This journal aims to: provide a vehicle for scholarly presentation and exchange of information between professionals, researchers and practitioners in the technology-enhanced education field; contribute to the advancement of scientific knowledge regarding the use of technology and computers in higher education; and inform readers about the latest developments in the application of information technologies (ITs) in higher education learning, training, research and management.


The Globethics Library contains vol. 13(2016) to current.

Recent Submissions

  • A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour

    Melissa Bond; Hassan Khosravi; Maarten De Laat; Nina Bergdahl; Violeta Negrea; Emily Oxley; Phuong Pham; Sin Wang Chong; George Siemens (SpringerOpen, 2024-01-01)
    Abstract 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.
  • Face-to-face vs. blended learning in higher education: a quantitative analysis of biological science student outcomes

    Claire V. Harper; Lucy M. McCormick; Linda Marron (SpringerOpen, 2024-01-01)
    Abstract 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.
  • Students' digital technology attitude, literacy and self-efficacy and their effect on online learning engagement

    Seyum Getenet; Robert Cantle; Petrea Redmond; Peter Albion (SpringerOpen, 2024-01-01)
    Abstract 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.
  • Integrating chatbots in education: insights from the Chatbot-Human Interaction Satisfaction Model (CHISM)

    Jose Belda-Medina; Vendula Kokošková (SpringerOpen, 2023-12-01)
    Abstract Recent advances in Artificial Intelligence (AI) have paved the way for the integration of text-based and voice-enabled chatbots as adaptive virtual tutors in education. Despite the increasing use of AI-powered chatbots in language learning, there is a lack of studies exploring the attitudes and perceptions of teachers and students towards these intelligent tutors. This study aims to compare several linguistic and technological aspects of four App-Integrated Chatbots (AICs) and to examine the perceptions among English as a Foreign Language (EFL) teacher candidates. In this mixed-methods research based on convenience sampling, 237 college students from Spain (n = 155) and the Czech Republic (n = 82) interacted with four AICs over a month, and evaluated them following a rubric based on the Chatbot-Human Interaction Satisfaction Model. This scale was specifically designed to assess different linguistic and technological features of AICs such as response interval, semantic coherence, sentence length, and user interface. Quantitative and qualitative data were gathered through a pre-post-survey, based on the CHISM model and student assessment reports. Quantitative data were analyzed using SPSS statistics software, while qualitative data were examined using QDA Miner software, focusing on identifying recurring themes through frequency analysis. The findings indicated a moderate level of satisfaction with AICs, suggesting that enhancements in areas such as better adapting to learner needs, integrating interactive multimedia, and improving speech technologies are necessary for a more human-like user interaction.
  • Students’ perceptions of using ChatGPT in a physics class as a virtual tutor

    Lu Ding; Tong Li; Shiyan Jiang; Albert Gapud (SpringerOpen, 2023-12-01)
    Abstract The latest development of Generative Artificial Intelligence (GenAI), particularly ChatGPT, has drawn the attention of educational researchers and practitioners. We have witnessed many innovative uses of ChatGPT in STEM classrooms. However, studies regarding students’ perceptions of ChatGPT as a virtual tutoring tool in STEM education are rare. The current study investigated undergraduate students’ perceptions of using ChatGPT in a physics class as an assistant tool for addressing physics questions. Specifically, the study examined the accuracy of ChatGPT in answering physics questions, the relationship between students’ ChatGPT trust levels and answer accuracy, and the influence of trust on students’ perceptions of ChatGPT. Our finding indicates that despite the inaccuracy of GenAI in question answering, most students trust its ability to provide correct answers. Trust in GenAI is also associated with students’ perceptions of GenAI. In addition, this study sheds light on students’ misconceptions toward GenAI and provides suggestions for future considerations in AI literacy teaching and research.
  • Flipped classroom in higher education: a systematic literature review and research challenges

    Maria Ijaz Baig; Elaheh Yadegaridehkordi (SpringerOpen, 2023-11-01)
    Abstract Flipped learning has garnered substantial attention as a potential means to enhance student engagement, improve learning outcomes, and adapt to the evolving educational landscape. However, despite the growing interest and potential benefits of flipped learning, several challenges and areas of concern persist. This systematic literature review critically examines the implementation of the flipped classroom in higher education by focusing on the role of technologies and tools, pedagogical activities and courses, and existing challenges. Using a systematic approach, a total of 30 research articles published between 2014 and 2023 were chosen for the review. This study identified video creation tools, learning management systems (LMS), content repositories, collaborative platforms, podcasts, and online assessment tools as technologies that play a central role in the flipped classroom. Moreover, this study identifies specific pedagogical activities within different courses that contribute to the effectiveness of flipped learning in higher education. The implementation challenges that teachers and students may face in the flipped classroom were presented, and potential strategies to alleviate these challenges were provided. This study will contribute to a more comprehensive understanding of flipped learning's benefits, technologies and tools, challenges, and potential to improve higher education.
  • Beyond emergency remote teaching: did the pandemic lead to lasting change in university courses?

