Blended learning boosts university students’ basketball skills and key fitness markers — evidence from a 12‑week CRCT

Table of Contents

1. Key Highlights
2. Introduction
3. The trial at a glance: design, participants and measures
4. What changed — headline results and effect sizes
5. How blended learning changed the learning environment
6. Why certain fitness markers improved more than others
7. Practical blueprint: what a blended university PE course should include
8. Recommendations to tighten fitness outcomes that lagged
9. Scaling blended PE across campus: institutional considerations
10. Limitations in the evidence and priority research questions
11. Broader implications for university physical education
12. Next steps for practitioners and researchers
13. FAQ

Key Highlights

• A 12‑week blended-learning basketball course at Jiangsu University of Science and Technology produced large, statistically significant gains in muscular endurance (pull‑ups), explosive power (standing long jump), lung capacity and sport‑specific skills (set shots and half‑court dribbling/shot) compared with a traditional teacher‑led class.
• The blended format combined short pre‑class MOOC videos, differentiated in‑class confrontational drills and post‑class online quizzes and video reflections; it shifted class time from lecture to individualized skill practice and yielded large effect sizes for technical outcomes (d > 1.5).
• The intervention had limited impact on aerobic endurance and produced ambiguous signals in BMI and short‑sprint outcomes, suggesting blended PE should be paired with targeted high‑intensity and explosive training and better body‑composition monitoring to meet broader fitness goals.

Introduction

Universities are formative environments for physical habits, social skills and the capacity to self‑manage learning. Physical education in higher education therefore carries weight well beyond the gym: better fitness correlates with improved academic performance, emotional regulation and lifelong health. Yet many university physical‑education programs still rely on teacher‑centered, single‑mode instruction and face resource constraints that reduce individualized practice and limit students’ progress.

A cluster randomized controlled trial (CRCT) conducted at Jiangsu University of Science and Technology tested whether a blended‑learning model — combining short MOOC videos before class, differentiated in‑class confrontational drills and automated post‑class quizzes and reflections — could accelerate gains in both objective fitness markers and basketball skills among first‑year male students. The study enrolled 69 volunteers, completed with 30 students per arm after attrition, and used established national fitness tests alongside basketball‑specific skill measures.

The trial’s outcomes illuminate where blended learning genuinely adds value and where curriculum design must be sharpened. The following analysis presents the study’s design and results, explains the mechanisms likely driving the effects, examines the limits of the intervention, and offers a practical blueprint for institutions that intend to scale blended approaches in sport and physical education.

The trial at a glance: design, participants and measures

The investigators used a cluster randomized controlled design, allocating two intact first‑year basketball classes to either a blended‑learning experimental group (EG) or a traditional teacher‑led control group (CG). Baseline characteristics were well matched: mean age 18.6 years, mean height 175.3 cm and mean BMI 22.3 kg/m^2. The 12‑week intervention followed a consistent weekly class structure for both groups (90 minutes per session) and the same instructor, with scheduling separated to avoid cross‑group contamination.

Key components of the experimental intervention:

• Pre‑class: short MOOC videos (under 10 minutes) hosted on a Chinese MOOC platform (icourse163), online quizzes and pre‑class feedback submissions. The platform tracked viewing time, quiz completion and forum activity.
• In‑class: warm‑ups, targeted technical instruction, confrontational small‑group practice (3v3 or 4v4) and intensive physical training exercises (e.g., interval runs, box jumps, weighted squats).
• Post‑class: automated quizzes with immediate feedback, online reflection tasks, and peer evaluation via videos uploaded to the LMS and discussed in WeChat groups.

Control group instruction followed a conventional teacher‑centered format: explanation and demonstration in class, group drills, and offline self‑directed pre/post practice with fewer structured online elements.

Objective outcomes were measured before and after the 12‑week period using China’s National Student Physical Health Standard (2014 revision). Fitness tests included BMI, lung capacity (vital capacity), pull‑ups (1‑minute max), standing long jump, 50‑meter sprint and 1,000‑meter run. Basketball skills were assessed with two validated measures: a 10‑attempt set‑shot test from the free‑throw line and a timed half‑court dribbling‑and‑shooting course. Evaluations achieved excellent inter‑rater reliability (ICC ≈ 0.9).

