Sources
Instructional Science
Associations between student-perceived teaching quality and students’ mathematics confidence and mathematics achievement: A study of Swedish grade 4 TIMSS 2019
Students’ socioeconomic background is a strong predictor of academic achievement. Likewise, teaching quality is considered an important prerequisite influencing students’ educational opportunities and their academic achievement and affective outcomes. Empirically, there is still a need for research on these presumed relations. Especially on the associations between younger students’ perceptions of teaching quality and students’ mathematics achievement and mathematics confidence. Drawing theoretically on educational effectiveness research, this study investigated relationships between aspects of teaching quality (classroom management and instructional clarity) and mathematics achievement and mathematics confidence. These relationships were explored in a secondary analysis of students’ perceptions of their teacher’s activities and instructions as indicators of teaching quality. The rationale is that students experience and perceive the teacher’s actions in the classroom, and they may be influenced differently. Aggregating students’ perceptions at the classroom level will decrease the bias of individual ratings. The sample comprised grade four data from the Swedish 2019 cycle of the IEA Trends in Mathematics and Science Study (TIMSS) (N = 3,965). Multilevel structural equation modelling was used to keep classroom variation separate from individual variation when exploring associations between classroom management and instructional clarity and mathematics achievement, and mathematics confidence. Results showed that at the classroom level, mathematics achievement is strongly related to aggregated classroom management. At the individual level, classroom management was associated significantly with mathematics achievement, and instructional clarity with mathematics confidence. Additionally, teaching quality could be beneficial for students with lower SES backgrounds, as findings indicated an achievement gap between classrooms in Sweden. Limitations and implications are discussed.
Dynamics of university students’ epistemic emotions during game-based learning in an international relations course
Enhancing learning of nanoscale phenomena: the role of agent-based models and embodied reasoning
Understanding nanoscale phenomena poses significant challenges for students because it requires reasoning about emergent behaviours that are invisible and highly abstract. Agent-based models (ABMs) can function as embodied learning environments, enabling students to manipulate individual agents and observe system-level outcomes while engaging embodied processes such as perspective taking, causal inference, and motor-supported thinking. In this study, two treatment conditions were developed: (a) working with ABMs and (b) watching visualization videos. The aim was to investigate whether ABMs are associated with greater engagement in embodied reasoning strategies than videos, and whether such engagement is associated with conceptual understanding in nanoscience education. Twenty-seven undergraduate students were assigned to one of the two conditions, and their conceptual understanding was assessed through pre- and post-tests alongside process data. Results showed that ABM students demonstrated greater gains in explanatory knowledge, knowledge transfer and engaged more frequently in embodied reasoning strategies. Beyond nanoscience, the findings illustrate how technology-enhanced instructional designs that leverage embodied processes—such as gesture, spatial transformation, and active manipulation—may support students’ reasoning about complex scientific phenomena across STEM domains.
Effects of a situated learning theory based mobile augmented reality application on primary students’ Tang poetry learning outcomes, motivation, and technology acceptance
Mobile augmented reality (MAR) provides learners with an immersive and interactive experience. However, few studies have applied situated learning theory (SLT) to the design of MAR in educational contexts, and even fewer have explored its use in the learning of Tang poetry. This study aimed to address this gap by designing and developing a mobile augmented reality application (app) based on situated learning theory (SLT-MAR) to support primary school students in learning Tang poetry. The study involved 120 4th-grade students from a public primary school in central China. Using a QUAN-qual sequential mixed-methods experimental design, this study examined the effectiveness of the SLT-MAR app. The results indicated that students using the SLT-MAR app outperformed students in the MAR and traditional classroom learning (TCL) groups in terms of Tang poetry achievement, motivation, and technology acceptance. In addition, semi-structured interviews revealed that students perceived the SLT-MAR app as enhancing their motivation and found it beneficial to their Tang poetry learning. The study discusses both theoretical and practical implications, contributes to the literature on integrating SLT with AR technology in educational settings, and provides insights for future research and practice.
Examining teacher change in reflective practice: Insights for professional development on adopting representational scaffolding in the inquiry-based learning
Inclusive pedagogy in practice within the multi-tiered systems of support framework: a design-based research in a Chinese EFL classroom
This design-based research study examines the implementation of inclusive pedagogy in an English as a Foreign Language (EFL) classroom in a Chinese elementary school, integrating the Teaching Proficiency through Reading and Storytelling (TPRS) method within the Multi-Tiered System of Support (MTSS) framework. Addressing a critical gap, it explores how inclusive pedagogy can be effectively applied within China’s exam-oriented education system, where such practices are often misunderstood or underutilised. Over three years, iterative cycles of planning, action, development, and reflection refined teaching strategies and assessed their impact on diverse learners. Data were collected through teacher field notes, classroom videos, parent and peer-teacher feedback, and student assessments, including standardised tests and reading evaluations using the Oxford Reading Criterion Scale. The findings indicate that inclusive pedagogy significantly enhanced students’ English proficiency, engagement, and sense of belonging, benefiting high-, mid-, and low-performing students alike. Additionally, the study highlights the transformative role of teacher collaboration and parental involvement in overcoming cultural and systemic barriers. By providing empirical evidence of the effectiveness of inclusive pedagogy in a non-Western context, this research offers valuable insights for educators and policymakers aiming to implement inclusive practices in diverse educational settings. The findings underscore the importance of evidence-based, culturally responsive approaches to inclusive education that support all learners.
