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Cited by 23Teaching and Learning ProgrammingAugmented Reality ApplicationsProblem and Project Based Learning

Students as Designers of Augmented Reality: Impact on Learning and Motivation in Computer Science

Open access
Buchner, Josef, Kerres, Michael·2021·Multimodal Technologies and Interaction
DOI 10.3390/mti5080041
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Abstract

In this study, we report findings from the PCBuildAR project, in which students developed augmented reality (AR) artifacts following a guided design-based learning (DBL) approach. Sixty-two students participated in the study and were either in their first year to learn about computer science or were more experienced computer science students. In terms of learning performance, only the first-year students benefited from our guided DBL approach. In contrast, the experienced students were highly motivated to learn computer science not only immediately after the intervention, but also in the long term. For first-year students, this effect was only evident directly after the intervention. Overall, the guided DBL design proved to be effective for both motivation and learning, especially for younger students. For older learners, a better balance between guidance and autonomy is recommended.

AI snapshot

Written from the paper’s full text.

TL;DR

In the PCBuildAR project, a guided design-based learning approach had students create augmented reality artifacts for computer science, with different learning and motivational outcomes for first-year versus experienced students. First-year students improved learning performance, while experienced students maintained motivation longer but did not benefit in learning performance.

Key findings

  • First-year students improved learning performance from prior knowledge (Mdn = 2.00) to time point 1 (Mdn = 3.00; z = −2.16, p < 0.05, d = 0.62) and to time point 2 (Mdn = 3.00; z = −2.05, p < 0.05, d = 0.59). Experienced students worsened at time point 1 (Mdn = 2.00) compared with prior knowledge (Mdn = 3.00; z = −2.98, p < 0.05, d = 0.82), and their later improvement at time point 2 was not significant.
  • At time point 1, all students showed similar high values on all four motivational scales, with no significant differences between first-year and experienced students for intrinsic motivation, self-efficacy, self-determination, or career motivation.
  • At time point 2, experienced students scored significantly higher than first-year students on self-efficacy (mean rank = 35.00 vs. 26.32; U = 333.00, p < 0.05 one-tailed, d = 0.49) and self-determination (mean rank = 36.01 vs. 24.82; U = 295.50, p < 0.05, d = 0.64).
  • First-year students’ self-determination declined significantly from time point 1 (Mdn = 12.00) to time point 2 (Mdn = 10.00; z = −2.79, p < 0.01, d = 0.83); experienced students showed no significant decline or increase on any motivation scale between time points.

Methods

  • Field study in three Austrian “expert schools” with 62 students (42 girls; average age 11.84 years): 25 first-year computer science students (17 girls; M age = 10.16) and 37 experienced computer science students (25 girls; M age = 12.97). The guided DBL intervention ran from October 2019 to January 2020, with follow-up in May 2020.
  • Learning performance was assessed at prior knowledge, time point 1, and time point 2 using a five-point questionnaire (four single-choice and one multiple-choice question); the open-ended question was excluded because inter-rater reliability was unsatisfactory (Kappa = 0.36, 0.44, and 0.21). Motivation was measured at time points 1 and 2 with an adapted Science Motivation Questionnaire II (SMQ-II) covering intrinsic motivation, self-efficacy, self-determination, and career motivation (five items per scale, 0–4, maximum 20 per scale).
  • Data were analyzed in SPSS 27 using non-parametric methods: Mann-Whitney tests and Wilcoxon signed-rank tests, with effect sizes interpreted according to Cohen.

Limitations

  • Stated in the paper: results are tied to project specifics such as highly committed teachers and selected classes, so generalization is not possible; the study was a field study under real classroom conditions rather than a controlled randomized experiment; it focused only on DBL use separated from what happened before and after; and the small sample size limits pure quantitative analysis.
  • Evident in the text: the open-ended knowledge item was excluded due to poor inter-rater reliability; COVID-19 school closures prevented additional 3D modeling workshops, so the final 3D models were created by AR developers rather than students; and only two of eight planned computer components were completed as AR artifacts.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3390/mti5080041

Cites

56 of 56 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

10 of 25 cited sources checked · 7 supported · 0 flagged · accuracy not yet measured

  • Interpretationcitation

    The guided AR DBL approach may have helped the experienced students to not overestimate their abilities.

