult by students (Honkimaki et al., 2004). At the same time, student time devoted to the task was another predictor of EAE.

The impact of IWP and IWL on student time

The study found that IWL could increase student report of time spent, but IWP could not. This is new information and is partially in line with the study by Gasiewski et al. (2012), in which the time that faculty devoted to discussion or group work was found to increase engagement. A possible explanation for the discrepancy is that IWP leaves more room for procrastination, which could be a hindrance to student report of time spent. IWL, on the other hand, was able to keep students on track in discussions.

The impact of IWP, IWL, students’ time devoted and EAE on desirable learning outcomes

No significant impacts of IWP and IWL on PGI were found. Interactions alone are not powerful enough to create PGIs and improved performance. This differs from a previous study by Driver (2002) who found that interactions between learners could positively affect perceptions of overall class interactions and satisfaction, which could act as a facilitator of PGI.

A direct impact of IWP and IWL on individual performance could not be found either, and the relationship between interactions and performance in the case method is a complex and multidimensional one. Many factors such as student engagement, student affect and learning approaches can influence this relationship. Another possibility is that some of the peer interactions might not be educationally relevant as students give in to procrastination. This implies that IWL might not have ignited the necessary efforts on students’ part to make for improved performance. Also, the participants were likely to refer to IWL inside the classroom only, which might not have been enough to create a difference in outcomes. There was no significant impact of student time on predicting PGI or individual performance either, as other literature has also suggested. A possible explanation could be that the way students spend time, not just the amount of time, is more predictive of successful outcomes.

Factors affecting individual performance

The positive influence of PGI on individual performance has also been seen in studies discussed in the literature. Also, factors such as detailed instruction in terms of case discussion and analysis, a grading system and acknowledgement of students’ cognitive and personality traits can also contribute to individual learning outcomes.

There are, however, limitations to this study. The participants were undergraduates who are new to the tertiary environment. Novice students are less likely to participate fully in the use of case studies because they might not have the real-life working experience to contribute to fruitful discussions. It is much easier to engage in interactive teaching methods with part-time students who have full-time employment (Kember and Leung, 2005). Age and work experience make them more mature and confident to interact with others. Also, Asian students might express reticence to join class discussions. There are many barriers that Asian students face in class discussions, such as the language barrier, fear of losing face, preference for small groups instead of big class, reliance on others and passive habits from school (Jackson, 2002). They want the condition to be ‘right’ to speak up, for example, using localized cases or a supportive, slow-paced environment. Therefore, it was entirely possible that the participants experienced the same barriers interacting in class, leading to insignificant impacts of IWP and IWL on learning outcomes. Some students find the experience of working with their peers embarrassing, scary and difficult (Keville et al., 2013), while some other students might want to maintain harmony; hence, they are willing to trade-off their learning outcomes with social goals (Robinson et al., 2015). Another limitation could be the time spent in student work groups or tutorial, which is negatively correlated with grades if students have below-average ability. In this study, students were given the case to work in groups for analysis a week before the class, and then discuss it together in class in a 2-hour tutorial. This way, student time is divided into two steps. Therefore, it might be confusing to some students, who might have regarded their time to be in-class only. Also, student in work groups and tutorials in this study might have had an adverse effect on IWP if some of the participants have below-average ability. Future research regarding the lack of direct impact of IWP and IWL on desirable outcomes is worth further elaboration. In addition, due to the contrasting results with previous studies, more investigation is needed to explore if interaction alone is powerful enough to create PGIs and improved performance. It is also worthy to note that future studies should examine the quality of peer interactions more closely in order to provide evidence on the impact of IWP on PGI.

This study shed light on the multidimensionality and complexity of the relationship between interactivity in the case method and how it can impact performance, the most important question. From the findings, it is clear that interactivity alone, whether it is between peers or between faculty–student, cannot produce desirable outcomes such as PGIs or improved individual performance. EAE plays a very important role in making these outcomes happen. In order for EAE to occur, there needs to be a high level of instrumental and relevant interactivity between peers and between faculty–student. Student time is also an important area that can be enhanced by the right type and quality of interactivity, which can in turn enhance EAE. The rewards that EAE can bring are worthwhile. Both PGIs and improved individual performance will result from EAE. Students would feel more positive in their learning with others, have better learning orientation and a more critical grasp of concepts, and ultimately may perform academically better.

The findings also show that interactions in the use of case studies have the potential to increase desirable learning outcomes. However, the type and the quality of interactions need to be examined more closely in order to have a better picture as to how and why it can improve group interactions and individual performance. The study also points to an interesting fact that IWL seems to produce more fruitful results, which raises the interesting question about the role of the lecturer in discussion cases. Is the lecturer a facilitator of discussions, a counsellor or a conductor who can help students devote time and energy to the right place at the right time? It also touched on a few barriers that might hinder success in the use of case studies. Procrastination, reluctance to participate in discussions, lack of preparation and language barriers are all worth further research in order to understand better about their impact and learn how to manage them for better learning outcomes.

Footnotes

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author biographies

Mathews Nkhoma is Head of Department Business Information Technology and Logistics at RMIT University Vietnam. His major topics of research are zero-day attacks and vulnerability research, ethical hacking, network security management, forensic computing and evidence recovery, cybercrime, identity theft, consumer protection, trust and confidence, and impact of information communication technology (ICT) in education. Address: Department of Business IT and Logistics, RMIT University Vietnam, 702 Nguyen Van Linh, District 7, Ho Chi Minh City, Vietnam. [email: mathews.nkhoma@rmit.edu.vn]

Narumon Sriratanaviriyakul is Senior Lecturer in Business Information Technology and Logistics at RMIT University Vietnam. Her major topics of research are case teaching methodology, game-based learning, knowledge management, cyberbullying and online advertising. Address: Department of Business IT and Logistics, RMIT University Vietnam, 702 Nguyen Van Linh, District 7, Ho Chi Minh City, Vietnam. [email: narumon@rmit.edu.vn]

Huy Le Quang is Research Assistant at RMIT University Vietnam. His major topics of research are economics of education, active learning, corporate finance, capital market anomalies and quantitative methods. Address: Asia Graduate Centre, RMIT University Vietnam, 702 Nguyen Van Linh, District 7, Ho Chi Minh City, Vietnam. [email: huy.lequang@rmit.edu.vn]

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