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Cited by 33Intelligent Tutoring Systems and Adaptive LearningVisual and Cognitive Learning ProcessesMathematics Education and Teaching Techniques

Factors affecting learning of vector math from computer-based practice: Feedback complexity and prior knowledge

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Heckler, Andrew F., Mikula, Brendon D.·2016·Physical Review Physics Education Research
DOI 10.1103/physrevphyseducres.12.010134
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Abstract

In a computer-based learning environment providing a brief general explanation of the correct solution process leads to stronger learning gains than telling students about the correctness of their answer.

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Integrity signals

Registry

Crossref · 10.1103/physrevphyseducres.12.010134

Cites

37 of 38 references verified0 citation errors · 0 not checkable

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  • Feedback effectiveness depends on contextual factors (learning environment, learner characteristics) and intrinsic features (modality, delivery mode, source, content).

    “Its impact depends on contextual factors such as the learning environment and learner characteristics (Heckler and Mikula, 2016), as well as intrinsic features of the feedback itself, including modality, delivery mode, source, and, most importantly, its content (Ramadan Elbaioumi Shaddad and Jember, 2024; Graham et al., 2015; Rüdian et al., 2025).”

    From Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning (2026) · cites this paper as Heckler and Mikula, 2016

    The Summary and General Discussion reports that feedback effectiveness varied with students’ prior knowledge, supporting the learner-characteristics part of the clause cited to Heckler and Mikula. In “Varying complexity of feedback,” the authors caution that findings may not apply to a different domain and context, but they do not establish that differences between learning environments change feedback effectiveness. The later list of feedback features is attributed to other citations, not this paper. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • A particularly effective form of feedback provides the correct answer with an explanation of why it is correct.

    “A particularly effective form of feedback provides the correct answer with an explanation of why it is the correct answer (Heckler and Mikula, 2016; Van der Kleij et al., 2015; Butler et al., 2013; Wisniewski et al., 2020).”

    From Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning (2026) · cites this paper as Heckler and Mikula, 2016

    The Experiment 2 results section reports the largest gain over control for feedback combining the correct response and an explanation, and finds some evidence of added benefit. But it found no significant difference between that combination and explanation feedback alone; the benefit of more complex feedback was concentrated among students with low prior knowledge. The unqualified description of this combination as particularly effective overstates the finding. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • The benefits of elaborated feedback are particularly pronounced for learners with lower prior knowledge and lower course grades.

    “Further, Heckler and Mikula (2016) found that the benefits of elaborated feedback were particularly pronounced for learners with lower prior knowledge and lower course grades.”

    From Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning (2026) · cites this paper as Heckler and Mikula, 2016

  • There is robust evidence that elaborate feedback with correct answers facilitates learning.

    “On the one hand, there is robust evidence that elaborate feedback with correct answers facilitates learning (Heckler and Mikula, 2016; Van der Kleij et al., 2015; Wisniewski et al., 2020).”

    From Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning (2026) · cites this paper as Heckler and Mikula, 2016

  • Elaborated feedback with correct answer produces larger learning gains, particularly for students with lower prior knowledge.

    “Such elaborated feedback with the correct answer can deliver information directly and efficiently, and it can be further elaborated to include explanations, strategies, and guidance for self-regulation (Wisniewski et al., 2020), which shows effectiveness in producing larger learning gains, particularly for students with lower prior knowledge (Heckler and Mikula, 2016).”

    From Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning (2026) · cites this paper as Heckler and Mikula, 2016

Self-check

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Cites

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Research works printed but not linked: 6

  1. M. J. Hannafin, K. M. Hannafin, and D. W. Dalton, Feed- back and emerging instructional technologies, in Interac- tive Instruction and Feedback, edited by J. V. Demposey and G. C. Dales (Educational Technology, Englewood Cliffs, 1993), pp. 263-286.
  2. S. Narciss and K. Huth, How to design informative tutoring feedback for multimedia learning, in Instructional Design for Multimedia Learning, edited by H. M. Niegemann, D. Leutner, and R. Brunken (Waxman, Munster, NY, 2004), pp. 181-195.
  3. A. F. Heckler, B. Mikula, and R. Rosenblatt, Student accuracy in reading logarithmic plots: the problem and how to fix it, Proceedings of the 2013 IEEE Frontiers in Education Conference, pp. 1066-1071.
  4. B. D. Mikula and A. F. Heckler, The effectiveness of brief, spaced practice on student difficulties with basic and essential engineering skills, Proceedings of the 2013 IEEE Frontiers in Education Conference, pp. 1059-1065.
  5. B. D. Mikula and A. F. Heckler, Student difficulties with trigonometric vector components persist in multiple pop- ulations, Proceedings of the 2013 Physics Education Research Conference, Portland, OR, pp. 253-256.
  6. See Supplemental Material at http://link.aps.org/ supplemental/10.1103/PhysRevPhysEducRes.12.010134 for examples of training materials for vector essential skills and the DotCross vector assessment.

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Authors
Heckler, A. F., & Mikula, B. D.
Affiliations
The Ohio State University (US)
ORCID
Heckler, Andrew F.
Venue
Physical Review Physics Education Research
Publisher
American Physical Society (APS)
Published
2016-06-09
Volume
12
Issue
1
ISSN
2469-9896 (ISSN-L)
DOI
10.1103/physrevphyseducres.12.010134
OpenAlex
W2414190984
Cited by
32
References
37
Citations by year
2026: 4 · 2025: 8 · 2024: 4 · 2023: 2 · 2021: 5 · 2020: 1 · 2019: 3 · 2018: 3 · 2017: 2
FWCI
2.20
Type
journal article
Language
en
Open access
Licence
Locations
  • Physical Review Physics Education Research (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Science Foundation (DMR-1420451)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
  • Social Sciences › Education › Mathematics Education and Teaching Techniques
Keywords
vector mathematics, feedback complexity, prior knowledge, physics learning, computer-assisted instruction

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