Quick actionable summaries from the research on teaching and learning.
This study examines how teacher, peer, and self-feedback influence student learning in an online assessment context. Using a quasi-experimental design with university students, the authors compared how students perceive different feedback types versus how those feedback types actually impact writing improvement.
Each of these illusions contain an element of truth that makes them appealing and enduring, but they do not have evidence to support their effectiveness in student learning. This blog post explains each of these illusions, describes why they are harmful to learning, and provides guidance for improving instruction.
This study examines a “flipped feedback” model where students engage with feedback before final submission. Students submit a draft, review generic feedback on common errors, self-assess using a rubric, predict their grade, revise the assignment, and request targeted feedback.
This article argues that many problems in psychological and behavioral research stem not only from statistical practices but also from how researchers define and measure constructs. The authors introduce the concept of questionable measurement practices (QMPs)—research decisions about measurement that raise doubts about the validity of a study’s conclusions. When such decisions are hidden or poorly documented, it becomes difficult for readers or other researchers to evaluate threats to construct validity, internal validity, statistical validity, and external validity, which ultimately undermines the credibility and replicability of research findings.
This week, I want to highlight a substack post critiquing a recent research article. The author critically examines a widely referenced paper claiming that increased AI use degrades critical thinking skills. Pookins argues that the study’s design and methodology are fundamentally flawed: the sample isn’t representative, the survey measures self-reported beliefs rather than actual critical thinking performance, and many items intended to measure different constructs are essentially redundant. Because of these flaws, he asserts that the paper does not provide reliable evidence that AI use causes a decline in critical thinking, meaning that its frequent citation in media and academic discussions may be misleading or premature. Moreover, he points to evidence that the paper itself may have been AI-generated.
This is a systematic review and meta-analysis of experimental research on ChatGPT’s impact on student learning (69 studies from 2022–2024). The goal was to move beyond simple correlations and look at causal effects of ChatGPT use in education settings.
Researchers examined how lecture video playback speed affects student learning by having undergraduates watch recorded lectures at normal speed (1x), faster speeds (1.5x, 2x, 2.5x), or by watching videos more than once at increased speed. Students completed comprehension tests immediately after viewing and again one week later. The study focused on whether faster playback harms understanding or long-term retention, a common concern among instructors using recorded lectures.
This study investigated a hybrid assessment format called Multiple-Choice with Elaboration Questions (MCEQs). In these questions, students not only select a multiple-choice answer but also must justify their choice in writing. The research was conducted across four sections of an upper-division psychology research methods course at a large public university.
This paper argues that students’ frequent use of ineffective learning strategies (like rereading and highlighting) isn’t just due to lack of awareness, time pressure, or goals — it may also reflect habitual behavior. Traditional research on self-regulated learning emphasizes deliberate choice and metacognition, but this article suggests that many study practices have become automatic routines triggered by environmental cues. Ineffective strategies often become habituated because they are easy, familiar, and contextually ingrained.
Fiorella and Mayer argue that learning is generative—students learn best when they actively make sense of new information by selecting, organizing, and integrating it with prior knowledge. They synthesize research identifying eight evidence-based strategies that consistently promote deeper understanding and transfer across contexts. These strategies shift learners from passive reception to active sense-making.