    “Hence, we can summarize that the guided AR DBL approach may have helped the experienced students to not overestimate their abilities, which was identified in [17] as a possible factor for the motivational decline.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [17] Vongkulluksn, 2018, Motivational Factors in Makerspaces: A Mixed Methods Study of Elementary School Students’ Situational Interest, Self-Efficacy, and Achievement Emotions, IJ STEM Ed. source
  • Premisecitation

    DBL can also lead to frustration, boredom, and anger, in addition to positive emotions.

    “This is also reflected in [39], in which the authors found that DBL can also lead to frustration, boredom, and anger, in addition to the more positive emotions of happiness and excitement.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [39] Zhang, 2020, Children’s Emotions in Design-Based Learning: A Systematic Review, J. Sci. Educ. Technol. source

      The Achievement Emotions section reports happiness and excitement alongside frustration and boredom in DBL studies. It mentions anger only as a survey item used to measure failure tolerance, not as an observed outcome of DBL. The Recommendations for Future Research section also cautions that evidence of specific negative-emotion incidents is minimal; the claim overstates the evidence for anger. (LLM, conf=0.83)

      Experimental — accuracy not yet measured
  • Premisecitation

    DBL appears to have a particularly positive effect on motivational and affective factors during learning.

    “In addition to learning, DBL appears to have a particularly positive effect on motivational and affective factors during learning [38,39].”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [38] Doppelt, 2008, Identifying Students’ Perceptions of the Important Classroom Features Affecting Learning Aspects of a Design-Based Learning Environment, Learn. Environ. Res. source
    • cites [39] Zhang, 2020, Children’s Emotions in Design-Based Learning: A Systematic Review, J. Sci. Educ. Technol. source
  • Findingown data

    Experienced students were highly motivated to learn computer science immediately after the intervention and in the long term.

    “In contrast, the experienced students were highly motivated to learn computer science not only immediately after the intervention, but also in the long term.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Changes in learning performance and motivation observed from pre- to post-intervention are attributable to the guided AR DBL intervention rather than to other factors such as maturation, regular classroom instruction, or testing effects.
    • Rests on an assumption: The first-year and experienced student groups are comparable in relevant respects other than age and prior computer science experience, so differences in outcomes can be attributed to age/experience.
    • Rests on an assumption: The long-term follow-up in May 2020, conducted during COVID-19 school closures via virtual classroom, did not differentially bias motivation responses between first-year and experienced students.
  • Findingown data

    In terms of learning performance, only first-year students benefited from the guided DBL approach.

    “In terms of learning performance, only the firstyear students benefited from our guided DBL approach.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The project-developed knowledge questionnaire validly measures students' knowledge of computer components and their functionalities.
    • Rests on an assumption: Changes in learning performance and motivation observed from pre- to post-intervention are attributable to the guided AR DBL intervention rather than to other factors such as maturation, regular classroom instruction, or testing effects.
    • Rests on an assumption: The first-year and experienced student groups are comparable in relevant respects other than age and prior computer science experience, so differences in outcomes can be attributed to age/experience.
  • May combine two claims. 21
  • Classified as probably not a substantial claim. 13

  • May combine two claims.

    Premisecitation

    DBL has a motivating effect, generates interest, and can arouse curiosity about professional topics.

    “Furthermore, DBL has a motivating effect, generates interest, and can arouse curiosity about professional topics.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [39] Zhang, 2020, Children’s Emotions in Design-Based Learning: A Systematic Review, J. Sci. Educ. Technol. source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    In a one-semester makerspace DBL approach, students' self-efficacy and situational interest decreased significantly over the period.

    “In [17] students, self-efficacy and situational interest decreased significantly over the period of a one-semester makerspace DBL approach.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [17] Vongkulluksn, 2018, Motivational Factors in Makerspaces: A Mixed Methods Study of Elementary School Students’ Situational Interest, Self-Efficacy, and Achievement Emotions, IJ STEM Ed. source

  • Probably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing). May combine two claims.

    Premisecitation

    There is little empirical evidence for the motivational effects of DBL and more research is needed.

    “More generally, the authors in [17] maintain that there is little empirical evidence for the motivational effects of DBL and thus more research is needed.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [17] Vongkulluksn, 2018, Motivational Factors in Makerspaces: A Mixed Methods Study of Elementary School Students’ Situational Interest, Self-Efficacy, and Achievement Emotions, IJ STEM Ed. source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The decrease in self-efficacy and situational interest was stronger for higher-level students than for lower-level students.

