Journal of Computing in Higher Education
1 day 12 hours ago
Using a mixed-methods approach, this study examines the relationship between personality traits, attitudes toward collaborative learning, and cultural intelligence in Collaborative Online International Learning (COIL) at individual, group, and within-group levels. It also explores how group composition and attitudes toward intercultural collaboration impact students’ cultural intelligence. The study includes 84 students from two universities, one located in the Netherlands and one located in the United Kingdom. Quantitative data from the Cultural Intelligence Scale (CQS), Big Five Inventory (BFI-2-XS), and Students’ Appraisals of Group Assignments (SAGA) were collected to measure cultural intelligence, personality traits, and attitudes toward collaborative learning group work. Additionally, qualitative data were gathered from student reflection reports and focus groups to provide deeper insights into their experiences. Findings show that personality traits such as conscientiousness, negative emotionality, and open-mindedness, along with attitudes toward collaborative learning, significantly predict cultural intelligence. Course design, including group composition, assessment, and attendance, also influence intercultural learning outcomes. These insights can help educators improve COIL course design for enhanced intercultural learning experiences.
6 days 12 hours ago
In the context of increasingly digitalized education, understanding students’ beliefs about online knowledge and their related learning behaviors is vital for enhancing the effectiveness of e-learning environments. This study aimed to investigate the predictive effects and relationships among gender, online information search strategies (OISS), the study process (SP), online academic help-seeking behaviors (OAHS), and internet-specific epistemic beliefs (ISEB) among 600 college students. The results of the study yielded several significant findings. First, male students were more likely than their female counterparts to adopt deep study processes. Additionally, male students were more inclined to seek academic help through both formal and informal channels. Moreover, the study revealed close relationships among the dimensions of students’ ISEB, OAHS, SP, and OISS. The study also showed that OAHS, SP, and OISS jointly and significantly predicted various dimensions of ISEB. The explanatory powers of these factors ranged from 19 to 36%. Notably, information-searching behaviors in OAHS emerged as common predictors. Finally, an ISEB cluster analysis revealed three distinct clusters: naive beliefs with high justification, modest beliefs with moderately low justification, and sophisticated beliefs with low justification.
1 week 1 day ago
The rapid evolution of Generative AI (GenAI) in higher education has outpaced the pedagogical supports and institutional guidance needed for equitable and effective use. While widely adopted, GenAI chatbots are often deployed without robust pedagogical frameworks or instructor mediation, heightening the risk of amplifying existing disparities in access, participation, and learning outcomes between marginalized and privileged learners. The CHAT-ACTS framework was developed to guide instructors, instructional designers, and policy makers in utilizing personalized GenAI chatbots to coordinate active learning and self-regulated learning (SRL). However, it does not explicitly address the needs of marginalized students or the representational biases embedded in GenAI systems.This conceptual paper extends CHAT-ACTS by integrating culturally sustaining pedagogy (CSP) and universal design for learning (UDL) to foreground equity, inclusion, and learner identity. We propose theoretical expansions that embed inclusive teaching strategies, critical reflection, transparency, and affirmative consent into the framework, with particular attention to multilingual English as an Additional Language (EAL) students, students with disabilities, and students of colour. The expanded CHAT-ACTS clarifies the roles of instructors, learners, and chatbots, and offers practical strategies for fostering equitable human–AI partnerships in instructor-mediated contexts. By centering culturally responsive and accessible design, it aims to mitigate bias, enhance learner agency, belonging, and sense of empowerment, and contribute to a broader discourse on equitable AI in education that is both technologically innovative and transformative from an inclusion lens.
