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Via AI video generators, it is possible to create educational videos with humanistic instructors by simply providing a script. The characteristics of video types and features of instructors in videos impact video engagement and, consequently, performance. This study aimed to compare the impact of human instructors and AI-generated instructors in video lectures on video engagement and academic performance. Additionally, the study aimed to examine students’ opinions on both types of videos. Convergent-parallel approach mixed method was used in this study. A total of 108 undergraduate students participated: 48 in the experimental group, 52 in the control group, and eight in the focus group. While the experimental group (AI-generated instructor) and control group (human instructor) watched 10 minutes of two videos each in two weeks, the students in the focus group watched both types of videos with human and AI-generated instructors. Data were collected through the Video Engagement Scale (VES) after the experimental process, and the Academic Performance Test as a pretest and posttest was administered in both groups. The findings of the experimental part revealed that learners’ video engagement was higher in the course with the human instructor compared to the course with the AI-generated instructor. However, the instructor type did not have a significant effect on academic performance. The results based on the qualitative part showed that students thought the AI-generated instructor caused distraction, discomfort, and disconnectedness. However, when the video lesson topic was interesting or when students focused on the video with the intention of learning, these feelings could be ignored. In conclusion, even in today’s conditions, there is no difference in performance between human and AI-generated instructors. As AI technology continues to develop, the difference in engagement is expected to disappear, and AI-generated instructors could be used effectively in video lectures.

This study proposes a hypothetical model combining the unified theory of acceptance and use of technology (UTAUT) with self-determination theory (SDT) to explore design professionals’ behavioral intentions to use artificial intelligence (AI) tools. Moreover, it incorporates job replacement (JR) as a moderating role. Chinese-speaking design professionals in regions influenced by Confucian culture were surveyed. An analysis of 565 valid cases with AMOS (Analysis of Moment Structures) supported the structural model hypothesis. The model explains 52.1% of the variance in behavioral intention to use (BIU), proving its effectiveness in explaining these variances. The results further validate the importance of performance expectancy (PE) over effort expectancy (EE) in influencing BIU. Additionally, it has been shown that the impact on intrinsic motivation (IM) and extrinsic motivation (EM) can be either amplified or diminished by anxiety about JR. For individuals experiencing higher levels of JR anxiety, there is a marked increase in IM. They may perceive adopting AI tools as an opportunity to enhance their skills and job security. Conversely, this anxiety also significantly boosts EM, as the potential for improved efficiency and productivity with AI use becomes a compelling incentive. These findings suggest new paths for academic researchers to explore the psychological impacts of AI on design professionals’ roles. For practitioners, especially in human resources and organizational development, understanding these dynamics can guide the creation of training programs that address job replacement anxiety.

The rapid advancement of artificial intelligence (AI) in education underscores transformative prospects for open and distributed learning, encompassing distance, hybrid, and blended learning environments. This qualitative study, grounded in narrative inquiry, investigates the experiences and perceptions of 141 preservice teachers engaged with AI, mainly through ChatGPT, over a 3-week implementation on Zoom to understand its influence on their evolving professional identities and instructional methodologies. Employing Strauss and Corbin’s methodological approach of open, axial, and selective coding to analyze reflective narratives, the study unveils significant themes that underscore the dual nature of AI in education. Key findings reveal ChatGPT’s role in enhancing educational effectiveness and accessibility while raising ethical concerns regarding academic integrity and balanced usage. Specifically, ChatGPT was found to empower personalized learning and streamline procedures, yet challenges involving information accuracy and data security remained. The study significantly contributes to teacher education discourse by revealing AI’s complex educational impacts, highlighting an urgent need for comprehensive ethical AI literacy in teacher training curricula. However, critical ethical considerations and practical challenges involving academic integrity, information accuracy, and balanced AI use are also brought to light. The research also spotlights the need for responsible AI implementation in open and distributed learning to optimize educational outcomes while addressing potential risks. The study’s insights advocate for future-focused AI literacy frameworks that integrate technological adeptness with ethical considerations, preparing teacher candidates for an intelligent digital educational landscape.

This experimental study investigates the impact of ChatGPT-simplified authentic texts on university students’ reading comprehension, inferencing, and reading anxiety levels. A within-subjects design was employed, and 105 undergraduate English as a foreign language (EFL) students engaged in both original and ChatGPT-simplified text readings, serving as their own controls. The findings reveal a significant improvement in reading comprehension scores and inferencing scores following ChatGPT intervention. However, no significant change in reading anxiety levels was observed. Results suggest that ChatGPT simplification positively influences reading comprehension and inferencing, but its impact on reading anxiety remains inconclusive. This research contributes to literature on the use of artificial intelligence (AI) in education and sheds light on ChatGPT’s potential to influence language learning experiences within higher education contexts. The study highlights the practical application of ChatGPT as a tool for helping students engage in authentic text readings by making text more comprehensible. Based on the findings, several multifaceted implications that extend to various stakeholders in the field of language education are provided.