Statistical approach: paired t‑tests for within‑group changes, independent t‑tests for between‑group post‑test comparisons, and Cohen’s d to interpret effect sizes (0.2 small, 0.5 medium, 0.8 large). Significance threshold p < 0.05.

What changed — headline results and effect sizes

Both the blended and traditional formats produced pre‑to‑post improvements across multiple fitness measures, but the blended model yielded substantially larger gains in several domains.

Notable between‑group differences favoring blended learning:

• Pull‑ups: very large effect (p < 0.001, d = 2.03). EG increased from 5.50 ± 1.72 to 9.60 ± 1.81 reps; CG improved marginally from 5.13 ± 1.83 to 5.97 ± 2.03.
• Standing long jump: large effect (p < 0.001, d = 1.04). EG improved more than CG on explosive‑power distance.
• Lung capacity (vital capacity): large effect (d ≈ 1.14), with significant post‑test differences.
• Basketball set shot (successes out of 10): large sport‑skill effect (d = 1.63); EG climbed from ~3.2 to 6.8 successful shots, CG from ~2.9 to 4.7.
• Half‑court dribbling and shooting: EG showed a large effect (d = 1.55); CG exhibited only a small improvement (d = 0.41).

Ambiguous or limited outcomes:

• 1,000‑meter run (aerobic endurance): no significant between‑group difference (p = 0.597). Both groups improved slightly over time but gains did not differ meaningfully.
• 50‑meter sprint and BMI: between‑group Cohen’s d values were negative in the study’s reported calculations (d = −0.30 for sprint, d = −0.54 for BMI). These negative signs reflect the way group differences were computed (EG − CG) and indicate the direction of change favored the experimental group for BMI and sprint time; interpretation of magnitude and practical meaning requires attention to training specificity and body composition.

These results show a consistent pattern: when blended learning sped up the acquisition of technical, explosive and strength‑endurance capacities, it did so strongly. Endurance measures requiring higher training frequency or specific continuous load stimuli did not respond differentially to a once‑weekly course structure within 12 weeks.

How blended learning changed the learning environment

The experimental program reallocated classroom time. Pre‑class micro‑videos transferred much of the “what” (knowledge of technique, common errors, demonstrations) to the online environment. That freed in‑class minutes for “how” practice: more repetitions under realistic, confrontational conditions, immediate individualized feedback and small‑group competition. Two features matter most for the results achieved:

1. Deliberate practice with scaffolding

• Students arrived with a shared baseline understanding (video preview and quizzes), so classroom practice focused on high‑quality repetition and error correction. Teachers could scaffold tasks within each student’s zone of proximal development (ZPD), pushing skill difficulty incrementally while offering timely correction — a potent combination for motor learning and technical refinement.

2. Self‑regulated learning and reflective cycles

• The blend reinforced self‑regulated learning behaviors: students planned (watch videos, preview tasks), monitored (quiz feedback, teacher corrections), and reflected (upload practice videos, peer evaluation). Frequent, immediate feedback (both automated and teacher‑mediated) accelerated skill consolidation and helped students prioritize practice that addressed individual weaknesses.

These changes align with established learning theories. Self‑regulated learning predicts improved outcomes when learners manage the learning process; blended designs amplify opportunities for planning, monitoring and reflection. Sociocultural theory explains how the shift from transmission to scaffolded practice fosters learning within the student’s proximal development range.

Why certain fitness markers improved more than others

The blended design produced markedly better outcomes in muscular endurance (pull‑ups), explosive power (standing long jump) and lung capacity. Several interacting explanations explain this pattern.

Specificity of practice and in‑class intensity

• Blended classes devoted more in‑class minutes to targeted strength and power exercises (e.g., pull‑up sets, box jumps, weighted squats) and to drill configurations that replicate explosive, short‑duration basketball actions. Those high‑quality, focused repetitions drive neuromuscular adaptations that raise pull‑up counts and jump distance within 12 weeks.

Improved coaching feedback and technique correction

• Small‑group confrontational drills and video review permit immediate correction of technical flaws that limit mechanical efficiency — for example, arm swing and hip extension during vertical efforts or breathing mechanics for vital capacity. Corrective feedback improves movement economy and produces rapid performance gains.