Supports for preservice teachers’ metacognitive monitoring and explanations of fraction comparisons
Leveraging conjecture mapping for design-based research: a case of adoption and development
Design-based research (DBR) is a widely adopted methodology in the learning sciences. As an alternative to the conventional experimental studies in controlled lab settings, DBR recognizes the intricate dynamics of the naturalistic educational settings and distinguishes itself through well-defined contexts and iterative design cycles that generate findings to inform both theory and practice. A significant advancement in DBR is Sandoval’s introduction of conjecture mapping (CM) in 2004 and subsequently refined in 2014. CM provides a visual framework to elucidate the DBR designs, with the primary objective of identifying the most critical features of a learning design. This paper reports on a case of adopting CM in a three-cycle DBR study of Character Education Learning through Digital Storytelling in Primary 5 classrooms in Singapore. It refines the conjecture formulation and introduces a generic conjecture statement to reinforce the robustness of DBR design logic. Design principles, intervention evidence, and complementary evidence are incorporated as new components to strengthen the links between DBR and CM frameworks. Additionally, it enhances the visual notations of the map to systematically distinguish conjectures by their status and prominence within each cycle. This paper exemplifies the application of CM. It contributes to the ongoing discourse on CM, while advancing the discussion of DBR as a meta-methodology and proposing future directions for DBR and CM research.
The influence of role assignment strategy on learning engagement, learning outcomes, and perceptions in asynchronous discussion groups
Understanding compatibilities and inconsistencies between science teachers’ professed beliefs and practices about question-asking in science lessons
Teachers rehumanizing mathematics for students with disabilities; the role of empathy
Simulations, institutional know-how and the evolution of practices of learning: A commentary
Learning with concept maps: the effect of activity structure and the type of task
A review of the literature reveals a discrepancy about what type of task with concept maps is the most effective for individual learning. Furthermore, to date, no research has compared these tasks in individual and collaborative learning contexts. This paper explores the influence of the different tasks on learning, involving concept maps and summaries. The participants were 226 undergraduates who were randomly assigned to 12 experimental conditions. Two independent variables were considered: the knowledge representation task (fill-in-the-blanks concept map, sort a shuffled concepts-provided map, self-construct a map, write a summary) and the structure of the activity (individual + collaborative, collaborative + individual, and fully individual). In addition to the evaluation of comprehension and delayed recall, 4195 verbal exchanges during the collaborative activities were recorded and analyzed. Results confirm an interaction between the type of task and the structure of the activity. The students who self-constructed complete concept maps and then discussed them in pairs obtained better learning results than those in other conditions. Verbal interaction was much more dialogic in this type of task, with a significantly greater proportion than in the other conditions of in-depth exploratory talk episodes, and a lower proportion of cumulative talk. However, the fill-in-the-blanks and shuffled-concepts conditions provided a greater proportion of superficial exploratory talk, and the collaborative summary condition generated a greater proportion of non-dialogic talk fragments. The findings are discussed in the context of the ICAP (Interactive, Constructive, Active, and Passive learning) framework, cognitive load theory and the sociocultural perspective on dialogic learning.
Correction: How differences in self-determined motivation relate to the perception of formative assessment and self-regulated learning in secondary school: a person-centered approach
From curiosity to competence: the pivotal pathway of grit and flow in a large-scale study of Chinese adolescents
In an increasingly competitive educational landscape, understanding the determinants of academic success is imperative. While prior research has identified epistemic curiosity as a predictor of academic achievement, the psychological mechanisms through which curiosity cultivates internal resources—such as grit and learning flow—remain underexplored. Drawing on Psychological Capital Theory and Self-Determination Theory, the present study investigates how epistemic curiosity promotes grit and learning flow, which together constitute a serial mediation pathway leading to academic success in a large sample of Chinese adolescents. Utilizing data from 17,352 students across 36 primary and secondary schools in 21 provinces in China (age = 9–18, M = 14.1, SD = 2.9), we employed established measures such as the Epistemic Curiosity Scale, Grit-S, and Study-Related Flow Inventory. Our findings reveal that: (1) epistemic curiosity, grit, learning flow, and academic performance are significantly and positively correlated; (2) learning flow and grit may potentially serve as partial mediators in the relationships between epistemic curiosity and academic performance; (3) grit and learning flow point to a potential sequential mediation process connecting epistemic curiosity to academic performance. This research advances the theoretical understanding of curiosity-driven psychological resource development and offers valuable implications for educational policy and practice, emphasizing the importance of nurturing these psychological traits to enhance student performance.
