Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: Self-assessments, attitudes, and motivational orientations towards the use of digital media in teaching a comparison between student teachers of different subject clusters

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.

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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): Higher education. Verify the participant population and task before transferring the findings.

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

Abstract Findings from research on the education of teachers show that student teachers lack competence in the utilization of digital media and often fail to see the added value in using digital concepts in classrooms. Teacher training institutions are faced with the central challenge of providing student teachers and teachers with adaptive learning opportunities for their competence in digital areas. In the context of various teaching subjects, this raises questions about specific starting points and the actual needs of students, as well as university course offerings. The present study tackles this topic and explores the commonalities and differences between student teachers from three subject clusters: science, technology, engineering, and mathematics (STEM); language literature arts (LLA); and social sciences (SOCS). The questionnaire-based study examines student teachers' self-assessments of their abilities in terms of digital media, attitudes, and motivational orientations towards the use of digital media in teaching. Group comparisons show that STEM student teachers rate themselves better in competence facets such as technological knowledge, technological content knowledge, and technological pedagogical knowledge, although their attitudes and motivation towards learning with digital media do not differ. Despite the different interrelation effects of investigated influencing factors on motivation depending on subject clusters, the findings suggest the promotion of technological competencies and positive attitudes to increase motivation. Keywords: TPACK, Digitalisation, Teaching, Attitudes, Motivational orientation, Digitalisation-related competencies, Student teachers

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Survey / observational study

Stage: Higher education. 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

The present study addresses the research desiderata described above and aims to provide information on the subject-specific assessments, attitudes, and motivations of STEM, LLA, and SOCS student teachers in the context of their digitalisation-related competencies (research objective 1). Furthermore, the study aims to show the influences of self-assessments on the efficacy and attitudes of student teachers towards using digital media for learning and their motivational orientation towards their use of digital media in teaching (research objective 2).

Limitations and uncertainty · source passage

This study’s results thus confirm existing findings from comparable studies [23,65] and furthermore extend them with respect to the identified disparities between the three subgroups: STEM, LLA, and SOCS teaching students. Thus, it can be stated for the overall sample that in addition to the training of technology-related competencies and media pedagogical competencies, the formation of attitudes during the course of studies appears to be of central importance in the promotion of a positive motivational orientation towards the use of digital media among student teachers. When considering individual subject clusters, it is observable that technological didactic knowledge (TCK) is relevant to the explanation of motivational orientation for STEM and general education student teachers and not for LLA student teachers. The causes of this phenomenon can only be conjectured. Clarifying these facts in an evidence-based manner is a central desideratum for future research.

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.

  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

  1. 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.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. 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.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. 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

Marcus Brändle; Christina Sotiriadou; Bernd Zinn. Self-assessments, attitudes, and motivational orientations towards the use of digital media in teaching a comparison between student teachers of different subject clusters. Heliyon (2023-08-29). https://doi.org/10.1016/j.heliyon.2023.e19516

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.