Actionable companion · Feedback & scaffolding

Learning with agency: The Influence of Affective Feedback Adaptive Learning System on Learning Engagement and Self-Directed Learning

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

Read original paper Search paper agent CC BY 4.0

Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Age / stage not established. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

The outbreak of the two-year corona virus has made a great difference on existing methods of learning and instruction. Online education has become a crucial role to maintain non-stop learning after the post-epidemic period. The advanced technologies and growing popularity of network equipment have made it easy to deploy remote connections. However, teachers still face challenges when they actually implement distance courses. During the learning process, the quality of learning can be improved if the researchers consider multiple factors, including emotions, attitudes, engagement, cognition, neuroscientific and cultural psychology. After analyzing these factors, instructors can have better understanding of students’ mental building and cognitive understanding in their process of learning, and be familiar with the way of interaction with students and appropriately adjust their teaching. Therefore, the current study established a learning system that aimed to understand learners’ emotional signals during learning by applying the adaptive-feedback emotional computing technology. The purpose of the system was to allow learners to (1) self-examine their learning condition, (2) enhance their self-directed learning, (3) help learners who are in negative learning emotions or settings to lower anxieties, and (4) promote their learning attitudes and engagement. Result showed that the system with the adaptive-feedback emotional computing technology has significantly improved the learning effectiveness, lowered learning anxieties and increased students’ self-directed learning.

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Design requires full-text appraisal

Stage: Age / stage not established. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

A total of 70 university students from southern Taiwan participated in this study, and were divided into two groups with 35 students (odd number) for the control group and 35 students (plural number) for the experimental group. None of the students had any experience with adaptive feedback learning systems. Both groups of students accessed learning material at home. Each student was confirmed to have a webcam and internet connections. A 6-week period of experiment was planned using the distance learning instruction. To ensure students’ equivalent learning background, the pre-test of learning achievement was distributed, along with the questionnaires of achievement emotions questionnaire (AEQ), learning anxiety, learning engagement, learning attitude and self-directed learning scale. The study used presentation video to introduce the purpose of the course and the adaptive feedback mechanism, and ensured students’ operations of the system after completing the introduction. Students independently assessed the learning material without any limitation. At the end of the course, they were invited to complete the post-test and post-questionnaires (see Figure 3).

Limitations and uncertainty · source passage

A total of 70 university students from southern Taiwan participated in this study, and were divided into two groups with 35 students (odd number) for the control group and 35 students (plural number) for the experimental group. None of the students had any experience with adaptive feedback learning systems. Both groups of students accessed learning material at home. Each student was confirmed to have a webcam and internet connections. A 6-week period of experiment was planned using the distance learning instruction. To ensure students’ equivalent learning background, the pre-test of learning achievement was distributed, along with the questionnaires of achievement emotions questionnaire (AEQ), learning anxiety, learning engagement, learning attitude and self-directed learning scale. The study used presentation video to introduce the purpose of the course and the adaptive feedback mechanism, and ensured students’ operations of the system after completing the introduction. Students independently assessed the learning material without any limitation. At the end of the course, they were invited to complete the post-test and post-questionnaires (see Figure 3).

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
Use plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

A 90-day district pilot

Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Hsin-Lan Liu; Tao-Hua Wang; Hao-Chiang Koong Lin; Chin-Feng Lai; Yueh-Min Huang. The Influence of Affective Feedback Adaptive Learning System on Learning Engagement and Self-Directed Learning. Frontiers in Psychology (2022-04-27). https://doi.org/10.3389/fpsyg.2022.858411

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.