Simulation-based training in professional education: learning, participation, and instructional design
Testing the testing effect with advanced materials while accounting for individual differences
Retrieval practice benefits long-term memory performance in a variety of contexts, but its usefulness for retention of more advanced material is less clear. Additionally, evidence of the role of individual differences in retrieval strategy use is mixed. Such evidence is important to determine the usefulness of particular retrieval strategies as a function of cognitive processes, affective characteristics, and aspects of to-be-learned material. With a sample of undergraduate participants (n = 213), we examined how combining testing strategies (free recall, practice quizzing, and test question generation) could impact learning of a published research article compared to rereading, and how cognitive load, self-efficacy, and working memory capacity influenced these relationships. Although retrieval practice activities did not improve final test performance over rereading, the role of some individual differences varied as a function of assigned strategy. Compared to rereading, using retrieval practice increased cognitive load. Cognitive load then mediated the relationship between assigned strategy and retrieval performance, with higher cognitive load associated with lower final test scores. These results suggest that the memorial benefits of retrieval practice might be limited by cognitive load requirements.
Debriefing as epistemic engineering: conversational remembering and socially distributed metacognition in healthcare simulation
This study aims to deepen the theoretical foundations of debriefing in simulation-based pedagogy. By applying distributed cognition (DCog), we analyse a case of a simulation debriefing with nursing students. Using transcribed excerpts from video recordings, our cognitive ethnography shows how debriefing conversations function as a coordination device for two key processes: joint conversational remembering and distributed metacognition. According to our model, debriefing conversations coordinate a cascade of representations of simulated events. This cascade enables participants to engage reflexively with past actions, transforming these into shared learning opportunities. Reframing the trajectory of public representation in debriefing conversations in terms of DCog reveals new insights into the cognitive consequences of debriefing practices and how they can be developed. We highlight the importance of enabling multiple viewpoints and shared cognitive resources for epistemic outcomes, while also challenging students’ assumptions and foster critical thinking skills.
How are Lao teachers’ goals for teaching associated with their instructional practices?
Achievement goal theory (AGT) has become increasingly prominent for exploring the interplay between teachers’ goals and their instructional practices. Prior research has suggested that relational goals significantly impact mastery-oriented instruction, surpassing the influence of mastery goals. Nonetheless, much of this evidence is derived from studies conducted in Western contexts. The current work investigates the relationships between teachers’ goals and their instructional practices in the Lao context, aiming at providing a more nuanced understanding of these relationships and examining their applicability in collectivist cultural settings. A total of 207 teachers participated in this study by completing an online questionnaire that assessed their teaching goals, instructional approaches, and specific practices. Structural equation modeling was employed to analyze two models. Model 1 revealed positive correlations between relational and mastery goals with mastery-oriented instructional approaches and between ability-approach and ability-avoidance goals with performance-oriented strategies. Additionally, Model 2 demonstrated that relational goals were associated with recognition, grouping, and mutual respect, whereas mastery goals were linked to task and recognition. The findings underscore the central role of relational goals in fostering effective instructional practices, even in a collectivist cultural setting. This study also offers a more detailed understanding of the interconnection between the goals of teachers and their instructional practices, contributing to the cross-cultural AGT literature and providing insights for enhancing teaching practices globally.
Interactive Learning Environments
International Journal of Computer-Supported Collaborative Learning
Co-constructing critical data literacy in families: A technology-mediated learning perspective
As smart technologies become part of daily life, families face new opportunities and challenges in learning together. This paper introduces FamiData Hub, a speculative computer-supported collaborative learning (CSCL) prototype that supports families in building critical data literacy within smart homes. Through workshops with 17 families, the study explores how collaborative learning emerges through interaction, storytelling, and shared problem-solving, with family roles shifting dynamically. The findings challenge traditional adult-to-child teaching models, proposing instead a multidirectional learning space where anyone—including children and digital tools—can be the “more knowledgeable other.” The study highlights the value of family centered, socially embedded approaches to critical data literacy and offers insights for designing intergenerational CSCL systems to foster critical data literacy.
A phase-sensitive multimodal learning analysis of high- and low-performing teams: the dynamics of mutual engagement
Mutual engagement, the dynamic process through which collaborators reciprocally take up and sustain one another’s ideas and actions, is crucial to collaborative problem solving (CPS). However, existing research has yet to fully specify concepts or methodologies needed to capture these dynamic characteristics. This gap highlights the need to examine how these patterns evolve across different CPS phases to inform more sophisticated instructional strategies that enhance collaborative learning. This exploratory study integrates multimodal and content analyses to examine phase-sensitive patterns of mutual engagement in small teams. In total, 28 college students participated in video-recorded CPS activities across four distinct phases. The findings revealed that high-performing teams displayed structurally complete elaborative sequences, in which invitations to contribute were taken up, elaborated, and reciprocated. Low-performing teams, by contrast, exhibited fragmented sequences that failed to return to elaboration. These interactional differences co-occurred with distinct multimodal signatures. High-performing teams exhibited greater interest, less frequent neutral emotions, and early posture synchrony, patterns that were especially pronounced during the ideation phase. Conversely, low-performing teams showed lower interest, persistent neutral emotions, and late, reactive posture synchrony. The findings elucidate the socio-cognitive characteristics of mutual engagement and demonstrate the potential for integrating emotional and behavioral indicators for a richer understanding. These insights can inform the design of instructional scaffolding and phase-sensitive support systems to enhance successful collaborative learning.
