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<p>Today’s generation often seeks help from each other in online environments; however, only a few have investigated the role of Internet technologies and the nature of online help-seeking behaviour in collaborative learning environments. This paper presents an educational design research project that examines college students’ online help-seeking behaviour. The context was a large-enrollment science course that implemented a form of blended instruction – the flipped classroom. This paper proposes design guidelines for promoting help-seeking and discusses the application of these principles in the design of a web-based help-seeking tool (EchoLu). The study involved three iterations of implementation to continuously refine the web-based tool, and therefore to better address the help-seeking needs of students in the context. The revisions incorporated between iterations helped improve the embodiment of design principles and led to positive changes in students’ perceptions. The triangulated data revealed students’ interest in information-seeking as an additional form of help-seeking. The results of this study provide insight into the theories that informed the design of EchoLu and the design principles themselves. A new model illustrating processes involved in online help-seeking is discussed, and an emergent principle for online help-seeking is suggested.</p>

Collaborative learning can improve the pedagogical effectiveness of MOOCs.Group formation, an essential step in the design of collaborative learning activities, can be challenging in MOOCs given the scale and the wide variety in such contexts. We discuss the need for considering the behaviours of the students in the course to form groups in MOOC contexts, and propose a grouping approach that employs homogeneity in terms of students' engagement in the course. Two grouping strategies with different degrees of homogeneity are derived from this approach, and their impact to form successful groups is examined in a real MOOC context. The grouping criteria were established using student activity logs (e.g., page-views). The role of the timing of grouping was also examined by carrying out the intervention once in the first and once in the second half of the course. The results indicate that in both interventions, the groups formed with a greater degree of homogeneity had higher rates of taskcompletion and peer interactions, Additionally, students from these groups reported higher levels of satisfaction with their group experiences. On the other hand, a consistent improvement of all indicators was observed in the second intervention, since student engagement becomes more stable later in the course.

With the emergence of MOOCs, there is a growing interest in prediction research. Most existing predictive models do not consider the context for which they are intended, thus resulting in limited impact. Learning design (LD) can provide a contextual understanding for the design of predictive models in collaboration with the instructors, maximizing their potential for supporting learning. This paper presents the findings of a mixed-methods research that explored the potentials emerging from aligning LD and LA during the design of a predictive analytics solution and from involving the instructors in the design process. The context was a past MOOC, where the learner data and the instructors were accessible for posterior analysis and additional data collection. Through a close collaboration with the instructors, the details of the prediction task were identified, including the learning activity to focus on, the target variable to predict, and the practical constraints to consider. Later, two predictive models were built for the prediction task identified: LD-specific model, in which the features were based on the LD and pedagogical intentions, and a generic model, which was based on cumulative features, not informed by the LD. Although the LD-specific predictive model did not outperform the generic one, some features derived from the LD and pedagogical intentions were predictive. The quantity and the power of such features were associated with the degree to which the students acted as guided by the LD and pedagogical intentions. The leading instructor's opinion about the importance of the learning activities in the LD was compared with the results of the feature importance analysis. This comparison helped identify the parts of the LD that need improvement. That is, the results of the LA informed back the LD, where the instructor was a mediator. The implications for improving the LD are discussed.

Despite the advantages of MOOCs, such as the open and free access to education, these courses are criticized for students' lack of motivation and their high dropout rates. Gamification is a technique used to increase student motivation and engagement in smallscale educational contexts. However, the effects of gamification on student engagement have been scarcely explored in MOOC environments, and the findings so far are inconsistent. To address this gap, this research work examines the students' behavior towards earning badges and how it relates to their engagement in a gamified MOOC. According to the results, the behaviors towards badges of the active students were generally positive and significantly correlated with other variables measuring their engagement (e.g., pageviews, submitted tasks, forum posts), although this positive behavior seems to decrease throughout the course. Additionally, students that reported high motivation by badges at the end of the course showed a higher engagement level than those that were not appealed by badges.

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The rise of Artificial Intelligence (AI) technology and its impact on education has been a topic of growing concern in recent years. The new generation AI systems such as chatbots have become more accessible on the Internet and stronger in terms of capabilities. The use of chatbots, particularly ChatGPT, for generating academic essays at schools and colleges has sparked fears among scholars. This study aims to explore the originality of contents produced by one of the most popular AI chatbots, ChatGPT. To this end, two popular plagiarism detection tools were used to evaluate the originality of 50 essays generated by ChatGPT on various topics. Our results manifest that ChatGPT has a great po-tential to generate sophisticated text outputs without being well caught by the plagiarism check software. In other words, ChatGPT can create content on many topics with high originality as if they were written by someone. These findings align with the recent concerns about students using chatbots for an easy shortcut to success with minimal or no effort. Moreover, ChatGPT was asked to verify if the essays were generated by itself, as an additional measure of plagiarism check, and it showed superior performance compared to the tradi-tional plagiarism-detection tools. The paper discusses the need for institutions to consider appropriate measures to mitigate potential plagiarism issues and advise on the ongoing debate surrounding the impact of AI technology on education. Further implications are discussed in the paper.

Abstract-In this paper, a model for predicting students' performance levels is proposed which employs three machine learning algorithms: instance-based learning Classifier, Decision Tree and Naïve Bayes. In addition, three decision schemes were used to combine results of the machine learning techniques in different ways to investigate if better classification performance could be achieved. The experiment consists of two phases that are testing and training. These phases are conducted at three steps which correspond to different stages in the semester. At each step the number of attributes in the dataset has been increased and all attributes were included at final stage. The important characteristic of the dataset was that it only contains time-varying attributes rather than time-invariant attributes such as gender or age. This type of dataset has helped to learn to what extend time-invariant data has significant effect on prediction accuracy. The experiment results were evaluated in terms of overall accuracy, sensitivity and precision. Results are discussed compared to results reported in the relevant literature.