    “The decrease was stronger for higher-level students than for lower-level students.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [17] Vongkulluksn, 2018, Motivational Factors in Makerspaces: A Mixed Methods Study of Elementary School Students’ Situational Interest, Self-Efficacy, and Achievement Emotions, IJ STEM Ed. source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Learning with DBL and related methods is highly complex and can overwhelm learners.

    “Analysis of Kirschner et al. [16] showed that learning with DBL and related methods is highly complex, and can thus overwhelm learners.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [16] Kirschner, 2006, Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching, Educ. Psychol. source

      In “Current Research Supporting Direct Guidance” (page unavailable), the paper says that exploring a highly complex environment with little guidance may impose a heavy working-memory load detrimental to learning, particularly for novices. This supports the concern about related methods overwhelming learners, but the passages do not address DBL specifically or establish that all learners are overwhelmed. (LLM, conf=0.84)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Previous research on the motivational effect of DBL for STEM-learning supports this.

    “This is line with previous research on the motivational effect of DBL for STEM-learning [7,39,40].”
    Quote in the paperNot a substantial claim1 of 3 sources checked
    • cites [7] Doppelt, 2008, Engagement and Achievements: A Case Study of Design-Based Learning in a Science Context, J. Technol. Educ.
    • cites [39] Zhang, 2020, Children’s Emotions in Design-Based Learning: A Systematic Review, J. Sci. Educ. Technol. source
    • cites [40] Kiili, 2005, Content Creation Challenges and Flow Experience in Educational Games: The IT-Emperor Case, Internet High. Educ. source

  • May combine two claims.

    Premisecitation

    DBL can contribute to self-regulated learning and improves self-efficacy among learners.

    “Zhang, Markopoulos, and Bekker [39] conducted a systematic review and found that DBL can contribute to self-regulated learning and improves self-efficacy among learners.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [39] Zhang, 2020, Children’s Emotions in Design-Based Learning: A Systematic Review, J. Sci. Educ. Technol. source

      The Quasi-experimental Comparisons section reports that children in a DBL class had higher self-efficacy in science, and the Conclusion lists self-efficacy among the advantages found in the review. The passages do not establish that DBL contributes to self-regulated learning; a reference title mentions that topic, but does not report it as a DBL finding. Page numbers were not available in our copy. (LLM, conf=0.84)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Findingown data

    First-year students' intrinsic motivation, self-efficacy, and career motivation values decreased (not significantly) with small to medium effect sizes.

    “On the contrary, first-year students' intrinsic motivation, self-efficacy, and career motivation values decreased (not significantly) with small to medium effect sizes.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Experienced students reported higher values four months after the AR DBL intervention, with no decline compared to the results at time point 1.

    “Here, the experienced students reported higher values four months after the AR DBL intervention, with no decline compared to the results at time point 1.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingreasoning

    The self-determination scale values for first-year students diminished significantly, with a large effect size.

    “The self-determination scale values even diminished significantly, and the effect size was large.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    At time point 2, significant differences with medium effect sizes were found for intrinsic motivation, self-efficacy, and self-determination, and not for career motivation.

    “More interestingly, at time point 2, significant differences with medium effect sizes were found for intrinsic motivation, self-efficacy, and self-determination, and not for career motivation.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    At time point 2, experienced students improved their performance on the learning test again, but compared to their prior knowledge, this improvement was not significant.

    “At time point 2, the experienced students improved their performance on the learning test again (Mdn = 3.00); however, compared to their prior knowledge, this improvement was not significant, Wilcoxon signed-rank test, z = -0.54,p > 0.05, d = 0.14.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    One workshop was not enough to teach the students the necessary skills to design more complex 3D models.

    “One workshop was not enough to teach the students the necessary skills to design such more complex models.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    In terms of motivation to learn computer science, the results are promising for experienced students, because motivation remained high in the longer term.

    “In terms of motivation to learn computer science, the results are promising for experienced students, because motivation remained high in the longer term.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed changes in motivation are caused by the guided AR DBL approach rather than other factors.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    The results of the open-ended question were not included in further analysis.

    “Consequently, we do not include the results of the open-ended question in the further analysis.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductown data

    A Shapiro-Wilk test showed that most of the dependent variable's distributions differed significantly from the norm, so non-parametric methods were used to analyze the data.

    “A Shapiro-Wilk test showed that most of the dependent variable's distributions differed significantly from the norm; therefore, non-parametric methods were used to analyze the data.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductreasoning

    The results of this study are based on the specifics of the project, such as highly committed teachers and their selection of classes, so generalization of the results is not possible.

