Apps and Animations: Choosing Web-based Demonstrations to Support Student Learning
Bibliographic record
Abstract
Over the past decade, the prevalence of mobile devices (e.g., smartphones, tablets, laptops) on university campuses has skyrocketed. A 2015 survey shows these technologies are quickly becoming ubiquitous in the classroom, with 87% of university students using laptops and 64% of students using smartphones on a weekly basis to complete their schoolwork (Pearson, Harris Polls, 2015). These same students also agree that tablets will transform university learning in the future (83%), that mobile technology makes learning more fun (79%), and helps students perform better in class (68%); in addition, 40% of university students would like to use mobile technologies in classes more often than they do now, while only 13% would like to use mobile devices less often (Pearson, Harris Polls, 2015).\nMobile devices seem to tantalize both students and educators alike with the promise of enhanced student learning including tailored content, instructional methods based on the needs of individuals, interactive engagement with the material, exploration beyond the classroom, and connections to the material unrestricted by time or location. The goals of teaching-related apps and animations are obvious: to generate student interest in a topic, promote student engagement, concretize abstract principles, and to enhance student learning. However, the small-but-growing body of research on the use of apps and animations has suggested that all are not created equal, particularly with respect to the ultimate goal of enhancing student learning (e.g., Tversky, Morrison, & Betrancourt, 2002). Several reviews have reported mixed findings with respect to the effects of mobile technology on student learning, with some suggesting that it enhances learning (Hwang & Wu, 2014) and others finding few significant benefits in learning outcomes (Cheung & Hew, 2009). Some studies have even shown that objective measures of student learning of critical concepts are actually impaired by the use of animations or computer-based demonstrations of these concepts (Copeland, Scott, & Houska, 2010; Mayer, Hegarty, Mayer, & Campbell, 2005).\nThe purpose of this workshop is to introduce participants to some of the research exploring the use of apps, animations, and demonstrations (e.g., participation in a classical experiment on visual perception) in university-level courses, with a focus on identifying the characteristics that separate the good from the bad in terms of student learning measures. The ultimate goal is to provide guidelines that will help educators better identify those apps, animations, or other instructional technologies that will be most beneficial in terms of encouraging deep student understanding of course material. Much of the material in this workshop is drawn from research in education and psychology, but the principles that we discuss would apply to almost any domain.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".