    J. Broadbent; R. Ajjawi; M. Bearman; D. Boud; P. Dawson (SpringerOpen, 2023-11-01)
    Abstract The COVID-19 pandemic significantly disrupted traditional methods of teaching and learning within higher education. But what remained when the pandemic passed? While the majority of the literature explores the shifts during the pandemic, with much speculation about post-pandemic futures, a clear understanding of lasting implications remains elusive. To illuminate this knowledge gap, our study contrasts pedagogical practices in matched courses from the pre-pandemic year (2019) to the post-pandemic phase (2022/2023). We also investigate the factors influencing these changes and the perceptions of academics on these shifts. Data were gathered from academics in a large comprehensive Australian university of varying disciplines through a mixed-methods approach, collecting 67 survey responses and conducting 21 interviews. Findings indicate a notable increase in online learning activities, authentic and scaffolded assessments, and online unsupervised exams post-pandemic. These changes were primarily driven by university-guided adaptations, time and workload pressures, continued COVID-19 challenges, local leadership, an individual desire to innovate, and concerns about academic integrity. While most changes were seen as favourable by academics, perceptions were less positive concerning online examinations. These findings illuminate the enduring effects of the pandemic on higher education, suggesting longer-term implications than previous studies conducted during the acute phase of the pandemic.
  • Exploring language needs of college transfer students with learning analytics: towards a more equitable experience

    Dennis Foung; Julia Chen; Kin Cheung (SpringerOpen, 2023-11-01)
    Abstract College transfer students are those who follow a different trajectory in their higher education journeys than traditional students, completing a sub-degree before pursuing a bachelor’s degree at a university. While the possibility of transferring makes higher education accessible to these students, previous studies have found that they face various challenges, from issues with course load to language challenges. This study aims to examine (1) the critical factors contributing to the success of transfer students in a language course; and (2) how transfer students perform better or worse than those who enter university directly. This study conducted learning analytics with 700 college transfer students in Hong Kong, retrieving their demographic and learning data from the learning management system and the university academic registry. The results suggest that English exam scores, current semester GPA, graduating GPA at community college and current course load are important predictors of transfer students’ success in language courses. This study also finds that transfer students have lower levels of language proficiency than direct entrants. It concludes with specific recommendations to make higher education more accessible to transfer students and suggestions on how to use learning analytics to track students with different trajectories.
  • Not quite eye to A.I.: student and teacher perspectives on the use of generative artificial intelligence in the writing process

    Alex Barrett; Austin Pack (SpringerOpen, 2023-11-01)
    Abstract Generative artificial intelligence (GenAI) can be used to author academic texts at a similar level to what humans are capable of, causing concern about its misuse in education. Addressing the role of GenAI in teaching and learning has become an urgent task. This study reports the results of a survey comparing educators’ (n = 68) and university students’ (n = 158) perceptions on the appropriate use of GenAI in the writing process. The survey included representations of user prompts and output from ChatGPT, a GenAI chatbot, for each of six tasks of the writing process (brainstorming, outlining, writing, revising, feedback, and evaluating). Survey respondents were asked to differentiate between various uses of GenAI for these tasks, which were divided between student and teacher use. Results indicate minor disagreement between students and teachers on acceptable use of GenAI tools in the writing process, as well as classroom and institutional-level lack of preparedness for GenAI. These results imply the need for explicit guidelines and teacher professional development on the use of GenAI in educational contexts. This study can contribute to evidence-based guidelines on the integration of GenAI in teaching and learning.
  • Role of AI chatbots in education: systematic literature review

