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This study, originally prompted by the impact of the COVID-19 pandemic on educational practices, examined Canadian faculty members’ expectations of teaching and learning modalities in the year 2026. Employing a speculative methodology and thematic analysis, interview responses of 34 faculty members led to the construction of three hypothetical scenarios for future teaching and learning modalities: a hybrid work model, a high tech and flexible learning model, and a pre-pandemic status quo model. In contrast to radical education futures described in the literature, the findings do not depart significantly from dominant modes of teaching and learning. Nevertheless, these findings offer insights into the expectations that Canadian faculty members have with respect to future teaching and learning modalities, the contextual issues and concerns that they face, the use of speculative methodologies in educational technology research, and the potential impacts remote learning trends have on the future of higher education in Canada.

This study investigated (1) how students and their families’ choices of learning modality differed by student demographics, (2) how much movement (i.e. learning modality changes) occurred during the pandemic school year, and (3) the mediating effect of movement on the relation between initial learning modality choices and students’ mathematics performance. The participants involved 4008 7th-grade students from a suburban school district in the southeastern USA. Results of the chi-square tests revealed that initial learning modality choices and movement significantly differed by students’ demographic variables. Most White and Hispanic students initially selected in-person learning, whereas most Asian and Black students selected virtual learning. About half of Black (55.6%) and Hispanic (46.7%) students who initially selected virtual learning moved back to in-person classes, while most Asian students remained in virtual learning. Further, we performed a mediation analysis using the subset of the students ( n  = 2046) who completed both pretest and posttest on algebraic knowledge. The results indicated that the initial choice of learning modality had a significant indirect effect on posttest scores of algebraic knowledge through its association with the change of learning modality, after controlling for students’ demographic variables and pretest scores. Our findings suggest that instability in learning modality may be disruptive to student learning and is associated with lower end-of-year math performance.

Background. This study underscores the importance of motivation in self-regulated learning and technology-use efficacy, particularly in the context of online learning modality. The transition to blended and hybrid learning modalities has necessitated a reevaluation of the factors influencing student success.Methods. This quantitative survey study was conducted at Biliran Province State University (BiPSU) in the Philippines. Using a convenience sampling approach, data were collected through a Likert scale questionnaire to 800 respondents’ undergraduate students enrolled in the second semester of the 2022-2023 academic year. The study employed Partial Least Squares - Structural Equation Modeling (PLS-SEM) through SmartPLS to explore the relationships between perceived competence, value, pressure/tension, interest, effort, and technology-use efficacy. The measurement model was validated by assessing indicator reliability, internal consistency, construct reliability, and discriminant validity. The study's exploratory nature and statistical approach enabled a robust analysis of factors influencing students’ technology-use efficacy.Results. The results revealed that reducing pressure/tension and enhancing the perceived value of tasks are significant pathways to improving technology-use efficacy. Specifically, perceived choice and relatedness reduce pressure/tension, and both perceived choice and competence increase the value of the task, leading to higher technology-use efficacy. Effort/importance and interest/enjoyment did not significantly mediate the relationships between the predictors and technology-use efficacy.Conclusion. Fostering a sense of autonomy, competence, and relatedness may be more critical to promoting effective technology use than focusing solely on effort or enjoyment.

In metaverse, a digital‐twin smart home is a vital platform for immersive communication between the physical and virtual world. Triboelectric nanogenerators (TENGs) sensors contribute substantially to providing smart‐home monitoring. However, TENG deployment is hindered by its unstable output under environment changes. Herein, we develop a digital‐twin smart home using a robust all‐TENG based information mat (InfoMat), which consists of an in‐home mat array and an entry mat. The interdigital electrodes design allows environment‐insensitive ratiometric readout from the mat array to cancel the commonly experienced environmental variations. Arbitrary position sensing is also achieved because of the interval arrangement of the mat pixels. Concurrently, the two‐channel entry mat generates multi‐modality information to aid the 10‐user identification accuracy to increase from 93% to 99% compared to the one‐channel case. Furthermore, a digital‐twin smart home is visualized by real‐time projecting the information in smart home to virtual reality, including access authorization, position, walking trajectory, dynamic activities/sports, and so on. A digital‐twin smart home is realized by a scalable, robust, and all‐triboelectric nanogenerator (TENG) information‐mat (InfoMat), where not only the varying interdigital electrode designs for the mat pixels enable stable output from the mat and show high tolerance to environment changes, but also the multi‐modality deep learning effectively enhances the classification accuracy for identifications.

