Journal of Computing in Higher Education

Gamification is a design strategy that can support higher education students’ autonomous motivation by targeting the basic psychological needs. Yet, to date, the field lacks a comprehensive overview of how this can be achieved. The present scoping review aimed to uncover gamification design strategies that influence higher education students’ basic psychological need support. Following a hybrid search strategy, 20 studies were identified based on 897 entries in Scopus and subsequent backward and forward snowballing. The studies had designed gamification to support higher education students’ basic psychological needs and provided a rationale for how the needs were affected by the intervention. By conducting a basic content analysis, we identified four educational gamification design strategies to support autonomy, three to support competence, and two to support relatedness. The overall results highlight the importance of considering how individual game elements are designed and integrated into learning contexts and understanding how gamification design strategies concurrently affect different students and basic psychological needs.

This research examined students' awareness of ethical issues that may arise while using generative artificial intelligence (GAI) and their behavioral profiles regarding these awarenesses. In order to examine students' awareness of artificial intelligence ethics, the phenomenological approach, which is among the qualitative research methods, was used as a basis. 137 undergraduate students participated in the study. An open-ended survey was prepared in order to determine students' awareness, behaviors, and profiles regarding the ethical use of artificial intelligence tools. In the research, the ethics of artificial intelligence were examined comprehensively under the titles Privacy/Protection of Personal Data, Accuracy/Reliability/Impartiality, Copyright/Intellectual and Industrial Property Rights, Inequality of Opportunity/Injustice, Ethical Compliance, Social Manipulation/Persuasion, and Responsibility for Content Usage. The data obtained within the scope of the research was analyzed using descriptive analysis. Accordingly, although the majority of students think that artificial intelligence does not protect their privacy and personal data, they continue to use it by sharing their information. However, there are also students who prefer to use the content produced by GAI directly and think that GAI is neutral.

The increasing integration of Generative Artificial Intelligence (GAI) in higher education has created new opportunities for enhancing learning visibility and instructional transparency. Yet, AI-assisted learning processes often remain opaque, leaving educators uncertain about how AI shapes students’ reasoning, creativity, and ethical awareness. This study seeks to advance visible learning by designing a transparency-oriented AI-supported environment that systematically captures student–AI interaction trajectories within a project-based course. Drawing on Cognitive Load Theory (CLT), Creative Self-Beliefs (CSB), and Responsible AI (RAI) frameworks, the study examines how structured GAI support and perceived transparency relate to learners’ perceived cognitive load and creative self-beliefs. Using behavioral learning analytics and Partial Least Squares Structural Equation Modeling (PLS-SEM), results indicate that GAI support is positively associated with perceived cognitive load reduction both directly and indirectly through strengthened CSB. Furthermore, AI transparency moderates these relationships, such that structured and explainable AI interactions amplify learners’ reflective engagement. The findings extend CLT to complex decision-making contexts, position CSB as a psychological mechanism in AI-supported learning, and conceptualize Responsible AI as a pedagogical design condition rather than merely an ethical add-on. Practical implications are provided for educators seeking to balance cognitive support, creative empowerment, and accountability in AI-integrated higher education environments.

Instructional designers (IDs) in U.S. higher education play a pivotal yet often misunderstood role in advancing student learning and institutional goals. Despite contributing to critical pedagogical and technological innovations, IDs continue to navigate ambiguity in how their work is defined and valued. This basic qualitative inquiry explores how 15 instructional designers at public and private research institutions make sense of the professional status of their field within institutional contexts. Findings reveal tensions between participants’ aspirational understanding of instructional design as a pedagogical, mission-driven field and institutional practices that position their work as primarily technical or supportive. Major findings include widespread confusion about the ID role due to a lack of unified definitions, the tendency of IDs to simplify their roles when communicating with others, and the influential role of leadership in shaping departmental culture and status. Participants articulated a strong commitment to learner-centered design, access, and social impact, while navigating conditions that complicate recognition of their expertise. By foregrounding practitioner perspectives, this study contributes to ongoing conversations about professional identity, legitimacy, and the professionalization of instructional design in higher education.

