An exploratory study of student engagement in gamified online discussions

Chai

Proceedings of the 11th International Conference on Computer Supported Education

et al. 2019

Due to the adaptability and manageability in small-scale class teaching, Small Private Online Courses (SPOCs) have become a highly important learning apparatus in higher education. However, what psychological and social factors affect learning outcomes in SPOCs remains to be explored. This study aims to investigate the effects of proactive personality and social centrality on learning performance in the SPOCs context. On the one hand, we examine the independent effects of proactive personality and social centrality respectively. On the other hand, the combined effect of them is studied to gain a comprehensive understanding of the roles of psychological and social factors in students' SPOCs learning process. Results from correlation analyses indicate that proactive personality and social centralities are significantly correlated with learning performance. Further regression and ANOVA analyses demonstrate the applicability of the two models of indirection and interaction effects respectively.

Section: Discussionmentioning

confidence: 99%

Effects of Proactive Personality and Social Centrality on Learning Performance in SPOCs

Chai

Proceedings of the 11th International Conference on Computer Supported Education

et al. 2019

Computational Intelligence and Neuroscience

“…In recent years, researchers have investigated the effects of academic self-efficacy, teaching presence, and perceived ease of use on student engagement in MOOCs using statistical techniques [ 7 ]. Ding et al [ 48 ] studied the effect of gamification on student engagement in online discussion forums. Wells et al [ 49 ] studied student engagement and performance data using the LMS platform and concluded that student engagement increased as the exam approached.…”

Section: Related Workmentioning

confidence: 99%

Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores

Hussain

Zhu

Zhang

et al. 2018

264

167

Several challenges are associated with e-learning systems, the most significant of which is the lack of student motivation in various course activities and for various course materials. In this study, we used machine learning (ML) algorithms to identify low-engagement students in a social science course at the Open University (OU) to assess the effect of engagement on student performance. The input variables of the study included highest education level, final results, score on the assessment, and the number of clicks on virtual learning environment (VLE) activities, which included dataplus, forumng, glossary, oucollaborate, oucontent, resources, subpages, homepage, and URL during the first course assessment. The output variable was the student level of engagement in the various activities. To predict low-engagement students, we applied several ML algorithms to the dataset. Using these algorithms, trained models were first obtained; then, the accuracy and kappa values of the models were compared. The results demonstrated that the J48, decision tree, JRIP, and gradient-boosted classifiers exhibited better performance in terms of the accuracy, kappa value, and recall compared to the other tested models. Based on these findings, we developed a dashboard to facilitate instructor at the OU. These models can easily be incorporated into VLE systems to help instructors evaluate student engagement during VLE courses with regard to different activities and materials and to provide additional interventions for students in advance of their final exam. Furthermore, this study examined the relationship between student engagement and the course assessment score.

“…Intrinsic motivation refers to 'doing something because it is inherently interesting or enjoyable' (Ryan and Deci 2000: 55), and extrinsic motivation refers to 'doing something because it produces a separable outcome' (Ryan and Deci 2000: 55). Additionally, tangible rewards considerably affect peer discussions (Ding et al 2018). In sum, discussion topics that are engaging and challenging but not too difficult and extrinsic rewards for learners' performance levels may enhance peer dialogue on educational topics.…”

Section: Factors That Affect Peer Dialoguementioning

confidence: 99%

Dialogue-based peer learning of educational topics in a university classroom

2020

Learning and Teaching

This article aims to determine the feasibility of a course on education-related topics based on dialogue-based peer learning (DBPL). According to the literature, the procedures of DBPL are as follows: (1) read texts, (2) formulate individual opinions, (3) express individual opinions in turns, (4) ask and respond to questions, (5) adjust personal opinions. A quantitative survey, an open-ended question on learning perceptions and four paper-and-pencil tests on educational topics were employed to determine the effectiveness of the course for participants’ learning. Most of the participants performed well in the tests and perceived the benefits of the DBPL method for comprehension and for critical thinking on educational topics. The first three steps in the procedure outlined above were identified as key to the results of the study.