    Lasha Labadze; Maya Grigolia; Lela Machaidze (SpringerOpen, 2023-10-01)
    Abstract AI chatbots shook the world not long ago with their potential to revolutionize education systems in a myriad of ways. AI chatbots can provide immediate support by answering questions, offering explanations, and providing additional resources. Chatbots can also act as virtual teaching assistants, supporting educators through various means. In this paper, we try to understand the full benefits of AI chatbots in education, their opportunities, challenges, potential limitations, concerns, and prospects of using AI chatbots in educational settings. We conducted an extensive search across various academic databases, and after applying specific predefined criteria, we selected a final set of 67 relevant studies for review. The research findings emphasize the numerous benefits of integrating AI chatbots in education, as seen from both students' and educators' perspectives. We found that students primarily gain from AI-powered chatbots in three key areas: homework and study assistance, a personalized learning experience, and the development of various skills. For educators, the main advantages are the time-saving assistance and improved pedagogy. However, our research also emphasizes significant challenges and critical factors that educators need to handle diligently. These include concerns related to AI applications such as reliability, accuracy, and ethical considerations.
  • AI-generated feedback on writing: insights into efficacy and ENL student preference

    Juan Escalante; Austin Pack; Alex Barrett (SpringerOpen, 2023-10-01)
    Abstract The question of how generative AI tools, such as large language models and chatbots, can be leveraged ethically and effectively in education is ongoing. Given the critical role that writing plays in learning and assessment within educational institutions, it is of growing importance for educators to make thoughtful and informed decisions as to how and in what capacity generative AI tools should be leveraged to assist in the development of students’ writing skills. This paper reports on two longitudinal studies. Study 1 examined learning outcomes of 48 university English as a new language (ENL) learners in a six-week long repeated measures quasi experimental design where the experimental group received writing feedback generated from ChatGPT (GPT-4) and the control group received feedback from their human tutor. Study 2 analyzed the perceptions of a different group of 43 ENLs who received feedback from both ChatGPT and their tutor. Results of study 1 showed no difference in learning outcomes between the two groups. Study 2 results revealed a near even split in preference for AI-generated or human-generated feedback, with clear advantages to both forms of feedback apparent from the data. The main implication of these studies is that the use of AI-generated feedback can likely be incorporated into ENL essay evaluation without affecting learning outcomes, although we recommend a blended approach that utilizes the strengths of both forms of feedback. The main contribution of this paper is in addressing generative AI as an automatic essay evaluator while incorporating learner perspectives.
  • Examining students’ course trajectories using data mining and visualization approaches

    Rabia Maqsood; Paolo Ceravolo; Muhammad Ahmad; Muhammad Shahzad Sarfraz (SpringerOpen, 2023-10-01)
    Abstract The heterogeneous data acquired by educational institutes about students’ careers (e.g., performance scores, course preferences, attendance record, demographics, etc.) has been a source of investigation for Educational Data Mining (EDM) researchers for over two decades. EDM researchers have primarily focused on course-specific data analyses of students’ performances, and rare attempts are made at the domain level that may benefit the educational institutes at large to gauge and improve their institutional effectiveness. Our work aims to fill this gap by examining students’ transcripts data for identifying similar groups of students and patterns that might associate with these different cohorts of students based on: (a) difficulty level of a course category, (b) formation of course trajectories, and, (c) transitioning of students between different performance groups. We have exploited descriptive data mining and visualization methods to analyze transcript data of 1398 undergraduate Computer Science students of a private university in Pakistan. The dataset includes students’ transcript data of 124 courses from nine distinct course categories. In the end, we have discussed our findings in detail, challenges, and, future work directions.
  • Are open educational resources (OER) and practices (OEP) effective in improving learning achievement? A meta-analysis and research synthesis

    Ahmed Tlili; Juan Garzón; Soheil Salha; Ronghuai Huang; Lin Xu; Daniel Burgos; Mouna Denden; Orna Farrell; Robert Farrow; Aras Bozkurt (SpringerOpen, 2023-10-01)
    Abstract While several studies have investigated the various effects of open educational resources (OER) and open educational practices (OEP), few have focused on its connection to learning achievement. The related scientific literature is divided about the effects of OER and OEP with regards to their contribution to learning achievement. To address this tension, a meta-analysis and research synthesis of 25 studies (N = 119,840 participants) was conducted to quantitatively investigate the effects of OER and OEP on students’ learning achievement. The analysis included course subject, level of education, intervention duration, sample size, geographical distribution, and research design as moderating variables of the obtained effects. The findings revealed that OER and OEP have a significant yet negligible (g = 0.07, p < 0.001) effect. Additionally, the analysis found that the obtained effect can be moderated by several variables, including course subject, level of education and geographical distribution. The study findings can help various stakeholders (e.g., educators, instructional designers or policy makers) in understanding what might hinder OER and OEP effect on learning achievement, hence accommodating better learning outcomes and more effective interventions.
  • Leveraging computer vision for adaptive learning in STEM education: effect of engagement and self-efficacy

