The experimental class’s overall score rose from 3.475 to 3.751 on the five-point scale, while the control class’s score changed little, from 3.511 to 3.481. At posttest, the difference between classes was statistically significant at p < 0.001, with a reported Cohen’s d of 1.042. These findings support the study’s narrower conclusion that inquiry-centered teaching may help students understand why and how motor skills work; they do not establish that the intervention directly improved athletic execution or physical fitness.

The study was designed as a comparative teaching intervention in which the same teacher taught the same basketball course over one semester to both classes, with the principal difference being the instructional model. The experimental class used Argument-Driven Inquiry, or ADI, while the control class continued with traditional teaching. Before instruction, the researchers found no significant difference between the groups in overall motor-skill comprehension, with a pretest p value of 0.712 and Cohen’s d of 0.083.

Nor were there significant baseline differences in any of the questionnaire’s individual dimensions: explanation, interpretation, application, perspective, empathy, and self-knowledge. All corresponding confidence intervals included zero, and every baseline effect size was below 0.2. The results therefore support the researchers’ decision to regard the two classes as having comparable starting levels for the purpose of the comparison, although the use of intact classes rather than random assignment limits stronger causal attribution.

Motor-skill comprehension was operationalized through a 30-item questionnaire administered before and after the course. Its six dimensions were intended to capture more than the ability to reproduce a movement. Explanation covered concepts and methods of performing skills; interpretation concerned the functions and effects of mastering them; application addressed their use in practical situations; perspective assessed whether students could question motor skills and formulate high-quality questions; empathy concerned reflection from other learners’ perspectives; and self-knowledge evaluated students’ capacity to assess their own performance objectively.

Items used a five-point Likert scale. The investigators first surveyed 150 students to examine the instrument’s reliability and validity. Internal consistency was high: dimension-level Cronbach’s alpha coefficients ranged from 0.768 to 0.841, and the overall coefficient was 0.864.

Composite reliability values ranged from 0.845 to 0.888, while average variance extracted values ranged from 0.526 to 0.613. Sampling adequacy, discriminant-validity and multicollinearity checks also met thresholds used in the analysis. Within the experimental class, the questionnaire identified statistically significant pretest-to-posttest improvement in five of the six dimensions.

Explanation increased from 3.660 to 3.975, with p = 0.016 and Cohen’s d = 0.398. Interpretation rose from 3.645 to 3.960, with p = 0.019 and d = 0.386. Application increased from 3.530 to 3.815, with p = 0.005 and d = 0.474.

Perspective changed from 3.485 to 3.705, with p = 0.043 and d = 0.331, while empathy increased from 3.300 to 3.640, with p = 0.008 and d = 0.439. Self-knowledge was the exception, moving from 3.230 to 3.410 without statistical significance at p = 0.176. By contrast, the control class showed no statistically significant change in any dimension or in the overall score; its overall p value was 0.692.

The authors noted that self-knowledge may require longer development because it depends on substantial skill knowledge, self-regulated learning, and mastery of evaluation methods, but the study did not directly test that explanation. When the two classes were compared after the semester, the experimental group scored higher in explanation, interpretation, application and perspective. The posttest group differences were significant for explanation at p = 0.005, interpretation at p = 0.001, application at p = 0.001, and perspective at p = 0.044.

The reported effect sizes for those comparisons ranged from d = 0.462 to 0.828, with the largest corresponding to application. Empathy and self-knowledge did not differ significantly between the classes at posttest, with p values of 0.577 and 0.524, respectively. The researchers attributed those null comparisons to some improvement in the control group in the same areas, although the experimental group’s pretest-to-posttest gains were larger.

Because these are questionnaire reports, the statistical contrasts describe changes in students’ assessed understanding; they should not be read as direct measurements of technique, speed, accuracy, retention or transfer to competitive play. ADI places argumentation within structured inquiry rather than treating debate as an end in itself. The article describes an eight-stage implementation pathway adapted for physical education.

First, an instructor creates an authentic sporting problem from which students derive a question—for example, investigating why a stop-and-jump shot becomes distorted at high speed rather than simply copying a demonstration. Second, students propose hypotheses, including predictions that can initially be incorrect. Third, groups design a practice protocol, identify the variables to control, choose equipment and define how evidence will be recorded.

Fourth, they perform the inquiry and gather both quantitative observations, such as successful attempts or flight distance, and qualitative bodily feedback, such as perceived force production, balance or difficulty. Fifth, students analyze the results and build explanatory models. Sixth, they present arguments, cross-examine one another and receive biomechanical or movement-principle feedback.