    “The results of this study are based on the specifics of our project, such as the highly committed teachers and their selection of the classes.Therefore, a generalization of the results is not possible.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    Overall, the guided DBL design was effective for both motivation and learning, especially for younger students.

    “Overall, the guided DBL design proved to be effective for both motivation and learning, especially for younger students.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Changes in learning performance and motivation observed from pre- to post-intervention are attributable to the guided AR DBL intervention rather than to other factors such as maturation, regular classroom instruction, or testing effects.

  • May combine two claims.

    Premisecitation

    If these processes are still in progress during a performance assessment, cognitive overload can occur, resulting in poorer learning performance.

    “If these processes are still in progress during a performance assessment, cognitive overload can occur, resulting in poorer learning performance [57].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [57] Kalyuga, 2007, Expertise Reversal Effect and Its Implications for Learner-Tailored Instruction, Educ. Psychol. Rev. source

  • May combine two claims.

    Interpretationown data

    The intervention was perceived by the first-year students as autonomy-enhancing, whereas the period after the intervention, when traditional teaching was resumed, undermined this autonomy again.

    “The intervention was perceived by the first-year students as autonomy-enhancing, whereas the period after the intervention, when”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Another limitation is the sole focus on the DBL use, which was separated from what happened before and after this teaching approach.

    “Another limitation concerns the sole focus on the DBL use, which was separated from what happened before and after this teaching approach.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    The conflict processes were completed during performance measurement at time point 2, and the learners were able to retrieve their previous performance.

    “The conflict processes were completed during performance measurement at time point 2, and the learners were able to retrieve their previous performance.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    DBL is more effective than traditional learning because learners work on real problems in teams and pursue their own ideas, leading to higher engagement and better learning outcomes.

    “DBL is more effective than traditional learning because learners work on real problems in teams and are allowed to pursue their own ideas and approaches, which in turn leads to higher engagement and thus better learning outcomes [18,34,35].”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [18] Chen, 2019, Revisiting the Effects of Project-Based Learning on Students’ Academic Achievement: A Meta-Analysis Investigating Moderators, Educ. Res. Rev. source
    • cites [34] Yang, 2016, Student-Generated Content in College Teaching: Content Quality, Behavioural Pattern and Learning Performance: Student-Generated Content in College Teaching, J. Comput. Assist. Learn. source
    • cites [35] Gijlers, 2013, Learning by Designing Instruction in the Context of Simulation-Based Inquiry Learning, Educ. Technol. Soc.

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation

    In self-determination theory, the experience of autonomy is a central factor of intrinsic motivation, in addition to competence and relatedness.

    “In the self-determination theory of motivation [59], the experience of autonomy is a central factor of intrinsic motivation, in addition to the experience of competence and relatedness.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [59] Ryan, R.M., and Deci, E.L. (2020). Intrinsic and Extrinsic Motivation from a Self-Determination Theory Perspective: Definitions, Theory, Practices, and Future Directions. Contemp. Educ. Psychol., 101860. source

  • Classified as probably not a substantial claim.

    Interpretationcitation

    The guided instructional design did not allow the experienced students to align their prior knowledge with the newly learned information, resulting in a cognitive conflict.

    “One explanation for this effect might be that the guided instructional design does not allowed the experienced students to align their prior knowledge with the newly learned information, resulting in a cognitive conflict [57].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [57] Kalyuga, 2007, Expertise Reversal Effect and Its Implications for Learner-Tailored Instruction, Educ. Psychol. Rev. source

  • Probably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing).

    Premisecitation

    Comparisons of DBL between students with different characteristics are requested, investigating both affective and cognitive learning outcomes.

    “Comparisons of DBL between students with different characteristics are also requested in [18], investigating both affective and cognitive learning outcomes.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [18] Chen, 2019, Revisiting the Effects of Project-Based Learning on Students’ Academic Achievement: A Meta-Analysis Investigating Moderators, Educ. Res. Rev. source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    The plan to hold more workshops on 3D modeling was not possible due to COVID-19-related school closures shortly after the third workshop.

    “The plan was to hold more workshops on 3D modeling.This was no longer possible due to the COVID-19-related school closures shortly after our third workshop.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    The guided AR DBL approach may have contributed to maintaining high motivation in the longer term and stimulated a sustained interest in computer science education.

    “In our case, the guided AR DBL approach may have contributed to maintaining high motivation in the longer term and stimulated a sustained interest in computer science education.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed changes in motivation are caused by the guided AR DBL approach rather than other factors.