Several barriers hinder students from producing clear and impactful written work. Writing assignments are often given on an individual basis, similar to homework, and without any assistance. Students in a classroom context have access to both their classmates and the teacher while they are working in groups or pairs as part of their assignments. The majority of students, however, are clueless about how to begin their homework assignments. The introduction of artificial intelligence in education may help solve this problem. The current research intended to demonstrate the effects of employing automated writing evaluation (AWE) in fostering learners’ writing skills, motivation to write, enjoyment of writing, and academic buoyancy in open and distributed English as a foreign language (EFL) learning. The participants were 86 intermediate EFL students from China. The participants in the experimental group (n = 44) received instruction and feedback from their teachers only; participants in the control group (n = 42) were exposed to their teachers’ instruction as well as AWE. The results of data analysis via one-way multivariate analysis of variance indicated that the participants in the experimental group outperformed their peers in the control group in motivation to write, enjoyment in writing, academic buoyancy, and academic success in writing. Further in-depth discussions proceed regarding the implications of the study.

Artificial intelligence (AI) revolutionizes education by fundamentally altering the methods of teaching and processes of learning. Given such circumstances, it is essential to take into account the mental and psychological well-being of teachers as the architects of education. This research investigated the links between teacher immunity (TI), work passion (WP), job satisfaction (JS), occupational well-being (OW-B) and psychological well-being (PW-B) in the context of AI-assisted language learning. In order to achieve this objective, 392 Iranian teachers of English as a foreign language (EFL) were given the Language Teacher Immunity Instrument, the Work Passion Scale, the Job Satisfaction Questionnaire, the Occupational Well-Being Scale, and the Psychological Well-Being at Work Scale. By using confirmatory factor analysis and structural equation modeling, the study identified and quantified the impacts of TI, WP, JS, OW-B, and PW-B via data screening. The findings emphasize the crucial role that TI and WP play in providing a balance in teachers’ JS, OW-B, and PW-B while applying AI in their language instruction. The broad ramifications of this research are explored.

This study aimed to present a comprehensive bibliometric analysis of 1,726 academic studies from among those indexed by the Web of Science database platform between 2013 and 2023, to provide a general framework for the concept of artificial intelligence in education (AIEd). Trends in publications and citations across countries, institutions, academic journals, and authors were identified, as well as collaborations among these elements. Several bibliometric analysis techniques were applied, and for each analysis, the motivations behind the execution and method of producing findings were documented. Our findings showed that the number of studies on the concept of AIEd has increased significantly over time, with the U.S. and China being the most common countries of origin. Institutions in the U.S. stand out from those around the world. Pioneering journals in education have also emerged as prominent in the field of AIEd. On the other hand, collaboration between authors has been limited. The study was supplemented with keyword analysis to reveal thematic AIEd concepts and to reflect changing trends. For those exploring artificial intelligence in education, our insights on popular topics offer valuable guidance toward greater understanding of the latest advancements and key research areas.

Artificial Intelligence (AI), as an innovation in technology, has greatly affected human life. AI applications such as ChatGPT have been used in different fields, particularly education. However, the use of AI applications to enhance undergraduate students’ academic emotions and test anxiety has not been appropriately investigated. This study addresses the effects of undergraduate students’ test anxiety and academic emotions. A total of 160 undergraduate students majoring in different fields of study were selected through convenience sampling and divided into control and experimental groups. Both groups received test anxiety and academic emotions scales at the onset of the treatment. The students assigned to the experimental group were trained to use ChatGPT and monitored for learning and doing their assignments outside the classroom during the semester. The two groups received the scales at the end of the semester, which lasted 16 weeks. Independent samples t-tests were used for analyzing the data. Results revealed that using AI-empowered applications significantly reduced the students’ test anxiety and negative academic emotions but enhanced their positive academic emotions. Students can use ChatGPT as an auxiliary instrument to overcome their negative emotions and enhance their educational attainment. Findings affect teachers, educational technologists, educational psychologists, and students.

Artificial intelligence (AI) has contributed to various facets of human lives for decades. Teachers and students must have competency in AI and AI-empowered applications, particularly when using online electronic platforms such as learning management systems (LMS). This study investigates the structural relationship between AI literacy, academic well-being, and educational attainment of Iranian undergraduate students. Using a convenience sampling approach, we selected 400 undergraduate students from virtual universities equipped with LMS platforms and facilities. We collected data using three instruments—an AI literacy scale, an academic well-being scale, and educational attainment scale—and analyzed the data using Smart-PLS3 software. Results showed that the hypothetical model had acceptable psychometrics (divergent and convergent validity, internal consistency, and composite reliability). Results also showed that the general model had goodness of fit. The study thus confirms the direct effect of AI on academic well-being and educational attainment. By measuring variables of academic well-being, we also show that AI literacy in China and Iran significantly affects educational attainment. These findings have implications for students, teachers, and educational administrators of universities and higher education institutes, providing knowledge about the educational uses of AI applications.