The persistence of learning styles as a concept in educational discourse and research is paradoxical, given the overwhelming evidence discrediting the matching hypothesis, the notion that aligning teaching methods with students’ preferred learning styles enhances achievement. This paper examines the resurgence of learning styles across meta-analyses and proposes an explanation for their enduring appeal. Drawing on 17 meta-analyses, we distinguish between studies testing the matching hypothesis (effect size d  = .04) and correlational studies (average correlation r  = .24), revealing that the latter often conflates learning styles with learning strategies. Much of the research is flawed, and the argument is that there needs to be a shift away from matching learning styles toward teaching students adaptable and effective learning strategies that align more closely with task complexity and learning goals.

This study examines a person-centered approach to self-regulated learning among 606 University students (140 online, and 466 in blended learning mode). Latent profile analysis revealed five distinct profiles of self-regulated learning: minimal regulators, restrained regulators, calm self-reliant capable regulators, anxious capable collaborators, and super regulators. These profiles showed that: (1) differences in academic success are associated with a learner's capacity for motivational regulation and self-regulated learning strategy implementation, (2) online learners are more likely to belong to profiles that are more adaptive, and less reliant on collaborations with others, (3) for learners at the lower end of the self-regulation spectrum, an increase in both motivational regulation and adoption of self-regulated learning strategies may be academically beneficial, and (4) high motivational regulation and strategy adoption can be all for naught, if the student is also highly anxious with worry and concern regarding performance.

The edtech community has promoted claims that digital education enhances access, learning, and collaboration. The COVID-19 pandemic tested these claims like never before, as higher education systems seemingly overnight had to move teaching online. Through a sequential mixed-method approach, we investigated how 85 higher education leaders in 24 countries experienced this rapid digital transformation. Through their experiences, we identified the multiple and overlapping factors that contribute to an institution’s ability to realize the potential of digital education, in terms of access, learning and collaboration, whilst highlighting deeply rooted inequalities at the individual, institutional and system level. Drawing on these empirics, we put forth recommendations for closing the digital divides and pathways forward. Higher education leaders are uniquely positioned to move beyond the emergency adoption of online learning towards inclusive, long-term visions for digital education, which emphasize collaboration over individual gain.

This paper explores the practical prospects for using artificial intelligence technologies in professional English-speaking translator education. At the online conference ‘Translation Skills in Times of Artificial Intelligence’ (DingTalk platform, January 2022), the teachers of higher education institutions in China prioritized the translator’s competencies necessary for successful professional activity during the digital transformation of social and economic business relations. The educators also evaluated the demand for online services used in the education of English–Chinese interpreters. The survey results showed that the use of artificial intelligence technologies in educational practices could have a significant impact on the development of key competencies of future translators. Using a competency-based approach to interpreter training and considering the need to develop abilities, knowledge, and skills required for successful professional translation activity, the author developed the pedagogical concept of the online educational course ‘Simultaneous and asynchronous translation in a digital environment.’

This study examined the relationship between perceived social support and subjective well-being among college students in the context of online learning during the COVID-19 pandemic. 515 college students in China that participated in an online questionnaire investigation were selected as the research sample. The results showed that perceived social support was significantly and positively associated with life satisfaction and positive affect and was significantly and negatively related to negative affect among college students learning online during the COVID-19 pandemic. Psychological capital (PsyCap) significantly mediated the relationships between perceived social support and three subjective well-being variables. The present study provides some implications to protect college students’ subjective well-being in the context of online learning during the COVID-19 pandemic.

Pair programming (PP), as a mode of collaborative problem solving (CPS) in computer programming education, asks two students work in a pair to co-construct knowledge and solve problems. Considering the complex multimodality of pair programming caused by students’ discourses, behaviors, and socio-emotions, it is of critical importance to examine their collaborative patterns from a holistic, multimodal, dynamic perspective. But there is a lack of research investigating the collaborative patterns generated by the multimodality. This research applied multimodal learning analytics (MMLA) to collect 19 undergraduate student pairs’ multimodal process and products data to examine different collaborative patterns based on the quantitative, structural, and transitional characteristics. The results revealed four collaborative patterns (i.e., a consensus-achieved pattern, an argumentation-driven pattern, an individual-oriented pattern, and a trial-and-error pattern), associated with different levels of process and summative performances. Theoretical, pedagogical, and analytical implications were provided to guide the future research and practice.