International Journal of Educational Technology in Higher Education最新文献

The thriving technology penetration in all aspects of today’s life and deficiency of traditional pedagogies necessitate wise adoption of modern approaches in the educational context. As a few studies concerned the simultaneous application of classical educational theories with modern technological pedagogy, the present researchers launched General English (GE) courses enjoying the consolidation of Brain-Based Computer-Assisted-Language-Learning (BBCALL) and Flipped-Model (FM) with the aid of the Learning Management System (LMS) for fourteen 150-min sessions to explore their impact on vocabulary learning and reading comprehension (RC). In this pre/post-test experimental study, conducted in coeducational GE courses of a state university, 61 homogenous non-English major bachelors, selected via the convenience-sampling technique and screened by standard RC and GE-VOC tests, participated. Articulate Storyline software was used to develop intentional instructional content according to 12 BBL principles. BBCALL was the common aspect and in-class content attainment of non-flipped versus in-class content engagement and formative quizzes of flipped courses were the distinguishing features of the applied treatments. The statistical analyses of this action research demonstrated significantly meaningful outperformance of flipped BBCALL participants in vocabulary learning ((sig=0.001)) and RC ((sig=0.033)). To enhance results interpretation precision, gender was considered in groups’ differences. Although females in flipped course meaningfully outperformed on RC, male partakers of flipped course experienced the most meaningful improvement in VOC learning. Additionally, low-proficient learners benefited the most from such a self-paced and learner-centered education. The findings suggest that flexible instructional materials and effective tech integration could facilitate the improvement of higher-order thinking, creative problem-solving, and scaffolding.

Increasing student numbers, heterogeneity and individual biographies lead to a growing need for personalized support. To meet these challenges, an Individual Digital Study Assistant (IDSA) provides features to help students improve their self-regulation and organizational skills to achieve individual study goals. Based on qualitative expert interviews, a quantitative student survey, and current literature we derived requirements for an IDSA. Based on them, we designed, developed, and implemented a first IDSA prototype for higher education institutions (HEI). We continuously evaluated the prototype within different workshops and analyzed the usage data to improve it further in three enhanced prototypes. Based on this iterative process, we derived guidelines for an IDSA design and development. Accordingly, the framework, project management, content, team selection, team development, team communication, marketing, and student habits are important to consider. The guidelines advance the knowledge base of IDSA in HEI and guide and support practitioners in the design, development, and implementation of IDSA in HEI.

The complex trajectories of higher education students are deviations from the regular path due to delays in completing a degree, dropping out, taking breaks, or changing programmes. In this study, we investigated degree changing as a cause of complex student trajectories. We characterised cohorts of students who graduated with a complex trajectory and identified the characteristics that influenced the time to graduation. To support this predictive task, we employed machine learning techniques such as neural networks, support vector machines, and random forests. In addition, we used interpretable techniques such as decision trees to derive managerial insights that could prove useful to decision-makers. We validated the proposed methodology taking the University of Porto (Portugal) as case study. The results show that the time to degree (TTD) of students with and without complex trajectories was different. Moreover, the proposed models effectively predicted TTD, outperforming two benchmark models. The random forest model proved to be the best predictor. Finally, this study shows that the factors that best predict TTD are the median TTD and the admission regime of the programme of destination of transfer students, followed by the admission average of the previous programme. By identifying students who take longer to complete their studies, targeted interventions such as counselling and tutoring can be promoted, potentially improving completion rates and educational outcomes without having to use as many resources.

Little research has been conducted to investigate relationships between students’ community of inquiry (CoI) perceptions and their attitudes towards reading circles (ARC). To bridge the gap this quantitative research reports a cohort of Chinese students’ perceptions of CoI and its prediction for their attitudes towards reading circles (RC) in English as a Foreign Language learning. The researchers administered and collected 202 valid questionnaires. They analyzed the correlations between CoI and ARC dimensions, did regression analyses and came up with insightful findings. It’s found that, concerning CoI, students have more positive perceptions of teaching and cognitive presences than of social presence. Meanwhile teaching presence, cognitive presence, social presence of CoI and usefulness, affect, and behavior of ARC are closely correlated. CoI teaching presence and cognitive presence are significant predictors for usefulness and behavior of ARC, while the combination of the three CoI presences predicts affect of ARC. Investigating the relationships between a longstanding CoI framework and a fledging ARC scale provides great implications for Chinese blended EFL teaching, especially in reading courses.