Artificial intelligence (AI) offers new possibilities for English as a foreign language (EFL) learners to enhance their learning outcomes, provided that they have access to AI applications. However, little is written about the factors that influence their intention to use AI in distributed EFL learning contexts. This mixed-methods study, based on the technology acceptance model (TAM), examined the determinants of behavioral intention to use AI among 464 Chinese EFL college learners. As to quantitative data, a structural equation modelling (SEM) approach using IBM SPSS Amos (Version 24) produced some important findings. First, it was revealed that perceived ease of use significantly and positively predicts perceived usefulness and attitude toward AI. Second, attitude toward AI significantly and positively predicts behavioral intention to use AI. However, contrary to the TAM assumptions, perceived usefulness does not significantly predict either attitude toward AI or behavioral intention to use AI. Third, mediation analyses suggest that perceived ease of use has a significant and positive impact on students’ behavioral intention to use AI through their attitude toward AI, rather than through perceived usefulness. As to qualitative data, semi-structured interviews with 15 learners, analyzed by the software MAXQDA 2022, provide a nuanced understanding of the statistical patterns. This study also discusses the theoretical and pedagogical implications and suggests directions for future research.

Artificial intelligence (AI) has undergone considerable advancement in the contemporary period and represents an emerging technology in higher education. Cultural contexts significantly shape individuals’ perceptions, attitudes, and behaviors, particularly in the realm of technology acceptance. By adopting a cross-cultural lens, this research explores the potential variations across Chinese and international students from diverse countries in terms of attitudes and their behavioral intentions toward AI use. With a technology acceptance model (TAM) framework, the research used a survey approach, employing questionnaires as the primary means of data collection. The data were then analyzed through structural equation modeling and descriptive statistics. A substantial discrepancy was found in the prevalence, attitudes, and behavioral intentions toward AI use between Chinese and international students. Findings further revealed a stronger effect of perceived ease of use on both attitudes and behavioral intentions among international students compared with their Chinese counterparts. Findings suggest that cultural backgrounds and prior technological exposure play intricate roles in shaping perceptions of AI technology. The study emphasizes the need for tailored educational strategies to regulate diverse cultural perspectives, provide language-specific support, and ensure user-friendly interfaces. These insights contribute to the evolving discourse on technology acceptance in higher education and offer practical implications for educators and institutions toward optimizing AI integration in pedagogical practices.

Learners might have several challenges while attempting to learn a second/foreign language. Learners of Chinese face linguistic, psychological, and educational challenges. The integration of technology, especially artificial intelligence (AI), into teaching foreign languages is a blessing for teachers and learners. This study delved into the auxiliary role of AI-powered applications in mitigating the linguistic, psychological, and educational challenges which non-Chinese learners face while learning Chinese/Mandarin language. Qualitative research was employed, and 20 teachers of Chinese language were selected through theoretical sampling. In-depth interviews were used for collecting data, and MAXQDA was used for thematic analysis. Findings revealed that AI-powered educational applications are useful for helping language learners overcome the commonly reported linguistic, psychological, and educational challenges which non-Chinese learners and teachers of Mandarin might encounter. Findings verify the effectiveness of AI-powered applications, such as ChatGPT, Poe, Brainly, and so forth, in helping teachers and learners of Chinese language learn grammar, structure, idioms, and cultural issues of Chinese language. Findings have implications for foreign language (Chinese) learners and teachers, educational technologists, as well as syllabus designers.

This study, which is quasi-experimental in nature, looks into how language learners’ willingness to communicate and engagement in English as a foreign language (EFL) classrooms are affected by Duolingo. The control and experimental groups comprised two complete classes with forty EFL students. To compare learner engagement and communication willingness scores before and after treatment, the study used independent samples t-tests. The results demonstrated the groups’ initial homogeneity by showing no discernible differences prior to the intervention. The results confirmed the effects on learner engagement, which showed significant gains in affective, cognitive, and behavioral domains, indicating Duolingo’s beneficial impact on engagement in general. Furthermore, the significant effect sizes observed confirmed Duolingo’s contribution to improved language attitudes, engagement, and communicative confidence. Compared to the control group, the experimental group’s willingness to speak, read, write, comprehend, and communicate generally improved in a manner that was statistically significant. The significant effect sizes demonstrate how well Duolingo works to improve different aspects of willingness to share. The study emphasizes the pedagogical tool’s adaptability and encourages teachers to integrate Duolingo for a comprehensive and technologically enhanced language learning experience. Practical implications arise for EFL teachers who use online learning resources.

The consideration of students’ emotional and psychological health is crucial to facilitate effective teaching and grading practices. This study set out to shed light on the interplay between self-esteem (S-E), cognitive-emotion regulation (CER), academic enjoyment (AE), and language success (LS) in artificial intelligence (AI)-supported online language learning. To this end, the foreign language learning self-esteem scale, the Cognitive Emotion Control Questionnaire, the foreign language enjoyment scale, and a researcher-made test were distributed to 389 English as a foreign language learners in China. Screening the data with confirmatory factor analysis and structural equation modeling, the effects of S-E, CER, AE, and LS were identified and quantified. These results highlighted the important function that online courses assisted by AI perform in enhancing students’ CER and AE. This implied that students who have cultivated a robust sense of self-efficacy are adept at effectively regulating their cognitive and affective processes in AI-supported language learning. Possible improvements in language education are discussed, as are the study’s broader implications.