Advancing collaborative discourse through knowledge synthesis
Productive collaborative discourse requires students to continuously advance ideas, often through the creation, modification, and integration of digital artifacts in a communal space. Without these processes, ideas remain isolated, fragmented, and unable to advance shared understanding. To support such discourse processes, this study proposes a knowledge synthesis (KS) intervention to facilitate a process of creating knowledge syntheses from ideas represented in digital artifacts and then leveraging these knowledge syntheses, represented in new digital artifacts, to deepen student collaboration. To examine the enactment of this intervention in a graduate-level course, we asked: What were the key characteristics of students’ knowledge synthesis artifacts? How did student groups use the synthesis artifacts during their discourse? To what extent did the synthesis artifacts facilitate collaborative discourse? We analyzed multiple data sources—including student-created synthesis artifacts, perception data, classroom video recordings, and co-constructed group artifacts—using a combination of descriptive, content, and interaction analyses. Findings revealed diverse approaches to knowledge synthesis and showed that synthesis artifacts facilitated discourse progression, fostered a range of knowledge practices, and supported the evolution of group artifacts. By promoting knowledge synthesis and examining its role in collaborative discourse, this study contributes to computer-supported collaborative learning (CSCL) by advancing the theoretical understanding of knowledge synthesis and offering pedagogical strategies for supporting this practice in classrooms.
Understanding when anger becomes productive or destructive in collaborative educational games
In collaborative learning game environments where competition and collaboration coexist, conflicts among students are not uncommon. While conflicts of ideas and opinions are prevalent during collaborative learning, they are often perceived as elements to be avoided. One of the main concerns about conflict is its ability to trigger negative emotions, such as anger, which can compromise effective peer interaction, collaborative learning, and, in turn, diminish the quality of group discussions. However, this raises the question of whether anger always negatively affects collaborative learning. Most studies on negative emotions are related to test anxiety or boredom, while the impacts of emotions such as anger on learning are less explored. Especially within computer-supported collaborative learning (CSCL), there is limited research on how anger impacts students’ collaborative activities and learning. To address these issues, this paper aims to explore the potential relationship between anger and its impact on students’ collaborative discourse in a hybrid game-based simulation. Our findings suggest that anger has the potential to facilitate diverse and productive collaborative discussions. Students, driven by their anger, delved deeper into game mechanics, linked concepts to real-life situations, and employed various forms of logical reasoning to substantiate their opinions. However, the moment a student exhibited “tilting“ behavior, the quality of their collaborative discussions plummeted. Our findings provide important preliminary insights into the concept of “tilting” within immersive collaborative learning games and how it may manifest; they also offer guidance on the timing for educators’ intervention in collaborative discourse when anger arises among students.
Learning to deal with hate speech: An online collective intelligence experiment on the Collective Learning platform
Background: Online hate speech on social networks and the Internet is an increasingly pervasive phenomenon to which both children and adolescents are exposed. Objective: Our study’s main objective was to ascertain whether collective intelligence can improve their handling of hate speech. Methods: We conducted the study on the Collective Learning platform, comparing results between three groups of Spanish adolescents aged 15–16 years. The groups were of different sizes: one large group (G1, n = 123) and two smaller groups (G2, n = 18; G3, n = 23). Results: The experiment showed that the conditions for the emergence of collective intelligence were met within the large group (G1) but not in the two small groups (G2 and G3). The large group, as a collective, acquired capacities to deal with hate speech; however, this did not occur in the two smaller groups. Conclusions: Our study explains how the emergence of collective intelligence in online environments helps group members acquire a series of competencies. In particular, collective intelligence can help adolescents learn to deal with hate speech.
A quarter century of advances in collaboration sensing: a comprehensive review of high-frequency metrics and their connection to collaborative constructs from 2000 to 2025
The increasing availability of multimodal sensing technologies has opened new avenues for studying human interactions. However, there remains a lack of systematic synthesis regarding which multimodal metrics are most predictive of productive collaborations. This study addresses this gap by conducting a systematic literature review of 163 studies published since 2000. Grounded in the theoretical framework of multimodal collaboration analytics (MMCA; Schneider et al., 2022), we examine how different data modalities—verbal, gaze, body, head, log, and physiological—are used to assess collaboration. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework (Liberati et al., 2009), we categorize studies on the basis of the types of collaborative indicators, the metrics extracted from multimodal data, and the methods used to establish relationships between them. We find several gaps, including an over-representation of lab-based studies with small sample sizes, reliance on simplistic individual or group synchrony metrics, and a lack of standard indicators for collaboration. We discuss related Grand Challenges for MMCA, including scaling up research through field-based studies, developing interpretable models that contribute to theory, computing sophisticated sensor-based metrics that better capture the temporal dynamics of interaction, and designing interventions that support collaboration using fine-grained, high frequency sensor data.