Dose and recovery alignment

• Strength and power gains can emerge from relatively modest weekly exposure if the sessions are high in intensity and directed at specific motor patterns. Aerobic endurance, by contrast, depends heavily on cumulative weekly volume and sustained steady‑state or long‑interval stimulus; one weekly session at university PE intensity is not sufficient to produce measurable between‑group differences in 1,000‑meter times over 12 weeks.

Why sprint and BMI signals were ambiguous

• Short‑sprint performance (50 m) benefits both from technique and explosive power. Despite improvements in jumping and strength markers, sprint times did not show the same between‑group advantage. That likely reflects limited sprint‑specific training volume and an emphasis in blended classes on skill confrontation over continuous sprint repetitions.
• BMI is a blunt measure of body composition. The EG’s greater reductions in BMI may reflect fat loss, muscle gain or both. Because BMI combines weight and height without distinguishing fat and lean mass, modest increases in muscle can mask or offset fat loss. Without direct body‑composition data (e.g., DXA, bioimpedance), BMI changes remain difficult to interpret.

Practical blueprint: what a blended university PE course should include

The Jiangsu trial offers a replicable model. Universities seeking to implement blended PE should adopt an integrated curriculum that unites online microlearning, structured in‑person practice and reflective assessment. The following blueprint translates the trial components into actionable steps:

Pre‑class (asynchronous)

• Short, focused instruction videos (6–10 minutes) emphasizing one or two technical points and common mistakes. Provide slow‑motion and multi‑angle demonstrations for complex skills like layups or crossover moves.
• A brief pre‑class quiz (4–8 items) that gives immediate corrective feedback. Require video evidence of a short warm‑up or self‑practice drill weekly to encourage accountability.
• Clear learning objectives and a checklist (e.g., “today you will improve arm position on set shot” or “you will execute 3x 10s maximal box jumps with full hip extension”).

In‑class (synchronous, 90 minutes)

• Preparation (20 min): dynamic warm‑up focused on mobility, joint activation and sport‑specific explosive readiness. Review common errors from pre‑class quizzes.
• Core practice (60 min): differentiated stations and small‑sided confrontational drills (e.g., 2v2/3v3), with the teacher circulating to provide individualized cues. Include targeted physical sets: e.g., 3–4 sets of explosive box jumps, 3 sets of weighted squats, specific sprint drills with technical focus (not mere laps).
• Conclusion (10 min): active recovery, technical playback (teacher demonstrates student videos with corrections), assignment of a focused post‑class reflection.

Post‑class (asynchronous)

• Short automated quiz summarizing critical techniques and physiological tips.
• Video upload of a specific skill attempt for teacher feedback and for two peer evaluators to comment (structured rubric).
• Analytics dashboard: track individual viewing time, quiz scores, attendance and practice uploads to identify low engagement and to trigger personalized outreach.

Assessment and measurement

• Maintain objective fitness assessments at baseline and at periodic intervals (e.g., every 6–12 weeks). Beyond BMI, incorporate at least one body‑composition measure (skinfold calipers or bioimpedance) and simple field devices for jump height (jump mats) and sprint timing (laser gate or smartphone app).
• Use validated skill tests (e.g., set shot success, timed dribble course) and ensure inter‑rater reliability by training evaluators.

Technology and communication

• Choose an LMS or MOOC platform that records viewing analytics and quiz completion and supports video submissions. Complement with a social messaging channel for rapid reminders (WeChat in the trial, alternatives include WhatsApp, Teams or LMS forums).
• Protect privacy: obtain explicit consent for video uploads and set retention policies.

Teacher preparation

• Train instructors to design microlearning content, to run differentiated small‑group drills and to interpret online analytics. Effective feedback—brief, specific, and timely—drives the largest skill gains.

Scalability considerations

• Start with a pilot cohort, use analytics to refine video content and station design, and document teacher time required for video grading and individualized feedback. Consider training advanced undergraduates as peer coaches to scale individualized feedback loops.

Recommendations to tighten fitness outcomes that lagged

The study shows blended models are powerful for skill acquisition and strength/power gains but less so for endurance and sprint speed under the trial’s constraints. To close that gap implement the following:

1. Increase training frequency

• Twice weekly sessions, or supplemental supervised conditioning sessions, will produce the cumulative load necessary for aerobic adaptations measured by the 1,000‑meter run.