The transformative impact of advancements in educational technology, particularly those powered by artificial intelligence (AI), on the landscape of education and the teaching profession has been substantial. This study explores the repercussions of AI-powered technologies on teachers’ autonomous behavior, digital burnout, and professional development. The study involved a cohort of 320 high school teachers in China segregated into control and experimental groups. The experimental group received instructions on AI-integrated applications and how they might be used in education. However, the teachers assigned to the control group did not receive information on the use of AI educational applications. Three distinct questionnaires probing autonomous behaviors, digital burnout, and online professional development were administered, and the ensuing data were analyzed using independent sample t-tests. The findings elucidate a discernible positive impact of AI-integrated technology intervention on teachers’ professional development and autonomous behaviors. The incorporation of AI-enhanced tools facilitated an augmentation in teachers’ professional growth and bolstered their independent and self-directed instructional practices. Notably, using AI-integrated technology significantly reduced teachers’ susceptibility to digital burnout, signifying a potential alleviation of stressors associated with technology-mediated teaching. This research provides valuable insights into the multifaceted effects of AI-powered technologies on educators, shedding light on enhancing professional competencies and mitigating digital burnout. The implications extend beyond the confines of this study, resonating with the broader discourse on leveraging technology to augment the teaching profession and optimize the learning environment.

This study examines the effects of corrective feedback (CF) on language learners’ writing anxiety, writing complexity, fluency, and accuracy, and compares the effectiveness of feedback from human teachers with an AI-driven application called Poe. The study included three intact classes, each with 25 language learners. Using a quasi-experimental design with pretest and posttest measures, one class received feedback from the teacher, one from the Poe application, and the third received no response to their writing. Data were generated though tests and a writing anxiety scale developed for the study. Data analysis, conducted using one-way ANOVA tests, revealed significant effects of teacher and AI-generated feedback on learners’ writing anxiety, accuracy, and fluency. Interestingly, the group that received AI-generated feedback performed better than the group that received teacher feedback or no AI support. Additionally, learners in the AI-generated feedback group experienced a more significant reduction in writing anxiety than their peers. These results highlight the remarkable impact of AI-generated CF on improving writing outcomes and alleviating anxiety in undergraduate language learners at East China University of Political Science and Law . The study demonstrates the benefits of integrating AI applications into language learning contexts, particularly by promoting a supportive environment for students to develop writing skills. Educators, researchers, and developers can use these findings to inform pedagogical practices and technological interventions to optimize the language learning experience in primary school settings. This research highlights the effectiveness of AI-driven applications in language teaching. It highlights the importance of considering learners’ psychological well-being, particularly anxiety levels, when developing effective language learning interventions.

This research study explored the perspectives of 20 educational technologists from four Saudi Arabian universities regarding the integration of AI-powered technology, particularly ChatGPT, into online higher education. The study used a qualitative research method that relied on the principles of theoretical sampling to select participants and conducted in-depth interviews to collect their insights. The approach taken for data analysis was thematic analysis, which uncovered a rich range of insights on both the challenges and opportunities associated with students’ use of AI-integrated technology in the context of online higher education. Ten significant challenges emerged that shed light on the complexities and intricacies of integrating AI-powered technology into educational environments. These challenges included issues related to technological infrastructure, pedagogical adaptation, and the need for comprehensive training programs to empower both teachers and learners. Additionally, eight threats were examined that highlighted concerns about data security, privacy, and potential risks associated with AI technology in educational institutions. This study not only provided a comprehensive overview of the current landscape of AI-integrated technology in Saudi Arabian higher education, but also provided valuable insights for education stakeholders, technologists, and policy makers. It underscored the necessity of proactive measures to mitigate challenges and threats while harnessing the opportunities presented by AI technology to enhance the quality and effectiveness of online higher education.

In education, the integration of artificial intelligence (AI) has presented opportunities to transform the dynamics of online learning. This study investigated the impact of an AI-powered application, namely ChatGPT, on the autonomy of Saudi Arabian primary students participating in online classes. It also explored how the implementation of Chat GPT influenced Saudi Arabian primary students’ autonomy. In this mixed-methods study, a quasi-experimental design assessed the impact of ChatGPT on learner autonomy among 250 Saudi Arabian primary students from six primary schools in Riyadh, Saudi Arabia. The quantitative analysis employed descriptive statistics and t-tests, while the qualitative data underwent interpretative phenomenological analysis. To ensure coding reliability, 20% of the codes were independently reviewed by an external coder, with a 94% inter-coder agreement coefficient reached through consensus. Findings revealed that ChatGPT significantly affected the participants’ perceptions of autonomy and its different dimensions. Qualitative data showed that AI-powered applications contributed to the students’ autonomy in 10 different ways. Participants also mentioned that AI-powered apps might have some negative consequences. This study has theoretical implications for redefining learner autonomy in the digital age and calls for the exploration of many facets of autonomy. Practical applications from this study include strategic integration of AI into online education, data security, and the need for orientation programs.