1 week 2 days ago
Personalized learning has received notable attention due to its effectiveness, and intelligent tutoring systems (ITS) serve as a representative example. A commercially available ITS, Assessment and Learning in Knowledge Spaces (ALEKS) has particularly gained popularity in K-12 and higher education. The purposes of this study are to explore students’ learning experience and satisfaction with ALEKS and to examine the influential factors of learning achievement in ALEKS. The participants were 100 Korean undergraduates enrolled in blended learning courses using ALEKS. We measured the participants’ perceived satisfaction with ALEKS, as well as their self-efficacy and resource management. Additionally, ALEKS log data were used to evaluate learning achievement. For data analysis, we investigated why students were satisfied with learning with ALEKS and the influence of learning time, prior knowledge, self-efficacy, and resource management (e.g., time and learning environment management and effort regulation) on ALEKS learning achievement using a multiple regression model. The participants were satisfied with ALEKS because of the personalized learning features, learning effectiveness, learning motivation, and flexibility. Prior knowledge and learning time were statistically significant to predict ALEKS learning achievement. Self-efficacy, resource management, and effort regulation accounted for about 76.0% of ALEKS learning achievement. Practical implications are that instructors should systematically assess students’ entry-level skills and provide an appropriate timeframe for learning. To enhance self-efficacy, instructors should provide vicarious experience (e.g., opportunities to compare learning progress with others on the dashboard menu) or social persuasion (e.g., encouraging comments on learning progress). A short orientation should also be given to teach applicable resource management and effort regulation strategies for studying with ALEKS.
1 week 2 days ago
1 week 2 days ago
Despite vast experience in face-to-face teaching, faculty and graduate teaching assistants (GTAs) still have a need to develop an online teaching skill set. Emergency remote teaching resulted in an avalanche of professional development opportunities. Post COVID-19, very few faculty can say that they have not been challenged by the need to teach online. Unfortunately, reacting to emergency situations in higher education comes at the expense of online pedagogy. Furthermore, instructors and GTAs prefer online resources over face-to-face training because of time constraints. This demonstrates a critical need to offer easy-access, on-the-job, self-paced, and theoretically-sound support to instructors who struggle with teaching online courses. This study reports on the results of a design-based research project to create and implement Purdue Repository for online Teaching and Learning (PoRTAL). Our guiding research questions focused on faculty and GTAs need for support, the affordances needed for PoRTAL, its effectiveness, and the barriers for implementation, replication, and escalation. The research process included four major phases and each phase comprised two development steps. The four phases were: 1) needs analysis and exploration, 2) internal evaluation, 3) expert evaluation, and 4) implementation. Each phase resulted in design principles to contribute to theory. Six design principles were identified: accessibility of open educational repositories, human performance technology principles for faculty training, attainable universal design for learning guidelines, online pedagogy vs technology training, content and design balance, and cultural and institutional barriers for project replication. Recommendations for practice and limitations of implementation are given.
1 week 2 days ago
1 week 5 days ago
Online learning became a pervasive instructional method with the onset of the pandemic. Online programs and course offerings are continuing to increase due to their flexibility and convenience. Effective instructional methods in online education differ from methods that are effective in other learning environments because the absence of personal connection causes feelings of isolation. Collaborative learning May be a method to mitigate this issue, but designing effective collaborative learning in the online environment can be challenging, as there are more factors to consider than designing collaborative learning in a classroom setting. This study invited online students to share their experiences with the design of online group work as a function of group size, formation, duration, and type of collaborative work. Additionally, students’ perceptions of the effectiveness of online collaborative learning and levels of social and teaching presence were assessed through an electronic survey. The findings indicate that online collaborative activities occur in randomly assigned, small groups completing assignments, or peer reviews over various durations of the course. Levels of teaching and social presence were affected by group formation methods and activity type. The findings have implications for online instructors and instructional designers who have an interest in designing effective online collaborative learning experiences.