As HyFlex instruction has gained popularity in higher education, instructors face new demands when developing skills tailored to dual-modality teaching. While prior studies have examined instructors’ experiences with HyFlex through single-point surveys or interviews, researchers have yet to explore new instructors’ adaptation to HyFlex teaching over time. This qualitative study with sequential qualitative-quantitative mixed data analysis investigates the longitudinal HyFlex teaching experiences of two new instructors to understand how their perceptions, challenges, and instructional practices evolved throughout a semester in undergraduate, project-based design thinking course. Data were collected through weekly reflections and interviews over ten weeks across semester. We employed sequential qualitative-quantitative mixed analysis, combining inductive thematic analysis, qualitative time series analysis, and frequency analysis using text mining. Results revealed that new instructors primarily focused on technology integration, class preparation, engaging both remote and in-person students, setting expectations, and defining HyFlex instruction. Frequency analysis showed that technology integration was the most discussed theme, while defining HyFlex was the least. The findings were interpreted through the lens of the Technological, Pedagogical, and Content Knowledge (TPACK) framework. Instructors perceived Technological and Pedagogical Knowledge as the most needed TPACK domain for improving teaching in HyFlex as the most needed for improving instruction in HyFlex teaching. Practical implications for teacher development and institutional support are discussed, along with recommendations for future research.

This study complements the mainstream narrative in MOOC studies by emphasizing sustaining factors instead of dropout reasons. Using Kmeans clustering, sequential analysis, and course evaluation content analysis, we identified distinct engagement patterns and a nuanced view of student engagement in a MOOC. Frequent revisits defined the high-engagement cluster; within it, a subset of students voluntarily completed the end-of-course evaluation (hereafter, volunteer evaluators). These students showed targeted interactions, consistent activities, and emphasized values on practical tools and well-structured content. The findings provide insights into effective course design, emphasizing clear objectives, well-organized content, and the critical role of intrinsic motivation in sustaining engagement. This study highlights the importance of understanding diverse student behaviors to enhance MOOC engagement, particularly the unique characteristics of volunteer evaluators within a highly engaged cluster.

Social network analysis, as one of the social learning analytics (SLA) methods, have been combined with other analytical methods to understand social learning processes from a research perspective. However, few studies have devised the SLA tools to provide learning interventions. Filling this gap, this design-based research devised a student-facing SLA tool with the multi-method analytics to demonstrate network representations in China’s higher education context, with an expectation to foster student engagement. A multi-method approach was used to examine the effect of this tool on fostering students’ social, topic, and cognitive engagement in online collaborative discussions. Results showed that the SLA tool did not increase student engagement significantly. But the social network worked better for facilitating students’ social, topic, and cognitive engagement, compared to the topic and cognitive networks. Based on the empirical results, this research provided tool design and pedagogical implications to improve design and implementation of SLA tool in higher education.

Despite an increased understanding of the importance of student data to inform higher education teaching, little is known about how university faculty make sense of and use student data dashboards to inform their instruction. Through the lens of sensemaking theory, we explore how instructors navigate these tools and what challenges they experience during this process. Our findings suggest that faculty recognize the importance of student data in developing their courses, particularly to foster an inclusive and effective learning environment. However, there are a number of obstacles that arise when using student data dashboards. Study participants highlighted the limitations of the student data available to them, as well as multiple layers of support that are needed to ensure an understanding and appropriate use of the data. This research also revealed a common sentiment regarding the university’s responsibility to partner with faculty on student data and instruction-related issues. Overall, this study uncovers how faculty can be better equipped and supported in using data analytics tools towards the goal of improving student learning experiences and outcomes.