The release of ChatGPT has marked the dawn of a new information revolution that will transform how people communicate and make meaning. However, to date, little is known about the implications of ChatGPT for L2 composition instruction. To address this gap, the present study uses a systematic review design to synthesize available research on the educational potentials of ChatGPT as an instructional assistant, outline the implications of these potentials for L2 writing instruction, and discuss their practical applications. The findings, based on a meta-analysis of 42 research articles, demonstrate that ChatGPT can enhance L2 writing instruction by boosting learners’ motivation, automating instructional tasks, and offering instantaneous, personalized feedback to learners. These findings have important implications for harnessing the instructional potential of generative AI in L2 writing classes.

Pervasive internet use in higher education has rendered social anxiety a lesser problem in circumventing interactions. However, the attenuation of vitality in interactions still remains a contentious issue, especially in asynchronous online discussions. This study aims to elucidate how anonymity and social presence affect the relationship between avoidance of interaction and peer and content interaction in online discussions. In the true experimental study, we recruited 123 first-year university students, of which 62 were randomly assigned to the anonymous group, and 61 to the identified group. This paper adopts a moderated mediation model, in which anonymity and social presence are included as moderator and mediator, respectively. We revealed that the “straightforward” relationship between avoidance of interaction and peer and content interaction is actually highly complex. The results substantiate a full mediation of social presence and moderation of anonymity in favor of disclosed identity. This study accentuates the critical role of social presence in fostering interactions. The results also suggest that disclosing discussants’ identities might be useful during asynchronous interactions in online discussions for increasing social presence only if their avoidance of interaction is low; in the case of a high avoidance of interaction, anonymity should be preferred.

With technological advances, institutional stakeholders are considering evidence-based developments such as Curriculum Analytics (CA) to reflect on curriculum and its impact on student learning, dropouts, program quality, and overall educational effectiveness. However, little is known about the CA state of the art in Higher Education Institutions (HEIs), where dropout is a worldwide problem. Through a systematic literature review, this study summarizes 59 manuscripts about CA published until April 2024. The aim of this review is to identify: 1) WHERE CA was used; 2) WHO were the CA target stakeholders; 3) WHY CA was proposed; 4) WHAT types of data and what types of data gathering and analysis methods are employed; 5) HOW CA was designed, implemented and evaluated and what the stakeholders' role was; and 6) WHICH limitations and constraints affect CA and WHICH recommended practices could contribute to the CA success. Results from our review reveal a considerable number of CA solutions available. However, there is a need for more evidence on how CA solutions inform decision-making among various stakeholders. Thus, more longitudinal studies are needed, involving stakeholders not only in the design but also in the implementation and evaluation of CA solutions. At the same time, findings suggest that including multiple data sources can enrich the analysis and enable triangulation. Additionally, the lack of evidence on the role of CA in dropouts and decision-making in higher education institutions requires more future research on this aspect. Finally, researchers, practitioners, and decision-makers can use the findings obtained in this review to inform future research and practices on how to leverage CA to improve student learning, enhance the learning experience, and reduce student dropouts.

The study investigates how the 2×2 configuration of homogeneous and heterogeneous distributions of extraversion and prior knowledge influences group outcomes, including satisfaction, performance, and stability. Based on the standard deviation of extraversion and prior knowledge, groups were established to test experimentally, what form of grouping leads to best outcomes. The randomized controlled trial took place in the context of an online course with 355 prospective students, working in 82 groups. The two characteristics extraversion and prior knowledge were distributed algorithmically, either homogeneously or heterogeneously. Results showed no superiority of heterogeneous formation, yet there were systematic interaction effects by the experimental group formation on satisfaction and performance. Due to the increasing relevance of online groupwork, explorative results are reported and integrated. Ideas for future research on group formation as an important influencing factor are discussed. Findings supports knowledge about cooperative online learning by optimizing the selection of group members using a therefore implemented algorithm.

While extensive studies on informal online learning have been well documented to afford teachers’ collaborative learning and knowledge sharing, little is still known about their motivational factors regarding the continuance intention of informal online learning. To this end, an extended expectation confirmation model (ECM) was proposed including intrinsic and extrinsic motivation. The proposed research model and several hypotheses were empirically evaluated using questionnaire surveys with the valid data collected from 231 Chinese in-service teachers in the shared mobile learning community. The results consolidate the appropriateness of the extended ECM to explain teachers’ informal online learning continuance. Specifically, satisfaction is the major determinant of continuance intention, followed by perceived usefulness and intrinsic motivation. In addition, extrinsic motivation positively predicts perceived usefulness and confirmation. The results of this study provide some theoretical and practical implications into in-service teachers’ continuance intention of informal online learning.

This research explores preservice classroom teachers’ perceived importance, self-efficacy beliefs, participation frequencies and conceptions related to new literacies. The research is framed using a dual-level theory of new literacies. The participants of the study consisted of 364 preservice teachers studying in the department of primary education at two state universities in Turkey. The findings revealed that (i) while preservice teachers displayed high participation in new literacies requiring the consumption of information, they displayed low participation in information production activities; (ii) the importance preservice teachers attached to online or digital literacy activities and their self-efficacy beliefs significantly predicted their participation in these activities; (iii) preservice teachers’ conceptual understandings with regard to new literacies were compatible with their participation frequencies and perceived importance with regard to online or digital literacy activities. Some suggestions for preservice teachers’ skills, strategies, dispositions and practices related to new literacies are discussed.