2. Explicitly program sprint‑specific and high‑intensity work

• Integrate structured short‑sprint training with progressive overload (e.g., resisted sprints, overspeed drills) and technical sprint mechanics during in‑class time.

3. Add targeted plyometric and explosive programming

• Prescribe progressive plyometrics and loaded jump variations with attention to volume and recovery to enhance sprint transfer.

4. Monitor body composition directly

• Replace or supplement BMI with field body‑composition measures and track lean mass changes to understand the interplay between muscle gain and perceived BMI shifts.

5. Use individualized load management

• Combine LMS analytics with wearable data (heart rate, GPS) where feasible to tailor intensity and ensure sufficient stimulus without overreaching.

Scaling blended PE across campus: institutional considerations

Adoption at scale requires aligning policy, staffing and technology budgets.

Curriculum alignment

• Embed blended PE within degree requirements and learning outcomes. Define which fitness domains the course must address (e.g., motor skills, strength/power, endurance) and allocate weekly minutes accordingly.

Teacher capacity and development

• Invest in faculty workshops for microvideo production, formative assessment design and small‑group instruction techniques. Provide technical support for content hosting and analytics.

Resource allocation

• Budget for LMS licenses, short‑format video production, wearable monitoring (selectively), and tools for jump/sprint measurement. Where budgets are constrained, low‑cost smartphone timing apps and inexpensive force plates or jump mats can suffice.

Equity and access

• Ensure all students have access to required devices and internet. Provide on‑campus viewing stations or loaner devices when necessary. Design asynchronous content mindful of data costs and captioning requirements for accessibility.

Data governance

• Create clear policies for student video storage, consent, and who can view submitted practice materials. Anonymize analytics before reporting for program evaluation.

Assessment and credentialing

• Move beyond single end‑of‑term exams. Use formative online quizzes, video portfolios and peer evaluation as part of a multifaceted grade that reflects process and outcomes.

Limitations in the evidence and priority research questions

The trial is rigorous and practical, but limits must guide cautious interpretation and future study design.

Sample and setting

• The participants were all male first‑year students from a single Chinese university. Female students, older cohorts and students in other cultural contexts may respond differently to blended formats.

Training dose and duration

• The intervention delivered one scheduled PE session per week. Training frequency likely constrained improvements in aerobic endurance. Longer interventions or increased session frequency merit evaluation.

Outcome measurement

• BMI was used as the primary anthropometric indicator; direct measures of body composition (DXA, BIA, skinfolds) would clarify how lean mass and fat mass shifted. Similarly, more granular sprint and strength diagnostics (e.g., force‑velocity profiles, 20‑m split times) would reveal mechanisms.

Long‑term retention and transfer

• The trial tested outcomes after 12 weeks. Follow‑up studies should measure retention of skill gains and the persistence of fitness improvements, along with transfer to game performance and health markers (blood pressure, lipid profile).

Cost‑effectiveness and teacher workload

• Producing high‑quality microvideos and providing individualized feedback requires instructor time. Economic analyses should assess the incremental cost per unit improvement and explore peer‑feedback models to reduce teacher burden.

Equity and engagement

• Research should document how blended PE affects students with low baseline fitness, those with disabilities, or students who lack digital access, and identify strategies to ensure equitable benefit.

Broader implications for university physical education

The Jiangsu trial demonstrates that blended learning can deliver measurable, meaningful gains in both motor skill competence and selected physical capacities when course design deliberately couples online microlearning with in‑class practice. That outcome matters for universities seeking to fulfill multiple goals: improving student health, increasing engagement in lifelong physical activity, and offering flexible learning pathways.

Key policy implications:

• Reframe PE evaluation to reward process and technical proficiency alongside raw fitness outcomes.
• Prioritize investments that free teacher time for individualized coaching rather than simply expanding lecture content.
• Recognize blended learning as a tool to personalize instruction: use platform analytics to identify at‑risk students, tailor practice prescriptions and document progress.