    Ting-Ting Wu; Hsin-Yu Lee; Wei-Sheng Wang; Chia-Ju Lin; Yueh-Min Huang (SpringerOpen, 2023-09-01)
    Abstract In the field of Science, Technology, Engineering, and Mathematics (STEM) education, which aims to cultivate problem-solving skills, accurately assessing learners' engagement remains a significant challenge. We present a solution to this issue with the Real-time Automated STEM Engagement Detection System (RASEDS). This innovative system capitalizes on the power of artificial intelligence, computer vision, and the Interactive, Constructive, Active, and Passive (ICAP) framework. RASEDS uses You Only Learn One Representation (YOLOR) to detect and map learners' interactions onto the four levels of engagement delineated in the ICAP framework. This process informs the system's recommendation of adaptive learning materials, designed to boost both engagement and self-efficacy in STEM activities. Our study affirms that RASEDS accurately gauges engagement, and that the subsequent use of these adaptive materials significantly enhances both engagement and self-efficacy. Importantly, our research suggests a connection between elevated self-efficacy and increased engagement. As learners become more engaged in their learning process, their confidence is bolstered, thereby augmenting self-efficacy. We underscore the transformative potential of AI in facilitating adaptive learning in STEM education, highlighting the symbiotic relationship between engagement and self-efficacy.
  • Praxeological learning approach in the development of pre-service EFL teachers' TPACK and online information-seeking strategies

    Taibe Kulaksız (SpringerOpen, 2023-09-01)
    Abstract This study is purposed to implement and test a praxeological learning approach to enhance pre-service EFL teachers’ technological pedagogical content knowledge and online information-seeking skills. This study was conducted based on a convergent parallel mixed design involving thirty-seven sophomore pre-service EFL teachers. Multiple data collection tools were administered at the beginning and end of the course, and data were analyzed aligning with quantitative and qualitative methods complementarily. Active and decisive participation of the pre-service teachers shaped the course design following the sections of independent study, context orientation, and context-based study within the Technological Pedagogical Content Knowledge Framework. Findings showed that pre-service teachers’ technological pedagogical content knowledge and online information-seeking strategies of evaluation, selecting main ideas, and trial &amp; error were significantly improved. Praxeological learning, following the Technological Pedagogical Content Knowledge Framework step-by-step and highlighting context-sensitivity, scaffolded pre-service teachers’ knowledge construction cumulatively and provided them with authentic learning experiences. The praxeological learning approach can support long-term motivation for technology integration knowledge and skills acquisition for pre-service teachers’ future careers.
  • Social anxiety in digital learning environments: an international perspective and call to action

    Dirk Ifenthaler; Martin Cooper; Linda Daniela; Muhittin Sahin (SpringerOpen, 2023-08-01)
    Abstract The research focused on digital learning environments has identified various challenges for learners, such as technical problems, lack of community, motivation, self-regulation, self-efficacy, and social anxiety. Social anxiety is conceptualized as an emotional disorder that may impede achievement in higher education. The project reported here investigates N = 666 students' social anxiety in digital learning environments at four higher education institutions located in Australia, Germany, Latvia, and Turkey. This range of contexts allowed the research to cover a wide variety of cultural and institutional idiosyncrasies. Findings revealed different levels of social anxiety in higher education digital learning environments across countries and their cultural contexts. In addition, gender plays a significant role in social anxiety for peer interactions with female students reporting higher social anxiety than male students. The findings suggest that it is worth tertiary educators pausing to consider social anxiety's role in reducing interactions within digital learning environments. Additional research is required to establish the causes of social anxiety in digital learning environments and, as a result, to develop strategies to minimise its effect. Graphical Abstract
  • Acceptance of artificial intelligence among pre-service teachers: a multigroup analysis

