Brett E. Shelton

Proposes time and location entropy measures to characterize learners’ spatio‑temporal study patterns in an online course (n=5,293). High location–high time entropy groups (many places, many times) had the lowest success; students studying at more consistent times and in fewer locations performed better, with gender‑related differences observed.

Introduces time and location entropy to quantify ‘anytime, anywhere’ behavior using metadata from 5,293 students. High time‑and‑location entropy was associated with lower success; students studying at consistent times and fewer locations—particularly females in this cohort—showed higher performance. Results nuance assumptions about flexibility and success in online learning.

Synthesizes eight review papers from a corpus of 901 learning analytics publications to identify field maturity, emphases, and gaps. LA is in an early‑majority stage, with many conceptual/proof‑of‑concept works; small datasets (especially in K–12) predominate. Four major topics are highlighted: performance prediction, decision support, behavior detection/learner modeling, and dropout prediction.

Introduces a multistage predictive modeling method and evaluates it with large K–12 and higher‑education datasets. The approach achieved higher overall accuracy and sensitivity than a traditional model and identified two generalizable predictors across instruction‑ and discussion‑intensive courses.

To address gaps in performance prediction research, the paper proposes a multistage predictive modeling method emphasizing relative engagement. Using datasets from higher education and a K‑12 online school (13,368 students across 300+ courses), the approach outperformed traditional models on accuracy and sensitivity and identified two generalizable engagement predictors across instruction‑ and discussion‑intensive courses.

This study modeled early warning indicators of at‑risk students using learning‑management‑system interaction data. Analyses explored whether a holiday‑period effect contributes to failure and compared frequency of interaction versus amount of interaction as predictors. Findings suggest frequency of interaction is a preferable indicator for identifying students needing support.

Early‑warning approaches for at‑risk online learners are developed using LMS interaction data as indicators of social presence and engagement. Testing a prior “holiday effect” hypothesis, a new model emphasizing interaction frequency (rather than amount) is proposed as a more effective early predictor of risk.

Focusing on early‑warning for at‑risk online learners, the study modeled student interaction data from LMS logs as indicators of social presence. It tested the hypothesis of a holiday‑effect and compared frequency‑based interaction indicators, showing that interaction frequency is a better predictor of at‑risk status than aggregate amounts, informing timely instructional interventions.

Reports an ecologically valid implementation of GeePerS*Math, a GPS‑enabled mobile game for DHH learners. Teacher and student interviews/surveys indicated positive attitudes and transfer of skills to traditional curriculum, with additional outcomes such as orienteering gains and peer tutoring informing future design iterations.

Describes the design, development, and classroom implementation of an interactive‑fiction game for a 9th‑grade poetry unit, guided by an alignment principle between gameplay activity and instructional objectives. Findings show the approach created design tensions but also benefits; classroom transcripts and interviews highlighted where intentional alignments succeeded and failed.

Qualitative analysis of gameplay across six titles (three with explicit learning goals) explored presence and flow in immersive learning environments. Contrary to assumptions about camera perspective, emergent categories—content, emotion, motivation, engagement—exerted more influence on presence/flow than viewpoint alone.

Mixed‑methods study using an interactive‑fiction (IF) game to teach German vocabulary, reading, and cultural knowledge. Evidence suggests contextualized, immersive role play can aid learning, though many students were apprehensive about IF as a learning platform due to its departure from traditional instruction.

Conceptual and empirical argument that first-person, manipulative AR interfaces leverage proprioception and visuo‑motor processes to support spatial knowledge acquisition. Reviews properties that distinguish AR from conventional visualization and synthesizes findings indicating cognitive advantages of AR for learning dynamic spatial relationships.

Reports design and in‑situ evaluation of CareNet, an ambient display to support elders’ local care networks. Field deployments documented usage patterns and impacts on elders and caregivers, yielding design lessons for improving ambient displays for this growing user community.