2 weeks ago
This study aims to construct a model that explains and predicts the relationships between teachers’ readiness for blended learning, their digital competencies, and their self-efficacy in creating digital teaching materials. We aim to determine the predictive relationships between teachers' digital competencies and their digital teaching material development self-efficacy and their readiness for blended learning. This study also aims to produce a model that explains and predicts the relationships between digital competencies, digital teaching material development self-efficacy, and blended learning readiness. A descriptive survey model was implemented. The study group is made up of 317 teachers at different levels of education. In the study, three different data collection tools were implemented. These are "Readiness of Teachers for Blended Learning" scale, the fourth factor of the nine-item "Instructional Design" of the "Preparation of Pre-service Teachers for Integrating Technology in Education According to the SQD Model" scale, and the "Self-efficacy of Teachers for Developing Digital Teaching Materials" scale. A Structural Equation Model was used to construct a model that is designed to explain and predict the relationships between these scales. The findings of the study demonstrate that teachers' digital competencies positively affected their self-efficacy in enhancing digital teaching materials. Their self-efficacy in enhancing digital teaching materials positively affected their readiness for blended teaching. Furthermore, teachers’ self-efficacy in preparing digital teaching materials had a mediating role in the relationship between teachers' digital competencies and their readiness for blended teaching. These results demonstrate that as teachers become more proficient with technology, they become more confident and willing to prepare digital teaching materials.
2 weeks ago
This study examines the integration of AI in education (AIEd) through the lens of critical AI literacy, addressing the challenges educators face and the influence of dominant discourses on their perceptions of agency and collective well-being. Conducted within a newly designed course titled “Artificial Intelligence in Education,” the research explores the concept of discursive closure, which limits educators’ critical engagement by promoting deterministic views of AI. The findings reveal that discursive closure often forces educators into adopting AI without sufficient critical examination, marginalizing their voices and reducing their agency. By introducing the Directions of Cartographies of Critical AI Literacy, this study provides a framework for navigating AI integration that supports educators in mapping their struggles, imagining alternatives, and engaging in resistance and action to cultivate critical AI consciousness. The framework highlights overlapping pathways through which educators can challenge dominant narratives, surface marginalized perspectives, and envision more just futures for AI in education. Our findings illustrate that educators’ engagements with AI often blur neat distinctions between struggle, possibility, resistance, and action, highlighting critical AI literacy as a dynamic practice of negotiating contradictions rather than a linear progression.
2 weeks ago
Generative artificial intelligence (GenAI) offers potential solutions to educational challenges by personalizing learning experiences for diverse learners. However, it also introduces complexities around issues of ownership of learning. As an indicator of empowered learning, ownership is a multifaceted but underexplored concept. In this study, the authors adopted a mixed-methods multiple-case study design to explore non-traditional online students’ perceived ownership of learning when using ChatGPT for instructional design. The authors adopted the psychology ownership construct consisting of self-efficacy, accountability, belongingness, and self-identity as the theoretical framework. Findings revealed students’ prior AI experiences and educational levels played a significant role in their perceived ownership. In contrast, no significant differences were observed for students in different learning modalities (synchronous vs. asynchronous) or with different neurodivergent conditions. This multiple-case study also explored how students used ChatGPT within and outside the courses, perceived challenges, and the role of GenAI in promoting educational equity from students’ perspectives. As one of the first studies exploring students’ ownership of learning in GenAI-facilitated educational contexts, the implications for practice and research were discussed.
2 weeks 6 days ago
Educators in higher education routinely adapt their teaching in real time to respond to student needs, classroom dynamics, and emerging understanding. While adaptive learning systems offer personalisation through algorithms, they often overlook the nuanced, moment-to-moment decisions educators make during live instruction. This study explores how university educators enact real-time instructional flexibility and formalises these practices as a set of four adaptive teaching design patterns. Drawing on interviews and lesson graph analysis with twelve university educators, we identify common strategies for dynamically modifying lesson flow, content delivery, and engagement techniques. The resulting design patterns offer a structured vocabulary for describing and supporting educator-led adaptation. We discuss how these patterns can inform the design of hybrid and digital learning environments that preserve teacher agency and enhance pedagogical responsiveness. The findings have implications for both educational practice and the development of adaptive technologies that align more closely with the realities of university teaching.