    Chengming Zhang; Jessica Schießl; Lea Plößl; Florian Hofmann; Michaela Gläser-Zikuda (SpringerOpen, 2023-09-01)
    Abstract Over the past few years, there has been a significant increase in the utilization of artificial intelligence (AI)-based educational applications in education. As pre-service teachers’ attitudes towards educational technology that utilizes AI have a potential impact on the learning outcomes of their future students, it is essential to know more about pre-service teachers’ acceptance of AI. The aims of this study are (1) to discover what factors determine pre-service teachers’ intentions to utilize AI-based educational applications and (2) to determine whether gender differences exist within determinants that affect those behavioral intentions. A sample of 452 pre-service teachers (325 female) participated in a survey at one German university. Based on a prominent technology acceptance model, structural equation modeling, measurement invariance, and multigroup analysis were carried out. The results demonstrated that eight out of nine hypotheses were supported; perceived ease of use (β = 0.297***) and perceived usefulness (β = 0.501***) were identified as primary factors predicting pre-service teachers’ intention to use AI. Furthermore, the latent mean differences results indicated that two constructs, AI anxiety (z = − 3.217**) and perceived enjoyment (z = 2.556*), were significantly different by gender. In addition, it is noteworthy that the paths from AI anxiety to perceived ease of use (p = 0.018*) and from perceived ease of use to perceived usefulness (p = 0.002**) are moderated by gender. This study confirms the determinants influencing the behavioral intention based on the Technology Acceptance Model 3 of German pre-service teachers to use AI-based applications in education. Furthermore, the results demonstrate how essential it is to address gender-specific aspects in teacher education because there is a high percentage of female pre-service teachers, in general. This study contributes to state of the art in AI-powered education and teacher education.
  • University students’ intentions to learn artificial intelligence: the roles of supportive environments and expectancy–value beliefs

    Faming Wang; Ronnel B. King; Ching Sing Chai; Ying Zhou (SpringerOpen, 2023-08-01)
    Abstract Despite the importance of artificial intelligence (AI) for university students to thrive in the future workplace, few studies have been conducted to assess and foster their intentions to learn AI. Guided by the situated expectancy–value theory, this study adopted both variable- and person-centered approaches to explore the role of supportive environments and expectancy–value beliefs in fostering university students’ intentions to learn AI. The data were drawn from 494 university students. In Study 1, the variable-centered approach of structural equation modeling showed the critical role of supportive environments and expectancy–value beliefs in promoting students’ intentions to learn AI. In Study 2, the person-centered approach of latent profile analysis identified three subgroups of students based on their levels of supportive environments and expectancy–value beliefs. Consistent with Study 1, students who perceived more supportive environments and higher levels of expectancy–value beliefs had stronger intentions to learn AI. We also documented the influence of study of field, gender, and year level on students' perceptions of supportive environments, expectancy-value beliefs and intentions to learn AI. The implications of these findings in improving students’ intentions to learn AI are discussed.
  • Exploring student perceptions and use of face-to-face classes, technology-enhanced active learning, and online resources

    Joanne M. Lewohl (SpringerOpen, 2023-08-01)
    Abstract The current cohort of undergraduate students is often said to value technology and is assumed to prefer immersive, interactive, and personalized learning experiences. In contrast, many educators recognise the value of face-to-face classes and believe that attending class positively impacts student performance. A novel teaching strategy, including traditional lectures and interactive workshops using an educational technology platform were implemented in an undergraduate neurobiology course. Attendance in class and use of lecture capture recording were associated with improved student performance. Further, student attitudes toward the teaching strategy were evaluated via a survey. The survey respondents included those that regularly attended class and those that did not. Overall, irrespective of attendance, students thought that face-to-face classes were beneficial to their learning and the use of active learning activities helped them to understand the course content. The most common reasons for non-attendance in class were attributed to factors such as the class schedule, work and family commitments and were not related to the availability of class recordings and other online resources. In contrast, the most common reasons for attendance in class included the perceived benefit, the standard of teaching and the level of interest in the course. The novel teaching strategy had a positive impact on student learning, and can be used for in-person, online and asynchronous learning, providing a mechanism for educators to cater for students who wish to attend in-person classes as well as providing options for flexible delivery. Graphical Abstract
  • A familiar peer improves students’ behavior patterns, attention, and performance when learning from video lectures

    Zhongling Pi; Yi Zhang; Qiuchen Yu; Jiumin Yang (SpringerOpen, 2023-08-01)
    Abstract Synchronous online learning via technology has become a major trend in institutions of higher education, allowing students to learn from video lectures alongside their peers online. However, relatively little research has focused on the influence of these peers on students’ learning during video lectures and even less on the effect of peer familiarity. The present study aimed to test the various effects of peer presence and peer familiarity on learning from video lectures. There were three experimental conditions: individual-learning, paired-learning with an unfamiliar peer, and paired-learning with a familiar peer. ANCOVA results found that students paired with a familiar peer reported higher motivation in learning and more self-monitoring behaviors than those paired with an unfamiliar peer or who learned alone. Furthermore, students paired with both unfamiliar or familiar peers demonstrated better learning transfer than those who learned alone. Together, these results confirm the benefits of and support learning alongside a familiar peer during video lectures.

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