Digital competence is considered to be a crucial learning outcome in education in the 21st century. In this context, research highlights the fact that the perceptions that instructors manifest about different aspects of Information and Communication Technologies (ICTs) condition these professionals’ behaviour towards these learning resources. In the same line, this study aims to analyse the effects that exist between a series of dimensions related to the perception of university teaching staff on the capacity of ICTs to respond to different needs of students, on perceived efficacy and attitudes towards these tools and, lastly, on active behaviour towards their use. To do so, 345 university instructors from the Spanish educational system filled in an online questionnaire. The application of a Structural Equation Model underscores the fact that the ability of ICTs to respond to the different needs of students in the university classroom and their perceived efficacy in the teaching–learning process both exert a positive effect on attitudes in favour of their incorporation into the classroom. In turn, these attitudes also have a significant effect on active behaviour with ICT resources. In addition, various mediating effects are seen to influence an active behaviour. All this gives rise to a discussion on the implications of these results to encourage the training of university teaching staff in the knowledge and management of ICTs. Increasing confidence in ICTs as effective tools to respond to different needs could significantly favour positive attitudes and behaviour so that these resources are actively integrated into the classroom.

Innovative, pedagogically informed instructional design is instrumental in increasing student engagement and improving learning outcomes in online learning environments. Interactive learning resources provide students with the opportunity to engage with content in a more personalised manner. H5P (HTML 5 Package) is a collaborative platform that allows developers to create interactive content and has been regularly used in education settings. Some evidence suggests using interactive H5P resources in online education courses could lead to greater student engagement. However, to date, there has been little investigation into whether H5P resources can improve student learning outcomes. The current study aimed to assess whether using interactive H5P resources improved assessed learning outcomes in an online undergraduate psychology course. A randomized cross-over design was utilized to test whether students exposed to H5P interactive videos had improved assessment results when compared to a control group. This study found no meaningful differences in assessment scores between students exposed to H5P versus those that were not. There was low overall engagement with the interactive content. However, students who did engage with the resources reported a positive experience and indicated a preference for more interactive elements in future courses. Future research should extend on the instructional design obstacles identified in this study, for example, by examining whether improved accessibility and education on the benefits of interactive resources would increase engagement and grades.

Blended Learning (BL) as a pedagogical approach has increased in significance during the COVID-19 pandemic, with blended and online learning environments becoming the new digital norm for higher educational institutions around the globe. While BL has been discussed in the literature for thirty years, a common approach has been to categorise learner cohorts to support educators in better understanding students’ relationships with learning technologies. This approach, largely unsupported by empirical evidence, has failed to adequately address the challenges of integrating learning technologies to fit with non-traditional students’ preferences, their BL self-efficacy and the associated pedagogical implications. Focusing on student preference, our study presents findings from a pre-COVID survey of undergraduate students across four campuses of an Australian regional university where students shared their learning technology preferences and the self-regulated learning that influenced their academic self-efficacy in a BL context. Findings show students want consistency, relevance, and effectiveness with the use of BL tools, with a preference for lecture recordings and video resources to support their learning, while email and Facebook Messenger were preferred for communicating with peers and academic staff. Our study suggests a quality BL environment facilitates self-regulated learning using fit-for-purpose technological applications. Academic self-efficacy for BL can increase when students perceive the educational technologies used by their institution are sufficient for their learning needs.

Computer programming is a skill of increasing importance in scientific and technological fields. However, in introductory computer science (CS1) courses in higher education, approximately one in every three students fails. A common reason is that students are overwhelmed by an accelerated and inflexible pace of learning that jeopardizes success. Accordingly, in the computer science education literature it has been suggested that the pedagogical philosophy of ‘mastery learning,’ which supports students progressing at their own pace, can improve academic outcomes of CS1 courses. Nevertheless, few extended mastery learning implementations in CS1 have been documented in the literature, and there is a lack of guidance and best practices to foster its adoption. In this paper, we present a four-year action research study in which a modular mastery-based CS1 course was designed, evaluated and improved in successive iterations with cohorts of engineering freshmen in a Latin American research university (N = 959). In the first year of the intervention, only 19.3% of students passed the course in their first semester attempting it. In successive iterations, the instructional design, teaching and learning activities, course content, and course management were iteratively improved such that by the fourth year of offering 77.1% of students passed the course in their first semester. Over this period, course attrition was reduced from 25.0% to 3.8% of the cohort, and students’ mean time spent in the course decreased from 23.2 weeks (SD = 7.38) to 14.9 (SD = 3.64). Results indicate that modularization for mastery learning is a viable approach for improving academic results in a CS1 course. Practical considerations towards successful implementation of this approach are presented and discussed.

