Internet and Higher Education最新文献

This study examined the effects of feedback type and treatment order on nontraditional graduate students' perceptions of instructor-provided feedback in a fully online instructional design course. The graduate students were provided with video feedback and text feedback for four interconnected instructional design document assignments. A mixed design was utilized with feedback type as within-subjects factor and treatment order as between-subjects factor. Further, an inductive analysis approach with open coding was used to analyze the data from open-response items. The quantitative results demonstrated that the interaction between feedback type and treatment order was significant for perceived learning, feedback quality, presentation format, and engagement with feedback. The qualitative findings revealed that the students in both groups acknowledged the value of receiving video and text feedback, and identified several benefits and drawbacks of working with each feedback type.

Evidence suggests that learning sentiments are inextricably related to cognitive processing, and the exploration of the relationship remains to be an important research topic. This study collected discourse data from 40 college students in online collaborative learning activities. Epistemic network analysis (ENA) was employed to explore the connection between learning sentiments and cognitive processing and compare the ENA network characteristics of the higher- and lower-engagement groups. The results indicated that there was a joint connection between understand-analyze-neutral, and insightful sentiments had more association with neutral sentiments and understanding. Besides, distinctions existed between higher- and lower-engagement groups with respect to the association between learning sentiments and cognitive processing. The higher-engagement group had stronger associations around positive and confused sentiments, while the lower-engagement group had stronger associations around off-topic discussion. The findings of this research may serve as a reference for designing and implementing collaborative learning activities to increase cognitive levels.

Many online learning contexts are characterized by both high levels of student diversity on socio-demographic attributes (e.g., gender, first language) as well as task-related attributes (e.g., prior online-learning experiences, prior degrees). This longitudinal study investigated the relationships of multi-attributional diversity with CSCL processes and outcomes in a cohort of 1525 distance education freshmen randomly allocated to 343 groups over the course of a nine-week CSCL assignment. Group-level path analyses revealed that, if not explicitly managed, higher multi-attributional socio-demographic diversity was negatively related to the groups' structural integration (computed from digital data). Lower structural integration, in turn, was positively related to lower task-related collaboration time among students and, ultimately, a poorer grading of the groups' work by independent tutors. Moderation analyses further indicated that high task-related diversity operated as an amplifier of the negative relationship of high socio-demographic diversity with structural integration pointing to a risk constellation that requires active intervention.

Academics' use of social media platforms is widely recognized and often understood as an extension of traditional academic practice. However, this understanding does not account for academics' use of pseudonymous Twitter accounts. We used a combination of computational and human-driven methods to examine the activity of 59 anonymized, self-identified academics on Twitter. Our computational analysis identified five broad topics: discussing academic life, discussing British news and affairs, discussing everyday life, surviving lockdown, and engaging with academic Twitter. Within these broad topics, we identified 24 more specific codes, most of which were concentrated in individual topics, with some cross-cutting codes. These codes demonstrate how the pseudonymous accounts considered in this study can be considered “authentically academic” even if they do not conform with widespread expectations of academic social media use.

Employers now consider teamwork one of the essential skills for students to acquire during their academic life. However, COVID-19 has accelerated the transition towards online learning, affecting how we work in teams. This study looked at how scaffolding socially shared regulation of learning can influence teamwork in an online, project-based course. Intra-group peer assessment was used to analyze three variables during a first-year engineering course. By following the proposed scaffolding, students found an optimum balance in their contribution to team meetings. They also managed to establish a positive working environment earlier in the semester. This study contributes to the field by showing that scaffolding socially shared regulation in an online, project-based course allows for an interplay between collaboration during class and cooperation outside of it. This interplay ultimately leads teams to achieve better results on their final project.

