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Cited by 0Motivation and Self-Concept in SportsEducation, Achievement, and Giftednessscientometrics and bibliometrics research

Mapping the Research Landscape: A Comprehensive Bibliometric Analysis of Self-Determination Theory in Higher Education

Open access
Lin, Chen, Zhang, Yifan, Wang, Lanye, Wu, Jienan·2024·Proceedings of the 2024 2nd International Conference on Information Education and Artificial Intelligence
DOI 10.1145/3724504.3724576
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Abstract

In recent years, Self-DeterminationTheory (SDT) proposed by Deci and Ryan has been a major theoretical framework for understanding and describing college students' motivation, due to its emphasis on individual subjective initiative, ability, relationship satisfaction, etc. This helps students identify their motivation, happiness, and the importance of grades. Because SDT plays an irreplaceable and extremely important role in stimulating students' intrinsic motivation, and plays an important decision-making role in academic performance, it is necessary and legitimate to carry out the verification of SDT in colleges and universities. However, there have been many studies on SDT using traditional methods, but those new teaching models based on SDT such as SOP are in the blank stage. Therefore, this study used holistic literature statistical analysis to draw the overall outline of SDT research in higher education, used VOSviewer to conduct statistical analysis of 862 papers from Scopus, and found common networks and subject clusters. We came up with six theme clusters, two of which are "academic achievement", and the other five are "autonomous support" and "digital learning platform role". This shows that the current era attaches great importance to SDT, even including the change of online teaching forms under the impact of the novel coronavirus epidemic. It is not difficult to infer that SDT can be implemented in targeted applications in various educational scenarios. There is no doubt that SDT has a lot of space worth exploring in the combination of digital multimedia environment and teaching theory. This work can serve as a basis for future work and provide direction for practice in order to generate an environment conducive to promoting and protecting teacher motivation.

AI snapshot

Written from the paper’s full text.

TL;DR

This bibliometric study maps self-determination theory (SDT) research in higher education using 862 Scopus records and VOSviewer keyword co-occurrence analysis. It identifies six thematic clusters, with dominant themes around student motivation, academic achievement, and digital/online learning.

Key findings

  • From 864 initial Scopus records, 2 were removed for errata/classification errors, leaving 862 papers; 57 keywords met the ≥10 occurrence threshold and formed 508 co-occurrence relationships.
  • VOSviewer fractional counting produced six clusters: academic achievement and performance; motivation and education; application of empirical research; online learning and technology; student engagement and motivation; and higher education and pedagogy.
  • The most frequent keywords included self-determination theory (380 occurrences, 628 total link strength), higher education (222, 416), motivation (100, 194), transition (39, 70), post-secondary education (37, 91), online learning (30, 82), intrinsic motivation (29, 65), and autonomy (28, 76).
  • The paper concludes that dominant clusters are student motivation, academic achievement, and digital learning impact, and notes the COVID-19 pandemic’s role in shifting education toward online/blended platforms.

Methods

  • Scopus was searched across title, abstract, and keywords with no restrictions on time, language, source type, or document format; the search ended August 29, 2024, yielding 864 records, reduced to 862 after removing 2 records.
  • Records were cleaned and standardized with OpenRefine 3.7.6 and analyzed with VOSviewer 1.6.20 using fractional counting.
  • Keyword co-occurrence analysis used a threshold of 10 occurrences per keyword, producing the network and six thematic clusters.

Limitations

  • The paper states that the breadth and depth of SDT integration with emerging educational technologies need further expansion, and that more work is needed to explore the positive and negative impacts of new digital learning technologies; it also calls for interdisciplinary approaches.
  • Evident from the text: the study relies on a single database (Scopus), and the printed search string is repetitive/garbled with many duplicate “higher education” terms, which may affect reproducibility.
  • Evident from the text: the analysis is bibliometric co-occurrence only, with no qualitative validation or full-text synthesis of the clustered literature.

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

Integrity signals

Registry

Crossref · 10.1145/3724504.3724576

Cites

30 of 30 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

14 of 26 cited sources checked · 2 supported · 0 flagged · accuracy not yet measured

  • Premisecitation
    § 4.3 detailed explanation of clusters · p. 3

    Research on relatedness and competence has focused on improving the psychological needs satisfaction of higher education students, especially in online education.

    “research on these variables has focused on improving the psychological needs satisfaction of higher education students, especially in online education [24], [25], [26].”
    Quote in the paperNot a substantial claim1 of 3 sources checked
    • cites [24] [24] J. L. Howard, G. R. Slemp, and X. Wang, 'Need Support and Need Thwarting: A Meta-Analysis of Autonomy, Competence, and Relatedness Supportive and Thwarting Behaviors in Student Populations', Pers Soc Psychol Bull , vol. 00, no. 0, pp. 1-22, Jan. 2024, doi: 10.1177/01461672231225364. - source

      The Need-Supportive Behaviors section says competence- and relatedness-supportive strategies are likely to satisfy students’ corresponding psychological needs. It does not establish that this research has focused on higher education students, especially in online education; the paper discusses student populations more broadly. The supplied passages have no page numbers. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
    • cites [25] [25] L. M. Larson et al. , 'The academic environment and faculty well-being: the role of psychological needs', Journal of Career Assessment , vol. 27, no. 1, pp. 167-182, Feb. 2019, doi: 10.1177/1069072717748667. - source
    • cites [26] [26] D. Wood, 'The impact of students' perceived relatedness and competence upon their motivated engagement with learning activities: a self-determination theory perspective', Jul. 2016. Accessed: Feb. 16, 2024. [Online]. Available: https://www.semanticscholar.org/paper/The-impact-of-students'-perceivedrelatedness-and-%3A-Wood/1a8bbb98694f005ce620a915e4fe4d456e0b65b8 -

      Not checked in the free run.