Adult learners are a significant proportion of distance learners and many of these students are working mothers. Several instructional design models center the learner, and this requires understanding the learner needs, strengths, and context. There is a gap in the literature describing the experience of modern working mother students in distance education. To understand this experience, the researchers interviewed and observed six academically high-achieving working mother students as they participated in their distance education courses during the pandemic. A discourse analysis approach was utilized to analyze the data. This extreme sample revealed several strategies that these students used to be successful despite their challenges. The findings suggest that understanding the experiences of distance learners as they study in the home are important for effective course design. More specifically, working mothers face significant distractions in their study environments, but the cognitive load can be reduced by making use of their prior knowledge, scaffolding instruction, and encouraging social presence. Additional strategies from the literature that address these constructs are provided for instructors and instructional designers.

As online learning has become an inevitable trend in the post-peak era of the COVID-19 pandemic, distributed pair programming (DPP) is gaining momentum in both education and industry. DDP serves as a collaborative programming approach and also benefits the development of computational thinking, a fundamental skill in today’s world. This study conducted a systematic review of studies on DPP published after 2010 to understand the themes and factors that impact the team effectiveness of DPP and thus inform future research and practices on how to better leverage this approach for teaching and learning. The results showed that individual characteristics attracted major investigations in the selected 23 studies, including prior programming experience, actual skill, perceived skill, gender, personality, time management, confidence, and self-esteem, with pair compatibility identified as a critical team design factor that significantly affects programmers’ satisfaction. Although the feel-good factor in the team process was investigated, no significant impact was found. Under the team environment theme, we compared different opinions on the orientation (e.g., scripted roles) and the use of technology (e.g., integrated development environment tools). Future research should investigate how task structure influences team effectiveness of DPP and relates to computational thinking education. Additionally, because most studies were conducted in higher education contexts, more research in primary and secondary educational contexts is also needed.

With the rapid adoption of online learning across higher education, there is an urgent need to identify its challenges and ways of addressing them. Online group projects, in particular, present significant issues for educators. This paper presents the findings of a systematic literature review identifying the key challenges of online group projects, together with strategies to address them. From a corpus of 114 recent papers, the 57 most relevant were analysed, to identify themes related to challenges and strategies. Key challenges were: low and uneven participation by students; a lack of clarity and preparation for students; and poor relationships. Strategies for addressing challenges were: careful design of projects, particularly regarding fair assessment; clear guidance and preparation of students; and practical and emotional support throughout, to encourage confidence and engagement. The findings of this review will enable educators to design and facilitate online group projects which students find rewarding and valuable.

Earning Open Badges instead of regular grades and credits can be a motivating factor for high-performing students in terms of attending classes and completing assignments in extracurricular courses, but to what extent? And for what student profiles? To tackle these questions, we conducted a quantitative study with high-performing students. Each student involved in the study had consecutively attended two Java programming courses—one where credits and regular grades were issued for their achievements and performance in the course, and another extracurricular one where Open Badges were issued instead. The study compared the achievement goal orientation (AGO) of each student in the two courses (Wilcoxon paired test). It also examined how students’ AGO scores in the Open-Badges-only course were associated with class attendance, completion of assignments and public display of their achievements (badges)—both as individual correlations with these variables (Spearman method), as well as associations with student profiles based on these variables (identified with Ward hierarchical clustering). The results indicate that high-performing students feel less motivated in terms of outperforming/under-performing others and have less fear of not learning enough if they receive Open Badges rather than regular grades. Also, a small portion of high-performers will be fully engaged in an Open-Badges-only course (attendance/completing assignments), while the majority will attend but complete a few assignments or just attend. Still, their AGO is not correlated with attending classes, completing assignments and displaying badges.

Research in gamified learning is still needed to expound how gamification may be employed to realistically yield positive effects on learning motivation. It is essential to evaluate whether gamification can foster autonomous forms of motivation, such as intrinsic motivation, which has been related to learning persistence and performance quality, instead of non-autonomous extrinsic motivation, that has been shown to be unsustainable or at worse, harmful to learning. To explore this, the current study investigated how gamified learning affected various motivational dimensions such as the sense of competence, autonomy, valuation, intrinsic motivation, identified regulation and external regulation to provide a clearer picture of the motivational influence of gamification. This study involved a learning activity that was gamified in a long-term context. The findings showed that gamifying a non-graded learning activity increased the sense of autonomy and intrinsic motivation. Interestingly, the findings showed that gamification reduced non-autonomous motivation in an examination-oriented course. Nevertheless, gamified learning did not yield significant effects on the sense of competence and valuation. The relationships between the various motivational dimensions were discussed in this paper.

This article reports research into calculative and optionalized forms of online/blended course design in higher education. This was investigated through a critical case study, centered on two faculty members and one instructional designer at a university in the United States, and using an interpretive framework that highlighted the effects of calculation and optionalization in education. The course design practices at the designer’s disposal tended to distort the teaching ideals towards which the faculty members aimed, along with many of the teaching approaches they relied on to achieve their goals. The faculty often felt restricted in their ability to form relationships with their students, while also observing that students tended to resist their attempts to engage in what they referred to as formational activities. Through these and other experiential tensions, the faculty left the project with a pervasive sense of ambiguity about course design and its contribution towards their experience as educators. The article concludes by exploring what implications these findings have for the study and practice of online/blended course design in higher education.