The aim of this study is to gain insight into the interplay between attitudes, motivation, learning engagement, and perceived learning outcomes in massive open online courses (MOOCs). An online survey was administered to 232 MOOC learners. This study provided comprehensive explanations for individual differences in learning engagement and perceived learning outcomes in MOOCs with a modified model of the expectancy-value theory of achievement motivation. The structural equation modeling revealed that attitudes served as a precursor of participation in MOOCs that significantly influenced self-efficacy, intrinsic value, and task effort cost; self-efficacy and intrinsic value were positively associated with both learning engagement and perceived learning outcomes, while attitudes toward MOOC learning was positively related to perceived learning outcomes only. Furthermore, the mediation analyses highlighted that intrinsic value was a powerful mediator, which positively influenced the effects of attitudes and self-efficacy on learning engagement and perceived learning outcomes. The moderation analyses discovered that task effort cost moderated the effects of attitudes on learning engagement and perceived learning outcomes. Curriculum designers and instructors could benefit from this study to understand what rationales drive individuals to be engaged in MOOC learning and to reach greater perceived learning outcomes in MOOCs.

Although the field of Artificial Intelligence in Education (AIEd) has a substantial history as a research domain, never before has the rapid evolution of AI applications in education sparked such prominent public discourse. Given the already rapidly growing AIEd literature base in higher education, now is the time to ensure that the field has a solid research and conceptual grounding. This review of reviews is the first comprehensive meta review to explore the scope and nature of AIEd in higher education (AIHEd) research, by synthesising secondary research (e.g., systematic reviews), indexed in the Web of Science, Scopus, ERIC, EBSCOHost, IEEE Xplore, ScienceDirect and ACM Digital Library, or captured through snowballing in OpenAlex, ResearchGate and Google Scholar. Reviews were included if they synthesised applications of AI solely in formal higher or continuing education, were published in English between 2018 and July 2023, were journal articles or full conference papers, and if they had a method section 66 publications were included for data extraction and synthesis in EPPI Reviewer, which were predominantly systematic reviews (66.7%), published by authors from North America (27.3%), conducted in teams (89.4%) in mostly domestic-only collaborations (71.2%). Findings show that these reviews mostly focused on AIHEd generally (47.0%) or Profiling and Prediction (28.8%) as thematic foci, however key findings indicated a predominance of the use of Adaptive Systems and Personalisation in higher education. Research gaps identified suggest a need for greater ethical, methodological, and contextual considerations within future research, alongside interdisciplinary approaches to AIHEd application. Suggestions are provided to guide future primary and secondary research.

This study utilised students' online engagement, digital technology attitude, digital literacy, and self-efficacy theories to develop and test a model connecting these factors within a regional university in Australia. A field survey collected data from 110 first-year students. AMOS 28 was employed for measurement and structural model path analysis. The study initially examined the impact of students' attitudes and digital literacy on their self-efficacy. Subsequently, the effects of self-efficacy on five dimensions of online engagement were assessed: social, collaborative, cognitive, behavioural, and emotional. The findings indicated that positive student attitudes and digital literacy significantly contributed to self-efficacy, which, in turn, positively affected the engagement dimensions. This suggests that when designing and facilitating online, blended, or technology-enhanced courses in higher education, educators should pay attention to various elements of engagement. The study highlights the importance of considering students' attitudes and digital literacy in fostering self-efficacy and enhancing online learning engagements. Further research and implications for future studies are also recommended.

The COVID-19 pandemic caused a rapid seismic shift to online delivery in otherwise face-to-face higher education settings worldwide. This quantitative research study sought to investigate the effect of different delivery styles and assessment types on student outcomes. Specifically, grades achieved by first year undergraduate Biological Science students at a UK Higher Education institution were compared from seven modules across two different academic years, namely 2018–2019 and 2020–2021. The academic year 2018–2019 was delivered in the traditional face-to-face manner whereas the 2020–2021 method of delivery was via blended learning. The results showed that four of the seven modules were negatively affected by the transition from face-to-face to blended delivery (p < 0.05, T-test). One module was unaffected (p > 0.05, T-test) and the remaining two modules were positively affected (p < 0.05, T-test). However, the percentage of students requiring reassessments increased with blended learning delivery although this was not significant (p < 0.05, T-test). In summary, the majority of individual module marks decreased with blended learning compared to face-to-face delivery, with an associated increase in required reassessments. Although there are positive benefits to incorporating an element of online learning for students, it is important to utilise this information in future module delivery planning to support the varying student cohorts of the future.