Age-related differences in explanatory activities during collaborative learning with concept maps: experimental investigation using epistemic network analysis
Collaborative learning deepens understanding by elaborating knowledge and facilitating memory-related information processing through interactions with others. In computer-supported collaborative learning (CSCL), mechanisms identified in collaborative learning are scaffolded through tools such as group awareness and scripted collaboration. While collaborative learning is considered effective, it remains unclear how older adults learn in collaborative environments using concept maps, and how cognitive decline may hinder their learning. Therefore, this study investigates differences between younger and older adults in collaborative learning with concept maps, focusing on learning performance, concept map performance, and the collaborative learning process. Learning performance was assessed using test scores, concept map performance through concept map evaluations as a tool for externalizing knowledge, and the collaborative process using the Interactive-Constructive-Active-Passive (ICAP) framework, which captures cognitive engagement. Results showed that younger adults had higher learning performance than older adults, while older adults showed no significant improvement, indicating a lack of learning gain. Similarly, younger adults outperformed older adults in concept map performance, and no improvement was observed in older adults for concept map scores. This suggests that older adults found it more difficult to elaborate knowledge, such as integrating new information. Regarding the collaborative learning process, younger adults were more likely to engage at the active, while older adults showed higher engagement at the constructive and interactive levels. Epistemic network analysis (ENA) revealed stronger connections between constructive and interactive behaviors in younger adults, and between active and interactive behaviors in older adults. These findings suggest that while younger adults progressively deepen their engagement during collaborative learning, older adults may require the reactivation of memory to engage in elaboration. These results offer insights into designing effective CSCL environments tailored to the learning needs of older adults.
Editorial notes: Mechanisms as a unifying construct for CSCL research
Talk to the machine: Unleashing the potential of AI to scale dialogic education and reduce polarization
Dialogic education is largely advocated as a means to promote dialogue and reduce polarization. Chatbots based on large language models (LLMs) carry the potential to scale dialogic education by serving as conversation partners and sustaining a dialogic space on various topics. They combine human-like conversational abilities with machine patience. To explore this potential, we fine-tuned an LLM-based chatbot, LlamaLo, using a corpus of productive discussions. We analyzed ten discussions with LlamaLo on contentious topics, such as liberalism and cultural appropriation. Our findings show that LlamaLo effectively opens dialogic spaces by questioning interlocutors’ assumptions, presenting alternative perspectives, and providing relevant knowledge. However, challenges, such as negative tone and bias, could undermine the dialogic space and should be addressed computationally and pedagogically. We conclude that dedicated LLM-based chatbots have the potential for enhancing dialogic education and enabling seamless scripting responsive to real-time needs.
Understanding collaborative programming dynamics: The role of prior knowledge, engagement and ICAP learning modes
Understanding how multiple dimensions of learning engagement co-develop during collaborative programming remains a critical challenge. Drawing on the four-dimensional engagement framework encompassing behavioral, cognitive, emotional, and social components, this study employs multimodal learning analytics (MMLA) to investigate the dynamic interplay among engagement dimensions, prior knowledge, and leadership type in a university-level collaborative programming course. Group-level ICAP (Interactive, Constructive, Active, Passive) modes were coded from multimodal interaction data, while learning engagement was assessed via integrated behavioral, cognitive, emotional, and social indicators. Findings reveal that prior knowledge supports individual task execution but contributes to collaborative engagement only under strong leadership. The three core dimensions of learning engagement—behavioral, cognitive, and social—were found to be significantly interrelated, reflecting a tightly coupled system of action, thinking, and peer interaction. In contrast, emotional engagement showed weak or inconsistent correlations with the other dimensions. Furthermore, interactive discourse fostered richer engagement and higher achievement, with group formation strategies moderating these effects through the interplay of prior knowledge and leadership type. While ICAP modes and overall learning engagement were strongly correlated—partly reflecting overlapping cognitive components—ICAP captured discourse patterns, whereas engagement scores aggregated multidimensional indicators across the task, suggesting complementary rather than redundant constructs. These findings advance engagement research by integrating discourse classification and multidimensional profiling, offering practical guidance on group formation, scaffolding, and real-time engagement monitoring to enhance collaborative programming pedagogy.
How children blend feedback in a mixed-reality environment for collective embodied learning
With the rapid development of emerging technologies in education, this research explored how children use teacher-, peer-, and technology-provided feedback together toward collective and embodied learning in a mixed-reality environment. In this study, we investigated how young children interact with feedback in a mixed-reality environment, Science through Technology Enhanced Play (STEP), a system that tracks students’ movement and turns their embodiments into characters on a shared screen. We used coding and interaction analysis to analyze data from three episodes from a curriculum about states of matter across two research sites. Our analysis demonstrated that as the curriculum progressed, children engaged with collective and blended feedback, i.e., input that leads to collective sense-making and liminal blending of multiple sources, in ways that enhanced collective agency over their inquiry. First, we focused on how children transitioned from individualized views of feedback to more collective views by blending multiple sources of feedback (from self, peers, teachers/researchers, and technology) to make sense of solid bonds. Second, we found that the children leveraged feedback from their peers, facilitators, and technology-provided representations to explore how the particles must behave collectively to form liquid bonds. Third, we saw how children engaged with feedback differently on the basis of their role (observing versus embodying) in an activity focused on making gas bonds. More than simply demonstrating the sophistication with which young children engage in collective inquiry-based learning through embodied and technology-enhanced play designs, our work also demonstrates how future learning environments with complex feedback structures (i.e., the coordination of multiple sources and multiple modalities by children working collaboratively) can be designed to support student inquiry and young children’s agency in blending feedback sources that they determine enhance their collective sense-making.