  • May combine two claims.

    Interpretationown data

    For first-year students, a long-term effect was not as clear, but the guided AR DBL approach contributed to students' motivation to learn computer science after the intervention.

    “For the first-year students, a long-term effect was not as clear, but we found evidence that the guided AR DBL approach contributed to student's motivation to learn computer science after the intervention.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The observed changes in motivation are caused by the guided AR DBL approach rather than other factors.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The small sample size is a limitation of a pure quantitative analysis.

    “Additionally, the small sample size should be mentioned as a limitation of a pure quantitative analysis.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductown data

    Cronbach's alpha values for each SMQ-II scale were satisfactory across time points: intrinsic motivation α = 0.87-0.89, self-efficacy α = 0.82-0.91, self-determination α = 0.87-0.90, career motivation α = 0.91-0.91.

    “Cronbach's alpha values for each scale can be considered satisfactory across time points: intrinsic motivation scale α = 0.87-0.89,self-efficacy scale α = 0.82-0.91,selfdetermination scale α = 0.87-0.90,career motivation scale α = 0.91-0.91.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Self-determination is an essential factor in human motivation that correlates with the other motivational factors measured in this study, especially intrinsic motivation.

    “Self-determination is an essential factor in human motivation that correlates with the other motivational factors measured in this study [47], especially the sense of intrinsic motivation.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [47] Glynn, 2011, Science Motivation Questionnaire II: Validation with Science Majors and Nonscience Majors, J. Res. Sci. Teach. source
32 of 32

  • The paper relies on this without support.

    The project-developed knowledge questionnaire validly measures students' knowledge of computer components and their functionalities.

    “Knowledge was assessed with two instruments: a questionnaire developed by the project team and an open-ended question.The content of the questionnaire was adapted to the topics covered in class and in the workshops, i.e., knowing the functionalities of the computer components and understanding how they interact in a running computer system.”

  • The paper relies on this without support.

    Changes in learning performance and motivation observed from pre- to post-intervention are attributable to the guided AR DBL intervention rather than to other factors such as maturation, regular classroom instruction, or testing effects.

    “Overall, the guided DBL design proved to be effective for both motivation and learning, especially for younger students.”

  • The paper relies on this without support.

    The first-year and experienced student groups are comparable in relevant respects other than age and prior computer science experience, so differences in outcomes can be attributed to age/experience.

    “Twenty-five students (17 girls) were part of the first-year computer science class (M age = 10.16,SD age = 0.37) and 37 students (25 girls) were part of the experienced computer science class (M age = 12.97, SD age = 0.80).”

  • The paper relies on this without support.

    The long-term follow-up in May 2020, conducted during COVID-19 school closures via virtual classroom, did not differentially bias motivation responses between first-year and experienced students.

    “At the last appointment, schools were still closed due to the COVID-19 pandemic, so the questionnaire was completed by students during a virtual classroom session.”

  • The authors state this as an assumption.

    The motivation of the younger first-year students to learn computer science would be higher compared to the experienced students after the AR DBL intervention.

    “Our second research question is based on previous findings reported in [17]; hence, we assumed that the motivation of the younger first-year students to learn computer science would be higher compared to the experienced students after the AR DBL intervention.”

  • The paper relies on this without support.

    The observed improvements in learning performance are caused by the guided AR DBL approach rather than other factors.

    “Hence, we can conclude that the guided AR DBL approach helped the first-year students with little prior knowledge to improve their learning performance.”

  • The paper relies on this without support.

    The observed changes in motivation are caused by the guided AR DBL approach rather than other factors.

    “In our case, the guided AR DBL approach may have contributed to maintaining high motivation in the longer term and stimulated a sustained interest in computer science education.”
7 of 7

Statistics recomputed

Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.

