Student engagement in the learning process is the key to successful delivery of teaching and learning. Teachers face several challenges to engage learners in different disciplines, including computer science. This research conducts a review of BSc (Computer Science) programmes and introduces interactive activities to enhance learner engagement. The study was conducted using a repeated measure design involving 24 participants. The findings revealed that the use of technology, and collaborative and interactive activities in groups may positively influence learner engagement. The participants’ feedback before and after introduction of group tasks and interactive activities showed a significant (p < 0.001) and increasing trend in response to questions-related learner engagement. The participants agreed that their learning experience and engagement enhanced with the use of technology and interactive and collaborative activities.

With ever-increasing dataset size and data storage capacity, there is a strong need to build systems that can effectively utilize these vast datasets to extract valuable information. Large datasets often exhibit sparsity and pose cold start problems, necessitating the development of responsible recommender systems. Knowledge graphs have utility in responsibly representing information related to recommendation scenarios. However, many studies overlook explicitly encoding contextual information, which is crucial for reducing the bias of multi-layer propagation. Additionally, existing methods stack multiple layers to encode high-order neighbor information while disregarding the relational information between items and entities. This oversight hampers their ability to capture the collaborative signal latent in user-item interactions. This is particularly important in health informatics, where knowledge graphs consist of various entities connected to items through different relations. Ignoring the relational information renders them insufficient for modeling user preferences. This work presents an end-to-end recommendation framework named KGCAN (Knowledge Graph Enhanced Contextualized Attention-Based Network), which explicitly encodes both relational and contextual information of entities to preserve the original entity information. Furthermore, a user-specific attention mechanism is employed to capture personalized recommendations. The proposed model is validated on three benchmark datasets through extensive experiments. The experimental results demonstrate that KGCAN outperforms existing knowledge graph based recommendation models. Additionally, a case study from the healthcare domain is discussed, highlighting the importance of attention mechanisms and high-order connectivity in the responsible recommendation system for health informatics.

Online examinations are an integral component of online learning environments and research studies have identified academic dishonesty as a critical threat to the credibility of such examinations. Academic dishonesty exists in many forms. Collusion is seen as a major security threat, wherein a student invites a third party for help or to impersonate him or her in an online examination. This work aims to investigate the authentication of students using text-based and image-based challenge questions. The study reported in this paper involved 70 online participants from nine countries completing a five week online course and simulating an abuse case scenario. The results of a usability analysis suggested that i) image-based questions are more usable than text-based questions (p < 0.01) and ii) using a more flexible data entry method increased the usability of text-based questions (p < 0.01). An impersonation abuse scenario was simulated to test the influence of sharing with different database sizes. The findings revealed that iii) an increase in the number of questions shared for impersonation increased the success of an impersonation attack and the results showed a significant linear trend (p < 0.01). However, the number of correct answers decreased when the attacker had to memorize and answer the questions in an invigilated online examination or their response to questions was timed. The study also revealed that iv) an increase in the size of challenge question database decreased the success of an impersonation attack (p < 0.01).

Online examinations are an integral component of many online learning environments, which face many security challenges. Collusion is seen as a major security threat to such examinations, when a student invites a third party to impersonate or abet in a test. This work aims to strengthen the authentication of students via the use of dynamic profile questions. The study reported in this paper involved 31 online participants from five countries over a five-week period. The results of usability and security analysis are reported. The dynamic profile questions were more usable than both the text-based and image-based questions (p < 0.01). An impersonation abuse scenario was simulated using email and mobile phone. The impersonation attack via email was not successful, however, students were able to share answers to dynamic profile questions with a third party impersonator in real time, which resulted in 93% correct answers. The sharing of information via phone took place in real time during an online test and the response time of an impersonator was significantly different (p < 0.01) than a student. The study also revealed that a response time factor may be implemented to identify and report impersonation attacks.