The purpose of this study is to explore the influence of the microlearning instructional approach in an online introductory database programming classroom. The ultimate goal of this study is to inform educators and instructional designers on the design and development of microlearning content that maximizes student learning. Grounded within the frameworks of cognitive theories of learning—Cognitive load theory, Cognitive theory of multimedia learning, and Cognitive, affective theory of learning with media, this exploratory case study collected student assessment data from ten-course topic quizzes and exam scores in midterm and final exams. Additionally, the students' and the course instructor's perceptions while experiencing the microlearning instructional approach were collected and analyzed. As a result, the students scored significantly higher in the course topic quizzes under microlearning instruction compared to the video lectures condition. The students experienced less cognitive load and were more motivated and engaged throughout the learning process under the microlearning instruction. The students also reported some challenges of microlearning instruction, including missing social aspects of learning, fragmented learning for some complex topics, and Canvas LMS platform issues. Overall, this inquiry suggests the potential of microlearning as an instructional approach for introductory programming concepts. Some key implications of the findings are also addressed.

This study investigated the effects of pre-training types on cognitive load, self-efficacy, and problem-solving in computer programming. Pre-training was provided to help learners acquire schemas related to problem-solving strategies. 84 undergraduate students were randomly assigned to one of three groups and each group received three different types of pre-training: 1) WOE (worked-out example) and metacognitive scaffolding, 2) faded WOE and metacognitive scaffolding, and 3) WOE and faded metacognitive scaffolding. After the pre-training phase, the participants’ cognitive load, self-efficacy, and programming problem-solving skills were analyzed. Then, during the training phase, the participants were asked to attempt a programming problem-solving task with faded WOE and faded metacognitive scaffoldings. After the training phase, the participants’ cognitive load, self-efficacy, and programming problem-solving were analyzed again. The findings revealed that providing both cognitive scaffolding (i.e., WOE or faded WOE) and non-faded metacognitive scaffolding during the pre-training phase is effective for novice learners for optimizing cognitive load, promoting self-efficacy, and enhancing programming problem-solving skills.

The purpose of this multiple-case study was to investigate how expert instructors in case-based learning (CBL) conceptualize, structure, facilitate, and assess asynchronous online discussions while addressing challenges. Accordingly, we first interviewed three expert instructors with extensive teaching experience using CBL in higher education in online learning environments and then observed their online courses. Results indicated that (a) how instructors conceptualize case discussions may relate to different conceptualizations of their instructor role, (b) instructors typically clustered facilitation strategies creating posts that included content expertise, social congruence, and cognitive congruence, but how these strategies were implemented differed across instructors, and (c) instructors differentiated solutions to challenges associated with observed difficulties on the instructor and student side. Implications for practice and research are provided.

Laboratory activities are central to undergraduate student learning in science and engineering. With advancements in computer technology, many laboratory activities have shifted from providing students experiments in a physical mode to providing them in a virtual mode. Further, physical and virtual modes can be combined to address a single topic, as the modes have complementary affordances. In this paper, we report on the design and implementation of a physical and virtual laboratory on the topic of jar testing, a common process for drinking water treatment. The assignment for each laboratory mode was designed to leverage the mode’s affordances. Correspondingly, we hypothesized each would elicit a different subset of engineering epistemic practices. In a naturalistic, qualitative study design based on laboratory mode (physical or virtual) and laboratory order (virtual first or physical first), we collected process, product, and reflection data of students’ laboratory activity. Taking an orientation that learning is participation in valued disciplinary practice, data were coded and used to characterize how students engaged with each laboratory mode. Results showed that the virtual laboratory elicited more conceptual epistemic practices and the physical laboratory more material epistemic practices, aligning with the affordances of each mode. When students completed the laboratory in the virtual mode first, students demonstrated greater engagement in epistemic practices and more positive perceptions of their learning experience in the virtual mode than when they completed the physical mode first. In contrast, engagement in the physical mode was mostly unaffected by the laboratory order.

Coding think-aloud transcripts is time-consuming and labor-intensive. In this study, we examined the feasibility of predicting students’ reasoning activities based on their think-aloud transcripts by leveraging the affordances of text mining and machine learning techniques. We collected the think-aloud data of 34 medical students as they diagnosed virtual patients in an intelligent tutoring system. The think-aloud data were transcribed and segmented into 2,792 meaningful units. We used a text mining tool to analyze the linguistic features of think-aloud segments. Meanwhile, we manually coded the think-aloud segments using a medical reasoning coding scheme. We then trained eight types of supervised machine learning algorithms to predict reasoning activities based on the linguistic features of students’ think-aloud transcripts. We further investigated if the performance of prediction models differed between high and low performers. The results suggested that students’ reasoning activities could be predicted relatively accurately by the linguistic features of their think-aloud transcripts. Moreover, training the predictive models using the data instances of either high or low performers did not lower the models’ performance. This study has significant methodological and practical implications regarding the automatic analysis of think-aloud protocols and real-time assessment of students’ reasoning activities.