Journal of Computing in Higher Education

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.

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.

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.

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.

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.

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.

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.

In this manuscript, we explore the intersection of artificial intelligence (AI) and equitable learning in higher education, focusing on data science as a subset of AI and social justice as the core theme of equity. Our investigation sheds light on the nuanced tensions inherent in employing data science for social justice. Rooted in situated perspectives of learning and consequential learning, our study employs an instrumental case-study methodology and analysis techniques from interaction and conversation analysis. Collaborating with three undergraduate students and an urban farm, the students used data science practices to highlight inequities surrounding food justice and access to food. Our findings reveal three key tensions: (1) the undergraduates' discourse on simplicity versus complexity in utilizing data science for social justice, (2) the challenges of balancing data science with social justice imperatives, and (3) the successful application of data science by the students in their food justice project, culminating in a presentation of their findings to the farm's director. We conclude by discussing implications for research and the use of data science in social justice projects

In this study, the researchers surveyed 276 students from an undergraduate online course and analyzed 10,927 of traced behavior sessions in a learning management system. The study examined the relationship between students’ academic motivation (e.g., utility value, attainment value, intrinsic value, and self-efficacy) and their behavioral engagement in online learning. The researchers focused on both the quantity of behavioral engagement represented by the frequency of page views and the quality of behavioral engagement represented by the sequential patterns of page views. Markov Chain Model analyses revealed six unique sequential-action profiles of behavioral engagement, including Assignment-focused Profile, Dual-focused Profile on Assignment and Reading, Triangular-balanced Profile on Assignment, Reading, and Resource, Integrated Profile, Reading-focused Profile, and Assignment-focused with Planning Profile. Along with five single-action clusters (i.e., Assignment-only Profile, Reading-only Profile, Overview-only Profile, Resource-only Profile, and Overview-only Profile), the study included a total of eleven session clusters. The findings revealed nuanced relationships between motivation and various clusters of behavioral engagement in online learning.

Revealing the dynamics of collaborative engagement and its association with collaborative problem-solving (CPS) outcomes is beneficial for guiding teachers to provide adaptive support during CPS processes. Although existing research has indicated that the various dimensions of collaborative engagement influence each other over time, it remains unclear how the internal dynamics of collaborative engagement are associated with CPS outcomes. Additionally, previous studies have primarily employed manual coding to analyze collaborative engagement, which limits the exploration of the dynamic characteristics of collaborative engagement in large-scale datasets. This study aimed to investigate the internal dynamics of collaborative engagement, encompassing behavioral, cognitive, socio-emotional engagement, and their relationship with CPS outcomes. Specifically, a learning analytics method based on deep learning models was employed for the automatic detection of collaborative engagement. Subsequently, according to the results of automated detection, hidden Markov modeling was utilized to investigate the difference in the internal dynamics of collaborative engagement between high- and low-performing groups. These investigations were grounded in a dataset comprising 57,400 utterances collected from 20 groups participating in a CPS activity within a university setting. The findings showed that compared with high-performing groups, low-performing groups were more likely to become stuck in the states of "Limited Cognitive Engagement" and "Lone Neutral Participation" during CPS process. Furthermore, high-performing groups were found to transition away from the collaborative engagement state of "Cognitive Conflict with Confusion" by enhancing behavioral engagement and deepening cognitive engagement. While the low-performing groups remained trapped in the cycle between the states of "Cognitive Conflict with Confusion" and "Limited Cognitive Engagement." According to these findings, pedagogical insights and analytical implications were addressed.

The rapid emergence of artificial intelligence (AI) technologies is profoundly reshaping teaching and learning practices in higher education. This shift challenges traditional pedagogical approaches and increasingly requires learners to develop autonomy, adaptability, and lifelong learning skills. As this trend accelerates, it becomes essential to understand how AI-driven tools and teaching approaches are being used to promote self-determined learning. However, the use of AI technologies to support the development of lifelong learning skills in self-determined learners is still in its early stages, underscoring the need for further investigation into existing learning environments. Therefore, this scoping review investigates AI-driven tools and approaches for the development of self-determined lifelong learning skills. This is the first scoping review to present and synthesize self-determined lifelong learning skills within the implementation of AI-driven learning environments. The results suggest that AI-driven technologies predominantly develop self-determined lifelong learning skills and competencies such as learner agency, self-efficacy and capability, metacognition and reflection, responsibility, digital competence, critical thinking, collaborative learning, problem-solving, employability, and self-regulated learning but do not sufficiently draw on non-linear learning and learning how to learn skills. In addition, computational thinking, with its scarcity among other skills and competencies, holds significant promise for future research on the development of self-determined lifelong skills. University instructors, instructional designers, and future employers can draw on these insights to collaborate and integrate AI-driven technologies to enhance the development of self-determined lifelong learners.

This study aims to validate the mediation effects of assessment engagement and its constructs on the relationship between online rating ability and critical thinking. This study involved graduate students from a university who enrolled in the “Research Methods” course and utilized an online peer assessment system to rate the project report of each other. This study, based on social cognitive theory and self-determination theory, proposes and validates a concurrent mediation model—“online rating ability◊assessment engagement/its constructs◊critical thinking.” The findings are summarized as follows: (1) Rating ability significantly and positively influences assessment engagement, and assessment engagement significantly and positively influences critical thinking. (2) Rating ability significantly and positively influences all four assessment engagement constructs, and assessment behavioral engagement and assessment cognitive engagement significantly and positively influence critical thinking. (3) Rating ability not only significantly and directly influences critical thinking, but also simultaneously exerts a significant indirect influence on critical thinking through assessment engagement (significant indirect effect and partial mediation). (4) Rating ability can exert a significant and indirect influence on critical thinking concurrently through both assessment behavioral engagement and assessment cognitive engagement (significant indirect effect and full mediation); however, at this point, rating ability no longer significantly and directly influences critical thinking, nor does it significantly and indirectly influence critical thinking through assessment emotional engagement and assessment agentic engagement. (5) Assessment engagement exhibits a significant and partially competitive mediation effect in the influence of rating ability on critical thinking, with a substantial mediation effect size. (6) In the concurrent mediation model, assessment behavioral engagement and assessment cognitive engagement both exhibit complete and concurrent mediation effects in the influence of rating ability on critical thinking. The research results provide significant implications for academic theory and educational practices.