IRRODL
Online Learning and Opera: The Rise in Digital Vocal Master Classes and Workshops
Professionalism in opera singing depends on the quality of education. This study examines the teaching of opera singing online via master classes and seminars. Based on the present findings, master classes are effective in improving the voice technically and range-wise, whereas seminars are effective in helping opera singers achieve emotionality. These instructional methods were compared in terms of content variety, balance between theoretical and practical components, and their effect on performance artistry and technical skills. Those learning to sing opera through master classes reported experiencing improvements in memory (24%) and problem-solving (22%). Students who attended the online seminars reported improvements in concentration (28%) and memory (26%). Challenges include limited control over assignments, attendance, and assessment. This study highlights effective tools for online opera training, with potential for future comparisons of methods in developing vocal and instrumental skills.
Tutors’ Perspectives of Advancing Distance Learning Programs: A Comprehensive Understanding
This study critically examined tutors’ perspectives on advancing the academic development of teacher education programs delivered via open and distance learning (ODL) at Bangladesh Open University (BOU). Tutors play a pivotal role as frontline facilitators of instruction, yet their experiential insights are often underrepresented in institutional decision-making. Drawing on a constructivist paradigm and grounded theory methodology, this qualitative inquiry engaged 82 tutors across eight tutorial centres using open-ended survey questions. Through classical content analysis, eleven major themes emerged, including attendance in tutorial sessions, curriculum and module design, tutorial session frequency, physical resources, tutor professional development, and supervision of practice teaching. The findings reveal that tutors emphasise the need for structured learner engagement, participatory curriculum revision, robust infrastructural support, and institutional investment in tutor capacity-building. The study also highlights disparities between current program structures at BOU and international norms, suggesting the need for extended program duration and more integrated practicum experiences. Implications are drawn for institutional policy, academic design, and participatory governance in ODL. By foregrounding tutors’ voices, this study contributes to a more inclusive model of academic development and underscores what tutors perceive as the need to bridge the gap between policy directives and pedagogical realities in distance education.
Enhancing Team Performance in Hybrid-Flexible Course Learning: The Role of Team Communication and Atmosphere
Background: Hybrid-Flexible (Hyflex) courses have become a prominent teaching method post-COVID-19, offering students the flexibility to attend classes online, offline, or both. While this model promotes instructional continuity and student-centered learning, it presents challenges in managing effective team collaboration. Aims: to investigate the underexplored relationship between effective communication, team atmosphere, and team performance in Hyflex learning environments, the study seeks to provide novel insights into optimizing team dynamics and enhancing learning outcomes in flexible educational settings. Sample(s): The study involved 310 college students enrolled in the authors' Hyflex course.. Methods: An online survey was conducted using validated scales, Confirmatory Factor Analysis (CFA) and Multiple linear regression analyses were used to test the hypotheses. Results: Effective communication was found to significantly enhance team performance (β = 0.389, p < .001). Team atmosphere moderated this relationship (β = -0.118, p < .05), indicating that while a positive atmosphere generally supports performance, an overly positive atmosphere may reduce the critical engagement necessary for optimal outcomes. Conclusions: Both effective communication and a balanced team atmosphere are critical for maximizing team performance in Hyflex courses. Educators should foster communication strategies that encourage open dialogue and critical thinking while maintaining a supportive team environment. Overemphasis on maintaining positivity may hinder constructive critique and diminish performance.
Blended Learning Effectiveness and College Students’ Deep Learning Perceptions: The Community of Inquiry Perspective
Emerging technologies and innovative instructional methods have revolutionized education, making blended learning the new standard in the artificial intelligence era. However, poor integration of online and face-to-face learning has led to challenges such as superficial student engagement. This study developed a Community of Inquiry-based blended learning model and evaluated its effectiveness with 92 college students using a quasi-experimental approach. Over 16 weeks, the experimental group (n = 48) adopted the blended learning model, while the control group (n = 44) used traditional learning conditions. Learning effectiveness and deep learning perceptions were evaluated, revealing the blended learning group demonstrated superior learning effectiveness (d = 0.83) and reported higher deep learning perceptions (η2 = .05–.072) compared to the traditional learning group. These results provide valuable insights for educators aiming to design blended learning models that foster deep learning and improve overall learning effectiveness.