There is research showing benefits to both collaboration and note-taking, but a lack of research into how they may both work together in an online context. More specifically, there is a gap in the research looking at how collaborative note-taking and individual note-taking can be compared when considering the quality of the notes taken, and how note-quality can impact student performance. The present study looks at the online note-taking behavior and performance of 186 graduate students studying at a Korean university. The results indicate that students who collaborate perform better than individual note-takers on measures of recall of course content, but that individual note-takers perform better on tasks focused on academic writing. Furthermore, the findings suggest that note-quality has no effect on collaborative note-takers' recall of course content, and a slight negative impact on their writing, while individual note-takers benefit from higher quality notes for both recall and writing.

The goal of this study was to examine the effects of adaptive online feedback on self-regulated learning, motivation, and achievement. University students (N = 257) participated in an experimental field study with an intensive longitudinal design (daily assessment over 30 days). The experiment included a between-subject and a within-subject manipulation. The target of the feedback intervention was varied between subjects: Students either received (1) feedback on metacognitive aspects, (2) feedback on motivational aspects, (3) feedback on metacognitive and motivational aspects, (4) or no feedback. Within the three feedback groups, we additionally varied feedback content from day to day within-subjects. Students either received (1) informative feedback on self-regulated learning (2) directive feedback including only a strategy suggestion, (3) transformative feedback including feedback on self-regulated learning and a strategy suggestion, (4) or – on some days – no feedback. Results revealed that informative, directive, and transformative informative feedback reduced students' procrastination and improved daily self-monitoring, adherence to time schedules, and goal achievement compared to receiving no feedback. Informative and transformative feedback additionally improved planning strategies and concentration. Motivation and self-efficacy were unaffected by any kind of feedback. The positive effects of the intervention were most pronounced when students received feedback on metacognitive and motivational aspects. Moreover, students in the feedback groups achieved better grades in the examinations compared students in the control group. Together, results indicate that the feedback intervention effectively improved students' self-regulated learning and achievement. We discuss differential effectiveness of the feedback depending on feedback content.

Students' social experiences are critical to their academic success, including determining their motivation and engagement. However, little is known about how various social factors, including social presence and teacher involvement, relate to expectancy, task value, and facets of engagement in online learning. Using path modeling to analyze the survey responses of 122 college students, we examined the relations among social presence, teacher involvement, expectancy, task value, and cognitive and behavioral engagement guided by the situated expectancy-value theory. We discovered that social presence predicted expectancy, task value, and behavioral engagement, and task value impacted cognitive engagement. We found a mediating effect of expectancy-to-task value in the link between social presence and cognitive engagement. Overall, the predictive influence of expectancy on task value was critical in the model. Interestingly, teacher involvement did not play a role. These findings confirm the significance of online social features to motivation and engagement and further highlight the value of motivation for online learning success. Therefore, it is important to design online learning environments whereby students feel socially connected and motivated.

This study builds on views of identity as jointly accomplished stories about people expressed through interpersonal relations as they participate in learning communities. Specifically, we suggest a framework that introduces the notion of identity artifacts – Shared resources in the public sphere about participants' lives. We analyzed a blended, humanistic learning community in a graduate setting that included specially designed person-centered activities to facilitate closer interpersonal relationships. We found 2640 identity artifacts, collected from 14 participants, which were organized into a five-staged framework. We use these to describe the role the artifacts played in the transforming participation of the students in the community. We include an extended example of a student to elucidate how this process occurs. This research advances our understanding of the connection between people's interpersonal relations and their learning practices, and suggests an approach to online pedagogies in higher education that can foster robust learning communities

The purpose of this study was to investigate whether students' self-reported SRL align with their digital trace data collected from the learning management system. This study took place in an upper-level college agriculture course delivered in an asynchronous online format. By comparing online students' digital trace data with their self-reported data, this study found that digital trace data from LMS could predict students' performance more accurately than self-reported SRL data. Through cluster analysis, students were classified into three levels based on their self-regulatory ability and the characteristics of each group were analyzed. By incorporating qualitative data, we explored possible explanations for the differences between students' self-reported SRL data and the digital trace data. This study challenges us to question the validity of existing self-reported SRL instruments. The three-cluster division of students' learning behaviors provides practical implications for online teaching and learning.