Seventh, they document the inquiry and reflect on methodological limitations. Eighth, they return to practice, correct faulty actions, explain the principles behind the skill and attempt to transfer the technique to a new situation. The proposed pedagogy changes the teacher’s role during practical work.

Rather than immediately correcting a student’s movement, the instructor is expected to maintain safety, ask questions about controlled variables and prompt students to examine their own evidence. For example, when students compare a high badminton clear struck above the head with one struck at shoulder height, the task can be defined by the flight trajectory and landing position. A forearm-pass investigation might record the success rate of straight and bent arms and ask students to explain the difference mechanically.

The model also makes assumptions explicit: students write down their reasoning, classify proposed explanations, test them under planned conditions and confront competing interpretations. These examples illustrate the proposed framework across sports, but the paper explicitly states that they were not the specific activities tested in the reported intervention. Delivering all eight stages in a time-limited physical education lesson could be impractical, the author argues, because extended debate and report writing may reduce time available for movement.

The suggested compromise is to retain the full inquiry sequence while simplifying its presentation. Stages involving task definition, hypotheses, inquiry design, review, revision and skill internalization can remain central, while data collection, argumentation, reporting and reflection can be condensed into prepared forms, motor-learning journals and brief structured exchanges. The aim is to preserve the cognitive sequence without sacrificing sufficient practice.

Even so, the classroom comparison tested a basketball course as delivered by the study’s instructional design; it did not isolate which individual ADI stage, dose or classroom feature produced the observed questionnaire changes. The intervention also was not directly compared with other established inquiry-based physical education approaches. The trial has important limitations.

The design lacked random assignment, and the researchers did not report blinded assessment. The self-report instrument measured perceived understanding rather than observed movement execution, so the study cannot show that ADI improved basketball technique directly. The authors also emphasize that inquiry does not replace essential practice: students still need repeated physical activity, corrective feedback and opportunities to perform.

Future work would require larger and more diverse samples, direct performance measures and quantitative tests across other physical education courses. Subject to those constraints, the study offers a detailed classroom mechanism for moving beyond mechanical repetition: a cyclical sequence in which cognitive challenge, physical testing, argumentative reflection and revised practice are linked. Its clearest demonstrated result is an association between ADI-based basketball instruction and higher self-reported motor-skill comprehension in two small college classes.

Subject of Research: Perceived motor-skill comprehension in two college basketball classes taught with Argument-Driven Inquiry or traditional instruction

Article Title: Implementation approaches of improving college students’ comprehension level of motor skills based on argument-driven inquiry teaching model

Article References: Li, X. (2026). Implementation approaches of improving college students’ comprehension level of motor skills based on argument-driven inquiry teaching model. Frontiers in Sports and Active Living, 8, Article 1887661. https://doi.org/10.3389/fspor.2026.1887661

Image Credits: AI Generated

DOI: 10.3389/fspor.2026.1887661

Keywords: Argument-Driven Inquiry, college physical education, basketball teaching, motor-skill comprehension, classroom study, student questionnaire, inquiry-based learning, motor learning, physical education pedagogy, Implementation, approaches, improving

The research, conducted by Sejun Oh, Haemee Rim, Seongkyeong Kim, and Yong-Oh Lee, tackles a stubborn problem in mathematics education. Linear functions demand that students coordinate equations, graphs, and changing quantities simultaneously, and decades of research show that learners struggle to translate between these representations and to explain why a line behaves as it does. Formative feedback, the kind that tells students not just whether they are right but what to do next, is exactly what this kind of reasoning needs. Yet providing it mid-lesson is enormously demanding for teachers, because every student stumbles at a different moment on a different idea.

The team’s answer was the AlgeoMath module, a system built on a deliberate division of labor. For tasks in which students constructed or manipulated graphs, correctness was determined by rule-based evaluators and the AlgeoMath application programming interface, which checked structured outputs such as slope, intercept, and relational constraints rather than interpreting screenshots. For written explanation tasks, a large language model, specifically gpt-3.5-turbo-0125 accessed through OpenAI’s Chat Completions API, generated staged feedback aligned with rubric criteria and moderated templates. No fine-tuning was performed; instead, the model was constrained through prompt templates, rubric criteria, staged output fields, and expert-reviewed feedback structures.

The gamification layer was deliberately restrained. A short narrative frame, progress indicators, and a turtle helper organized the sequence of 21 core tasks, and after an incorrect submission the turtle displayed a hint while students could retry on the same screen. Crucially, the designers avoided leaderboards, badges, rankings, and competitive scoring entirely. The game elements existed not to dangle rewards but to make the feedback cycle visible: students always knew where they were, what the turtle was suggesting, and what their next action could be. Hints came in three levels, from a light nudge to re-check part of an answer, through a stronger cue, to a final stage that could include a concise direct explanation enabling the student to proceed.