  • Consistentz = -3.48reported p < .01 · recomputed p < .001

    “,z = -3.48,p < .01”

  • Consistentz = -1.47reported p > .05 · recomputed p = .142

    “,z = -1.47,p > .05”

  • Consistentz = -1.72reported p > .05 · recomputed p = .085

    “,z = -1.72,p > .05”

  • Consistentz = -2.16reported p < .05 · recomputed p = .031

    “z = -2.16,p < .05”

  • Consistentz = -2.05reported p < .05 · recomputed p = .040

    “z = -2.05,p < .05”

  • Consistentz = -2.98reported p < .05 · recomputed p = .003

    “z = -2.98,p < .05”

  • Consistentz = -0.54reported p > .05 · recomputed p = .589

    “z = -0.54,p > .05”

  • Consistentz = -0.3reported p > .05 · recomputed p = .764

    “,z = -0.30,p > .05”

  • Consistentz = -0.09reported p > .05 · recomputed p = .928

    “,z = -0.09,p > .05”

  • Consistentz = -0.17reported p > .05 · recomputed p = .865

    “,z = -0.17,p > .05”

  • Consistentz = -0.49reported p > .05 · recomputed p = .624

    “,z = -0.49,p > .05”

  • Consistentz = -1.43reported p > .05 · recomputed p = .153

    “,z = -1.43,p > .05”

  • Consistentz = -0.69reported p > .05 · recomputed p = .490

    “,z = -0.69,p > .05”

  • Consistentz = -1.87reported p < .05 · recomputed p = .061

    “,z = -1.87,p < .05”

    The paper states a one-tailed test

  • Consistentz = -2.41reported p < .05 · recomputed p = .016

    “,z = -2.41,p < .05”

  • Consistentz = -0.92reported p > .05 · recomputed p = .358

    “,z = -0.92,p > .05”

  • Consistentz = -1.75reported p > .05 · recomputed p = .080

    “,z = -1.75,p > .05”

  • Consistentz = -1.01reported p > .05 · recomputed p = .312

    “,z = -1.01,p > .05”

  • Consistentz = -2.79reported p < .01 · recomputed p = .005

    “,z = -2.79,p < .01”

  • Consistentz = -0.22reported p > .05 · recomputed p = .826

    “,z = -0.22,p > .05”

  • Consistentz = -0.59reported p > .05 · recomputed p = .555

    “,z = -0.59,p > .05”

  • Consistentz = -0.06reported p > .05 · recomputed p = .952

    “,z = -0.06,p > .05”

  • Consistentz = -0.36reported p > .05 · recomputed p = .719

    “z = -0.36,p > .05”

23 of 23

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Discussion incorrectly calls intrinsic-motivation difference significant

    Major

    The results report no significant between-group difference in intrinsic motivation at time point 2 (p > 0.05). The discussion instead includes intrinsic motivation among the significant differences, changing the reported motivational conclusion.

  • Discussion gives different experienced-student performance means

    Major

    The discussion gives prior-knowledge and time-point-1 means of 3.25 and 2.46 for experienced students. Table 1 reports 3.30 and 2.35, respectively.

  • Learning-performance result refers to an undefined time point 3

    Minor

    The methods define the follow-up in May 2020 as time point 2, not time point 3. The learning-performance results call that comparison time point 3.

  • First-year students did not have higher means on all four motivation scales

    Minor

    The discussion says first-year students had higher values on all four scales at time point 1. Table 1 shows their intrinsic-motivation mean was slightly lower: 13.72 versus 13.73.

4 of 4

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. Mathews, 2010, Using a Studio-Based Pedagogy to Engage Students in the Design Ot Mobile-Based Media, Engl. Teach. Pract. Crit., 9, 87

Cited by

Sorted by citations

Related

Authors
Buchner, J., & Kerres, M.
Affiliations
University of Duisburg-Essen (DE)
Corresponding author
Buchner, Josef
ORCID
Venue
Multimodal Technologies and Interaction
Publisher
MDPI AG
Published
2021-07-24
Volume
5
Issue
8
Pages
41
ISSN
2414-4088 (ISSN-L)
DOI
10.3390/mti5080041
OpenAlex
W3183746270
Cited by
23
References
56
Citations by year
2026: 3 · 2025: 7 · 2024: 3 · 2023: 6 · 2022: 3 · 2021: 1
FWCI
2.75
Type
journal article
Language
en
Open access
Licence
Locations
  • Multimodal Technologies and Interaction (publishedVersion, cc-by)
  • Universitätsbibliographie, Universität Duisburg-Essen (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DuEPublico (University of Duisburg-Essen) (submittedVersion, cc-by)
  • PHSG #Proforis (St.Gallen University of Teacher Education) (submittedVersion)
Funders
Innovation Foundation for Education (Innovationsstiftung für Bildung) (DLL-2_72)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
  • Social Sciences › Education › Problem and Project Based Learning
Keywords
augmented reality, design-based learning, computer science education, student motivation, first-year students, learning performance

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-11-02.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-11-02.

Cited sources are checked by gpt-6-sol.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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