Blended or online courses (BOC) present unique challenges for students compared to traditional face-to-face learning environments. Such challenges may have an impact upon student persistence. The objective of this study was to identify factors contributing to student persistence in BOC. Structural equation modeling was used to examine the relationships between the predictor variables (a) community of inquiry presences, (b) learner autonomy, and (c) satisfaction with the dependent variable student persistence. Convenience sampling was used and a total of 348 students, enrolled in BOC at a post-secondary institution in the French-speaking region of Quebec, Canada, completed an online questionnaire. The results showed that student persistence in BOC can be explained by teaching and cognitive presence, by learner autonomy, and by student satisfaction. The full model, including all predictor variables, explained 23.6% of the variation in student persistence.

This study presents a conceptual replication of Moreno’s (Appl Cogn Psychol 21:765–781. 10.1002/acp.1348, 2007) study on the benefits of adhering to the segmentation principle when utilizing multimedia learning objects. Furthermore, this study expands upon the original by taking place in a low-immersive virtual reality environment, allowing for further understanding on the extent to which multimedia principles are still relevant. Both a synchronous and an asynchronous case are presented. Results indicate benefits for both cases in far transfer of learning. Furthermore, synchronous learners indicated a significant reduction in cognitive load and increased overall attitudes towards learning due to segmented instruction.

Online laboratories have gained a great deal of interest in recent years with benefits including reduced costs, support for increasing student numbers, increased flexibility and accessibility to practical work for students attending distance learning courses or with physical disabilities. However, designing teaching and learning activities for online laboratories introduces new challenges because many learning aspects that are inherent in conventional laboratories (e.g. safety, ethics, motor skills etc.) must be explicitly designed into online laboratories. This research aims to assist educators to design Science, Technology, Engineering and Mathematics (STEM) online laboratories that develop a broad range of learning objectives to meet students’ educational needs. In this paper a framework for STEM online laboratory learning objectives is introduced, building on previous approaches in the literature. The framework provides a structured approach to help course designers and educational technologists to design and assess the learning objectives and design characteristics of online experiments. The framework was used to map 23 online laboratories at a large distance learning university, and the results identified some trends and gaps in learning objective coverage. The results highlight the importance of defining the full breadth of learning objectives for online experiments at the design stage to ensure that the experiment is appropriately designed to allow students to achieve the desired learning outcomes. Furthermore, different online experiment designs are appropriate to different learning objectives, so care must be taken to select the most appropriate delivery mechanism for the online laboratory. It is proposed that the framework could be used by educators to support the design of new online laboratories as well as evaluating the laboratory learning objectives coverage in existing online laboratories.

Artificial intelligence-generated content feedback (AIGCF) has become increasingly valuable in the field of learning. Although research exists on AIGCF’s effectiveness, with some studies showing improved student writing and others showing minimal or negative effects, their overall impact remains unclear. This study aimed to examine the effect of AIGCF, exemplified by ChatGPT-4, on non-native English students’ writing quality and evaluate the quality of AIGCF itself. We conducted a single-group experiment with undergraduates. Thirty-two participants completed a series of writing tasks over ten weeks and received AIGCF for their work. We assessed the writing quality based on syntactic complexity, lexical complexity, accuracy, and fluency. We also evaluated the quality of AIGCF with respect to criteria-based feedback, clarity of improvement directions, accuracy, prioritization of essential features, and supportive tone. Preliminary findings suggested that AIGCF might be useful in influencing syntactic and lexical complexity, but its impact on improving accuracy and fluency was variable. The study revealed strengths and weaknesses in the quality of AIGCF, with criteria-based feedback emerging as a notable strength. The study also showed that the quality of feedback based on criteria and the clarity of suggestions for improvement got better over time. However, the prioritization of essential features, the accuracy of the feedback, and the tone of support decreased. It was concluded that the effectiveness of AIGC varies depending on the specific writing area. This study provided valuable insights into the potential of AIGCF in writing instruction and highlighted areas for future research.