1 month 1 week ago
Written communication is an important skill for college students as it fosters the critical thinking and analytical skills required for success in education and beyond. Finding the time and resources needed to provide students with substantial feedback can be a challenge for faculty. Artificial Intelligence (AI) can provide (1) innovative support to address obstacles associated with grading and (2) formative feedback to students. However, as AI continues to make strides within education, there is mounting concern around human–AI partnerships. The present study centers on the impact of human–AI integrated grading and feedback. It focuses on educators’ perception of one AI tool in its ability to assist both educators in providing feedback and students in evaluating their own writing during the process. The research team examined adoption of the tool in four post-secondary courses across multiple semesters, in psychology, learning technology, and public administration, looking at changes in student writing, educator feedback, and occurrences of educators overriding the AI in grading. Findings suggest educators found the tool to be moderately useful in grading and providing feedback, primarily via regular implementation of long-form assignments that used the AI grading assistant. Educators perceived the AI took care of some mechanical aspects of providing feedback and grading. We also describe aspects of the AI tool where there was a perceived need for educator grading override and input. We conclude with recommendations for educators and administrators to optimize AI grading tools to ease workload and enhance student engagement in the writing process.
1 month 3 weeks ago
This paper aimed to explore the effectiveness of Generative Artificial Intelligence (GenAI) in improving classroom interactional justice. This experiment employed a within-subjects design, where twenty students first completed tasks under control condition with the assistance of a teacher, and then completed tasks under experimental condition with the assistance of the GenAI. The data we collected included students’ perception of classroom interactional justice, the opportunity justice in classroom interactions involving the interaction frequency and the quantity of interaction content, and task scores. The results showed that the perception of classroom interactional justice among students in the teacher group was significantly higher than that of the GenAI group. However, the interaction frequency and the quantity of interaction content in the GenAI group were significantly higher than those of the teacher group. In addition, students achieved significantly higher task scores with the assistance of GenAI. This study emphasizes the potential of GenAI in improving classroom interactional justice and offers implications for the utilization of GenAI in education.
1 month 3 weeks ago
The current study aims to test how learners’ digital literacy and dialogue with instructors function in their engagement and satisfaction in synchronous-oriented online learning environments. For this aim, a multiple mediation model was tested based on the transactional distance theory. The tested model explains the large portion of the variance in learner satisfaction, underlining the critical roles of digital literacy and dialogue in synchronous-oriented online learning. Further findings showed that both the single and multiple mediations of learner-instructor interaction and engagement are significant in the digital literacy-satisfaction relationship. The main implication of the present study is that improved learner engagement and satisfaction are possible through adequate digital literacy and improved dialogue between learners and instructors even in learning contexts where learning materials are rigid, have low individualization, or cannot meet diverse learner needs. The findings were discussed based on the theory and relevant studies, and recommendations were offered for sustainability and resilience in higher education.
2 months ago
A mixed-methods study was conducted to examine the influence of diversified feedback strategies, including teacher-provided feedback, peer feedback, and automated writing evaluation (AWE) feedback, on the development of deep writing skills (conceptualized as clarity, argumentation quality, grammatical conventions, punctuation, citation accuracy, and organization and coherence) among 187 randomly selected Chinese EFL learners in an online learning context. Methodological triangulation was employed, incorporating both quantitative and qualitative data sources to provide a detailed analysis of feedback dynamics. A one-way ANCOVA was performed to analyze the data and interpret the findings. Results indicated that the integration of interactive feedback types contributed significantly to the enhancement of deep writing skills. Specifically, the mean scores for the intervention group (M = 86.13, SD = 9.03) were higher than those for the control group (M = 72.56, SD = 12.23), F(1, 185) = 98.52, p < .001, ηp2 = 0.35. Note that subsequent analyses revealed a higher partial η2 (0.98) when accounting for the interaction between group assignment and pretest scores, indicating that the combined effect of feedback type and prior proficiency explained nearly all variance in posttest outcomes. The initial ηp2 = 0.35 reflects the main effect of group assignment alone. The combined application of teacher and peer feedback, along with Grammarly Premium feedback, demonstrated a statistically significant effect on multiple writing dimensions, including clarity (p < .01), argumentation quality (p < .01), grammatical conventions (p < .01), punctuation (p < .01), citation accuracy (p < .01), and organization and coherence (p < .01). The findings underscore the theoretical potential of interactive feedback strategies to enhance EFL learners’ deep writing skills. From a pedagogical perspective, the study highlights the importance of incorporating diverse feedback strategies to design effective writing instruction targeting specific areas for improvement and fostering learner engagement and motivation. The research emphasizes that utilizing multiple feedback sources facilitates a comprehensive and nuanced approach to feedback, ultimately improving learners’ writing competencies in an EFL context.