The classroom results were striking on the implementation side. All 22 ninth-graders at School A completed all 24 tasks, while the 27 eighth-graders at School B completed 98.1 percent of their 21-task sequence. Across both schools, roughly 70.7 percent of task instances were solved on the first submission, with a mean of about 1.53 submission events per task. More telling was what happened after failure: among initially incorrect answers that received a second attempt, 57.5 percent were corrected on that attempt, and among those still wrong after two tries, 43.3 percent were fixed on the third. The staged hint cycle, in other words, was not decoration; students were actually reading the feedback and using it to revise.

The module’s embedded scoring revealed a consistent pattern across both classrooms. Overall performance was high at School A, where the material served as enrichment after the main linear functions content had been taught, with a mean score of 96.1 out of 100, compared with 78.8 at School B, where eighth-graders met the material during the regular unit. But the rubric-dimension profiles told a more interesting story: in both schools, covariational reasoning and mathematical communication and justification were the weakest dimensions. Students could often manipulate graphs and identify slopes, but coordinating how two quantities change together, and explaining why equal slopes produce parallel lines, remained the hardest part. These profiles give teachers a concrete map of where follow-up instruction should go.

Students’ immediate self-reports also shifted. Of eight survey items administered before and after the lesson to 48 matched pairs, six showed statistically significant increases after Holm correction for multiple comparisons: enjoyment of learning mathematics, interest in mathematics, self-perceived competence, self-confidence, understanding difficult content, and perceived learning speed, with effect sizes ranging from 0.41 to 0.64. Notably, two general liking-and-interest items did not change significantly, a selective pattern the authors interpret as reflecting the lesson’s task experience rather than a wholesale transformation of attitudes toward mathematics. Written reflections echoed this, with students describing the game-like format as enjoyable and the turtle hints as helping them find their mistakes and keep going.

The study’s most sobering numbers concern the quality of the AI’s judgments. When 100 constructed responses were sampled and two experienced educators independently rated them as high, middle, or low against the same rubric, the educators agreed with each other 92 percent of the time, with a Cohen’s kappa of 0.863. The automated judgments agreed exactly with each educator only 69 percent of the time, with kappa values around 0.44 to 0.45. The disagreements concentrated in borderline responses with partially articulated reasoning, pinpointing a specific engineering target: the system needs clearer partial-credit guidance and exemplars of intermediate-quality explanations before its judgments can be trusted without human review.

A separate expert audit added a second layer of scrutiny. A panel of 20 mathematics educators conducted three rounds of design-time review of the feedback template library, which comprised 122 templates linked to the linear function tasks. Twenty-one templates, or 17.2 percent, were flagged at least once, and the dominant problem was not mathematical error, which accounted for only one flag, but scaffold calibration: 17 of the flagged issues involved messages whose level of help did not match their assigned stage, such as a Level 3 slot containing a reflective question rather than direct teaching. Encouragingly, only four flagged templates actually appeared in the pilot logs, accounting for just 2.1 percent of the 1,409 feedback instances delivered, and all templates and prompt rules were updated after the audit.

The authors are careful about the limits of what this pilot shows. The two classes were convenience-sampled, differed in grade level and task exposure, and there was no comparison condition, so no causal claims about learning gains can be made, and independent achievement and transfer were not measured. The logs show submissions after feedback appeared but cannot prove students attended to each hint. Still, the contribution is a concrete design template for teacher-supervised AI feedback: exact mathematical checks where determinism is possible, language-model hints where explanation matters, rubric-based reports for teachers, and human moderation as a standing safeguard. Future work, the team suggests, should prioritize partial-credit exemplars, scaffold calibration, and richer teacher analytics, alongside controlled comparisons with delayed outcome measures to test whether the immediate confidence boost translates into durable mathematical understanding.

Subject of Research: GPT-assisted formative feedback in a gamified linear function learning module for middle school mathematics

Article Title: GPT-assisted formative feedback for middle-school linear function inquiry: Design and classroom evaluation of a gamified AlgeoMath module

Article References: Oh, S., Rim, H., Kim, S., & Lee, Y.-O. (2026). GPT-assisted formative feedback for middle-school linear function inquiry: Design and classroom evaluation of a gamified AlgeoMath module. Heliyon, 12(15), Article e45558. https://doi.org/10.1016/j.heliyon.2026.e45558

Image Credits: AI Generated

DOI: 10.1016/j.heliyon.2026.e45558

Keywords: formative feedback, large language models, GPT, linear functions, gamification, mathematics education, middle school, covariational reasoning, rubric scoring, human-AI agreement, dynamic graphing, classroom study