Bibliographic record
Abstract
The ever-changing digital technology landscape in higher education has given rise to what we envision as the Digital Learning Playground (Pinto & Leite, 2020).Essential to the concept of the digital playground is the notion of fun, playfulness, positivity and exploration.In this special issue, we invite readers to journey through diverse perspectives and insightful research illuminating digital play's challenges, innovations, and potential in higher education.Specifically, we focus on three areas of play and exploration: artificial intelligence and ChatGPT, building and exploring the Discord online community, and reading as a socially constructed activity.In the first article, Lauricella and Edmunds (2023) focus on how Ludic Pedagogyintegrating fun, play, playfulness, and positivity into learning -can boost intrinsic motivation, specifically through ChatGPT.They discuss how ChatGPT and generative AI can build curiosity, encourage experimentation, and develop authentic assessment.In the second paper, Lauricella et al. (2023b) focus on the benefits and challenges of using Discord, a tool explicitly designed to stimulate discussion, conversations, and community.It is worth noting that the tool was initially designed for gamers in the context of play.In this case study, using Discord helped build the classroom community, increased engagement, and established a casual, informal learning environment.In the final article, Lauricella et al. (2023a), examine Perusall, a tool designed to make reading more engaging and interactive.Typically, students do not see academic reading as pleasurable, with less than 30% completing reading assignments (Kerr & Frese,2016).However, in this case study, students reported using Perusall to share and post ideas, comments and questions while reading.Using Perusall was "fun" and "engaging" because they enjoyed positive communication with classmates.We view the idea of a "digital playground" as a metaphor for experimentation and creativity and a reference to a shifting paradigm in higher education, where learning is not just acquired passively.Instead, it is experienced and co-created, fostering an exhilarating academic environment boundless in possibilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".