2 months ago
In instructional design (ID) projects, work is commonly completed in teams. Therefore, instructional design students need opportunities to develop their interpersonal skills to be prepared for future collaborative work. Frequently, instructors use team activities to facilitate these skills. However, facilitating team cohesion and improving students’ attitudes towards teamwork to enhance their engagement in team activities is challenging. Gamification is a strategy that can potentially address these challenges. In this mixed method, quasi-experimental study, we examined the effectiveness of using gamification in online team activities to improve students’ attitudes towards teamwork, their learning outcomes and experiences, and team cohesiveness. Participants included students in two sections of an online instructional design project management course. While all course activities were the same for both sections, gamification strategies (leaderboards, monetary reward, gamified quizzes) were incorporated for one section of students. Data included online survey responses and reflections. A significant difference was observed in the coefficient of variation between the two groups, suggesting that gamification of activities increased the conformity in student opinions with respect to cognitive learning, dialogue promotion and open communication, and collaborative learning. Insights from student reflections regarding team activities offer guidance for facilitating team activities in online courses.
2 months 1 week ago
Online learning has become a fundamental component of higher education. Predictive analytics tools are increasingly used in online learning environments to provide performance insights and learning support. These analytical approaches continue to be evaluated for their effectiveness and scalability in diverse educational settings. Despite their potential benefits, concerns exist regarding equitable outcomes across different demographic groups and individual learners. Current research on AI fairness in education has established theoretical foundations while also highlighting some limitations. There are preliminary studies categorizing sensitive individual attributes into binary groups (e.g., economic status), which may not capture the complexity of real-world scenarios. Furthermore, defining and operationalizing individual fairness is potentially more challenging than group fairness, leading most research to focus only on the latter. However, it is crucial to ensure that AI algorithms provide similar individuals with similar predictions. To address these concerns, we propose a fairness-aware model called Con-LSTM. This model incorporates contrastive learning to promote fair feature learning for multi-level prediction tasks. We evaluated the group fairness (measured by Subgroup AUC) and individual fairness (measured by similarity-based consistency score) of Con-LSTM using the Open University Learning Analytics Dataset, comparing it against fairness-unaware LSTM model. Our findings show that Con-LSTM achieves both high predictive accuracy and fairness. By balancing these two critical aspects, our approach highlights the potential to offer fair and reliable insights into student performance. This advancement could lead to more personalized and equitable educational experiences, demonstrating the significant benefits of integrating fairness-aware principles into AI models used in education.
2 months 4 weeks ago
In the domain of learning analytics, which leverages data-driven approaches to support teaching, a valuable application emerges in the form of AI-based educational predictive models. Beyond performance, researchers increasingly emphasize the fairness of such models, with fairness being assessed based on demographic attributes. This paper first constructs an engagement detection model within an online learning community exhibiting gender imbalances, situated in a blended learning university environment psychology course. Drawing upon communication theory, the model’s features are designed, followed by an exploration of statistical disparities between male and female characteristics. Subsequently, through a comparative analysis of AI models that either exclude or include gender, considering performance changes and group fairness, coupled with explainable AI methodologies, the impact of gender on the models is examined. The results suggest that improvements in model performance are more likely to benefit from sensitive attributes with high feature importance. In scenarios where gender is excluded, latent biases inherent within the dataset may lead to group unfairness, exacerbated upon inclusion of gender. Based on these findings, we delineate circumstances under which modeling sensitive attributes is permissible, while discussing arguments supporting and opposing the inclusion of such attributes from both performance and fairness perspectives. The analytical methods and findings of this study offer novel insights for designing more equitable and effective educational predictive models within learning analytics.