Psychometric properties of the Arabic version of the Unified Theory of Acceptance and Use of Technology (UTAUT-2012) Among Nursing Students
The integration of artificial intelligence (AI) into nursing education is essential for equipping future nurses with the skills required to navigate an increasingly technology-driven healthcare environment. This study aimed to validate the Arabic version of the Unified Theory of Acceptance and Use of Technology (UTAUT-2012) model in assessing factors influencing nursing students’ acceptance and use of AI in healthcare education. A cross-sectional pilot study was conducted with 200 nursing students to evaluate the psychometric properties of the Arabic-translated UTAUT (2012) instrument. Confirmatory factor analysis was performed using covariance-based structural equation modeling (CB-SEM) in SmartPLS (Version 4.1.0). Confirmatory factor analysis supported the construct validity of the nine UTAUT 2012 constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, habit, behavioral intention, and use behavior. All items showed acceptable factor loadings (> .5), composite and construct reliability (> 0.7), and average variance extracted (> 0.5). Discriminant validity was confirmed using the Fornell-Larcker criterion and the heterotrait-monotrait ratio. The findings offer valuable insights into the factors influencing Arabic-speaking nursing students’ acceptance and use of AI in healthcare education, supporting the model’s validity in this cultural context.
Assessment of Learner Engagement and Expert Evaluations of AI-Generated Versus Human-Created Interactive Content in an Online Course
Generative artificial intelligence (GenAI) has introduced a novel aspect to educational methodologies and sparked fresh dialogues regarding the creation and evaluation of instructional resources. This project seeks to investigate the impact of GenAI on the development and assessment of online course materials and learners’ engagement with these materials in the online learning environment. The study analyzed GenAI-generated multiple-choice questions, fill-in-the-blank exercises, and true-false activities during 3 weeks of a 14-week online course. Subject matter experts assessed these documents in regards to content, relevance, and clarity. Data was collected through an online form with open-ended questions. The interactions of learners with the GenAI-created learning activities were analyzed using log records of the learning management system and compared to the content provided by the course instructor regarding interaction levels. The study’s conclusions elucidate the capability of GenAI technologies to produce course-specific content and their efficacy in education. We stress that human specialists’ critical evaluations play a crucial part in improving the pedagogical validity of GenAI-powered learning materials. Further research into topics including the ethical dimension, the effect on academic achievement, and student motivation is recommended.
Insights from an Umbrella Review of Flipped Learning in Higher Education
There is a noticeable growth in the number of systematic reviews published in open, distance, and digital education (ODDE), with a growing focus on flipped teaching and learning, particularly in higher education, emphasizing the need to consolidate evidence and findings under one comprehensive review. This umbrella review aims to thoroughly understand the current state of flipped learning in higher education and pinpoint research gaps, analyzing 23 systematic reviews published between 2018 and 2022 from three international databases: Web of Science, Education Source, and Scopus. It delves into publication and authorship patterns while synthesizing key insights. The thematic scope of the reviews reveals that many were focused on the effectiveness of flipped learning and teaching interventions, as well as learning design. The review explored theories guiding practice and research, instructional design considerations, and the application of flipped classrooms in various fields of study. It also examined the reported challenges of the flipped classroom model. As there are a scarcity of theoretical frameworks and a lack of detailed information on the pedagogical challenges of this model, recommendations are presented to enhance research and practice of flipped teaching and learning. The results of this umbrella review provide valuable insights to guide research in future and improve the quality of systematic reviews in the field of ODDE in general and flipped teaching and learning in particular.
Mobile Learning in Mathematics: Benefits, Challenges, Strategies, and Proponents’ and Opponents’ Views
Integrating mobile learning technologies in mathematics education in light of the pandemic, natural disasters, and recent technological developments is a significant shift in practice. This research applied the systematic review approach to assess the impact of mobile learning aids on students’ learning attitudes, motivation, and performance in mathematics. Based on a synthesis of peer-reviewed articles published from 2010 to 2024, this paper focuses on five themes: (a) student and educator experience and engagement, (b) modification in instructional practices, (c) learning and achievements, (d) opportunities, challenges, and strategies, and (e) mobile learning perceptions. The review reveals that mobile learning enhances students’ essential and higher-order thinking skills by establishing interactivity and learning through inquiry. However, other factors, such as the unequal distribution of resources and technology, still pose a challenge. To address these issues, it is suggested that future policies should focus on improving infrastructure, digital competency, and professional learning for instructors. Further, it is essential for institutions to engage faculty members in collaborative platforms to enhance their practice and have secure measures for data protection. This systematic review emphasizes the need for the researcher to conduct these evaluations over time to gauge the long-term impact of mobile learning and to look at the effects of this form of learning on different groups of students. It also concludes that future studies need to pay attention to ethical concerns and ways to provide equal opportunities in integrating technology into mathematics education for all students.
Book Review: Brave New Words: How AI Will Revolutionize Education (and Why It’s a Good Thing)
Salman Khan’s Brave New Words emerges at a pivotal moment in educational history, when artificial intelligence (AI) is alternately celebrated as a transformative force and denounced as a threat to the human dimensions of teaching. Using his rich experience as the founder of Khan Academy, a globally renowned non-profit organization producing freely available educational videos and exercises, Khan weaves a conversational narrative that eschews dry technical jargon in favor of vivid case studies and practitioner anecdotes. This book targets readers who are new to AI, whether as parents, teachers, or education policy makers. Rather than serving as a step-by-step guide or an exhaustive chronicle of AI’s evolution, the book offers a practitioner’s reflection on how emerging technologies can be adapted to align with institutional goals and real-world classrooms. Khan brings readers into his conversations with education innovators. The author’s enthusiasm for AI technology in education sometimes outpaces a deeper engagement with its long-term social and pedagogical implications.