Implementation at scale must respect the tradeoffs observed: blended formats enhance skill uptake and targeted strength/power but need complementary programming if the institutional aim is to raise campus aerobic fitness and reduce cardiometabolic risk at population level.

Next steps for practitioners and researchers

For practitioners:

• Pilot blended modules in one sport or skill area, collect objective data and iteratively adapt content based on analytics and student feedback.
• Pair blended classes with supervised conditioning sessions or supplemental online conditioning programs for students aiming to improve endurance and speed.

For researchers:

• Test blended PE in female cohorts and across a broader range of sports.
• Compare weekly frequencies (1× vs 2× vs 3×) and include direct body‑composition measures.
• Investigate cost‑effective models of feedback delivery (peer coaching, AI‑assisted video review) that preserve learning gains while reducing instructor time.

FAQ

Q: What exactly made the blended approach more effective for basketball skills? A: The combination of brief, focused pre‑class videos and automated quizzes ensured students arrived prepared. That allowed instructors to dedicate in‑class time to purposeful, confrontational drills and individualized feedback. Repeated high‑quality practice under realistic game conditions and scaffolded corrections accelerated motor learning, increasing shot consistency and dribble‑to‑finish efficiency.

Q: Does blended PE replace the need for face‑to‑face instruction? A: No. Blended learning reallocates tasks: cognitive understanding and demonstration are handled asynchronously while face‑to‑face time emphasizes guided practice and coaching. The in‑person component remains essential for hands‑on correction, social play and physical conditioning.

Q: Why didn’t the program improve 1,000‑meter run performance more? A: Aerobic endurance improves through repeated steady‑state or interval sessions with sufficient weekly volume. The trial’s once‑weekly PE session did not provide enough cumulative stimulus for robust between‑group differences in the 1,000‑meter run over 12 weeks. Increasing frequency or adding conditioning modules would be necessary to change endurance outcomes.

Q: Should universities measure BMI to track PE outcomes? A: BMI provides a coarse snapshot and can be misleading when muscle mass changes. For active student populations, pair BMI with body‑composition measures (skinfold calipers, bioimpedance) or functional performance tests to gain clearer insight into health and fitness changes.

Q: How much instructor time does blended PE require? A: Initial time investment is front‑loaded: producing microvideos, setting up quizzes, and creating rubrics. Once content is in the LMS, incremental time focuses on reviewing student videos, providing targeted feedback and managing analytics. Peer review and structured rubrics can reduce instructor workload while maintaining quality.

Q: Can blended PE benefit students who are less motivated or lower‑fit? A: Yes, if implemented with accessible content, scaffolded tasks and strong feedback. The self‑regulated elements (previews, quizzes, reflection uploads) can increase accountability and motivation. However, deliberate strategies to engage lower‑fit students — such as progressive difficulty, positive reinforcement and peer support — are essential.

Q: Are there privacy concerns with video uploads and analytics? A: Absolutely. Obtain informed consent for video capture and set clear policies on who can view submissions, retention duration and deletion. Store data securely and anonymize analytics when used for program evaluation.

Q: What immediate changes can a PE teacher make to integrate blended elements? A: Start small: replace one lesson’s lecture with a 6–8 minute recorded demonstration and quiz, then use the freed class time for extra guided practice. Introduce one structured video‑upload assignment per month and a simple rubric for peer feedback. Monitor engagement via the LMS and iterate.

Q: Would blended learning work in other sports? A: Evidence from multiple studies indicates blended approaches can enhance skill acquisition across sports (e.g., soccer, badminton, volleyball). Success depends on thoughtful integration of microlearning, targeted in‑class practice and feedback loops specific to each sport’s movement demands.

Q: What is the single most important takeaway for program designers? A: Use technology strategically to move declarative instruction out of the gym and convert face‑to‑face time into deliberate, scaffolded practice with immediate feedback. Pair that pedagogy with appropriate training frequency and targeted conditioning for the fitness outcomes you intend to prioritize.

The Jiangsu University CRCT demonstrates that blended learning is not a novelty; it is a practical, evidence‑backed method to sharpen technical skill development and improve selected fitness domains in higher‑education physical education. To capture its full potential, institutions must combine intelligent course design with targeted conditioning, robust evaluation and faculty development so blended PE becomes both scalable and effective across diverse student populations.