Book Review: Resisting the Dehumanization of Refugees
Editorial – Volume 26, Issue 4
Editorial – Volume 26, Issue 4
Internet and Higher Education
Journal of Computer Assisted Learning
Journal of Computing in Higher Education
Examining group dynamics and composition characteristics with HLM in online collaborative instructional planning among pre-service teachers
This study investigated the relationships among socio-emotional climate, positive interdependence, and group outcome among pre-service teachers participating in an eight-week online collaborative instructional planning (CIP) project. Furthermore, it examined the moderating effects of group composition – gender and group history – on these relationships. Participants completed the Group Processes Scale assessing their perceptions of socio-emotional climate and positive interdependence. Considering the nested data structure, hierarchical linear modeling (HLM) analyses were conducted to predict socio-emotional climate, positive interdependence, and group outcome. The study revealed a significant and strong positive relationship between socio-emotional climate and positive interdependence, indicating that each mutually enhances the other in online CIP. Both socio-emotional climate and positive interdependence were significant predictors of group outcome, while their effects were especially evident in “all-male” groups, as well as in groups with no prior collaboration history, suggesting that group composition factors can amplify the benefits of group dynamics. These findings underscore the importance of fostering both positive interdependence and a strong socio-emotional climate, while strategically considering group composition to enhance the success of online CIP.
The AI disruption in engineering education: an analysis of changing student norms through cultural historical activity theory
This article explores the transformative impact of generative Artificial Intelligence (GenAI) on engineering education from a student perspective. Employing Cultural-Historical Activity Theory (CHAT), the study analyzes how GenAI challenges and changes established norms, and practices in and outside the classroom. Through thematic analysis of interviews with 25 students from a technical university in Northern Europe, we identify four themes of challenges or undergoing transformation due to GenAI: (1) the self-directiveness of students, (2) the objectives of learning, (3) the role of the teacher, and (4) the ethical aspects. The study reveals that participating students are developing new implicit rules for using GenAI to enhance their skills and understanding. These changes are driven by contradictions between traditional academic tools and the new expectations for self-directed and efficient learning support. While these students demonstrate awareness of GenAI’s flaws and the challenges for academic integrity, they appreciate the immediate and personalized support provided by GenAI, which contrasts with the slower, more dependent nature of teacher interactions. This shift in expectations is leading to a re-evaluation of the division of labor between these students and their teachers. The study concludes by discussing the implications for the investigated educational practice and the potential development of theory, emphasizing the need for similar engineering education institutions to respond to the specific challenges and transformations observed in this context.
Digital social multitasking in educational contexts: a study of motives and task relevance in college classrooms
The present study examined how college students’ motives for media multitasking and the task relevance of their multitasking behavior relate to their academic and psychosocial well-being during in-class group activities. College students (N = 262) completed surveys assessing their media multitasking motives, task relevance of media multitasking behaviors, academic engagement, classroom sense of belonging, and loneliness. Factor analysis identified three motives for media multitasking (i.e., information, connection, and boredom/habit) and two task-relevant multitasking behaviors (i.e., on-task and off-task). The path analysis revealed that while on-task media multitasking was not associated with any outcomes, off-task media multitasking was associated with lower academic engagement, despite being the less frequent behavior. Among the motives, the information motive was associated with higher academic engagement and classroom sense of belonging, regardless of students’ task-relevant media multitasking behaviors. The connection motive was related to greater on-task media multitasking but was not associated with off-task media multitasking or any outcomes. The boredom and habit motive was indirectly related to lower academic engagement through increased off-task media multitasking. Additionally, the total effects from the habit and boredom motive was associated with lower sense of belonging and higher loneliness, although neither on-task and off-task media multitasking were significant mediators. These findings suggest that students’ motivations for media multitasking during classroom group activities may be more influential than the task relevance of their multitasking behaviors in determining academic and psychosocial outcomes.
The significance of personality traits, collaborative attitudes, and group composition during collaborative online international learning (COIL): a mixed methods study
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.
How can internet-specific epistemic beliefs be cultivated? The role of information searching, the study process and online academic help-seeking
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.
Bridging educational equity gaps: expanding the CHAT-ACTS framework for personalized GenAI chatbots in higher education
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.
What makes ALEKS learning successful?: influences of prior knowledge, learning time, self-efficacy, and resource management on learning achievement
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.
A note from the advisory board
Using design-based research to gauge the impact of an open educational resource repository to support online teaching and learning
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.
Special issue on equity of artificial intelligence in higher education
The design and effectiveness of online collaborative work
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.
Teachers’ readiness for blended learning, digital competences and digital instructional material development self-efficacy
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.
Directions for navigating critical AI literacy in teacher education
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.
Examining non-traditional online learners’ ownership of learning in the context of ChatGPT-facilitated design
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.
Adaptive teaching in higher education: real-time design patterns for educator-led instruction
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.
Applications of human–AI interaction to optimize teaching workload and improve student writing
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.
Can generative AI further the classroom interactional justice?
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.
Understanding the pathways to online learner engagement and satisfaction in higher education: the function of digital literacy and dialogue
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.