  • Interpretationcitation
    § 4.3 detailed explanation of clusters · p. 3

    Research in Cluster 2 is mainly related to understanding and intrinsic motivation.

    “show that the research in this cluster is mainly related to understanding and intrinsicmotivation [18].”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [18] [18] E. R. Pelikan et al. , 'Distance learning in higher education during COVID-19: The role of basic psychological needs and intrinsic motivation for persistence and procrastination-a multi-country study', PLoS ONE , vol. 16, no. 10, p. e0257346, Oct. 2021, doi: 10.1371/journal.pone.0257346. - source
  • Findingcitation
    § 4.3 detailed explanation of clusters · p. 3

    Cluster 5 includes words such as 'academic performance', 'covid-19', 'gaming', and 'students involvement'.

    “The cluster includes words such as 'academic performance', 'covid-19', 'gaming', and 'students involvement' [4].”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [4] [4] G. Valentina, 'A longitudinal study of school climate: Reciprocal effects with student engagement and burnout', Psychology in the Schools , vol. 59, no. 8, pp. 1521-1537, 2022, doi: 10.1002/pits.22691. - source

      The School Climate and Student Engagement section discusses students’ involvement in school activities, but does not identify a word cluster. Searches for the claimed cluster terms, including “covid-19” and “gaming,” found no passage reporting those words as members of a cluster. (LLM, conf=0.76)

      Experimental — accuracy not yet measured
  • Findingown data
    § 3.2 data cleaning and harmonization · p. 2

    After deleting two records due to errata and classification errors, 862 records were suitable for bibliometric analysis.

    “Two records were deleted due to errata and classification errors, resulting in a total of 862 records suitable for bibliometric analysis.”
    Quote in the paperSubstantial claim
  • Findingown data
    § 4.3 detailed explanation of clusters · p. 2

    The average annual publication year of the Cluster 1 topic is 2016-2021.

    “The average annual publication year of this topic is 2016-2021”
    Quote in the paperNot a substantial claim
  • Classified as probably not a substantial claim. 26
  • May combine two claims. 11
  • The quote only partly states this claim. 1

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

    Interpretationreasoning
    § 4.3 detailed explanation of clusters · p. 3

    These keywords suggest the use of innovative teaching methods, such as flipped classrooms and gamification, to improve student participation and learning results.

    “These keywords suggest the use of innovative teaching methods, such as flipped classrooms [29] and gamification [30], in order to better improve student participation and learning results.”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites [29] [29] X. Wang, 'A Conceptual Review on EFL Teachers' Motivation and Engagement in Flipped Classrooms: A Social Networking Platform', Language Related Research , vol. 14, no. 3, pp. 239-264, 2023, doi: 10.29252/LRR.14.3.10. -

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [30] [30] M. Sailer and M. Sailer, 'Gamification of in-class activities in flipped classroom lectures', Brit J Educational Tech , vol. 52, no. 1, pp. 75-90, Jan. 2021, doi: 10.1111/bjet.12948. - source

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

    Premisecitation
    § 4.3 detailed explanation of clusters · p. 2

    Increasing autonomy for students will make them more motivated to learn and achieve better grades or outcomes.

    “The academic consensus points out that increasing autonomy for students will make them more motivated to learn and achieve better grades or outcomes [10], [15].”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites [10] [10] G. Rayner and T. Papakonstantinou, 'The use of self-determination theory to investigate career aspiration, choice of major and academic achievement of tertiary science students', International Journal of Science Education , vol. 42, no. 10, pp. 1635-1652, Jul. 2020, doi: 10.1080/09500693.2020.1774092. - source
    • cites [15] [15] M.-P. Borjas, C. Ricardo, E. L. Escalante-Barrios, J. Valencia, and J. Aparicio, 'Financial independence and academic achievement: are there key factors of transition to adulthood for young higher education students in colombia?', Front. Psychol. , vol. 11, p. 1330, Jun. 2020, doi: 10.3389/fpsyg.2020.01330. - source

      The Introduction section on academic performance and autonomy reports a positive relationship between autonomy and academic performance. But the Discussion says the study cannot establish that increasing autonomy improves its outcome measure, and the cited passages do not establish that autonomy increases motivation to learn. Page numbers are not available in our copy. (LLM, conf=0.88)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Premisecitation
    § 1 Introduction · p. 1

    SDT has generated increasing interest in higher education over the past decade because of its potential to increase student motivation and engagement, which in turn improves student academic performance.

    “SDT has generated increasing interest in higher education over the past decade because of its potential to increase student motivation and engagement in school, which in turn improves student academic performance [4].”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [4] [4] G. Valentina, 'A longitudinal study of school climate: Reciprocal effects with student engagement and burnout', Psychology in the Schools , vol. 59, no. 8, pp. 1521-1537, 2022, doi: 10.1002/pits.22691. - source

      Section 1.2 associates student engagement with educational success and with completion of secondary and postsecondary school. It does not say that interest in SDT has increased in higher education over the past decade, or that SDT increases motivation and engagement, which in turn improves academic performance. The paper's conclusion instead concerns the effects of school climate on engagement. (LLM, conf=0.86)

      Experimental — accuracy not yet measured

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation
    § 1 Introduction · p. 1

    The breadth and depth of SDT integration with emerging educational technologies is limited and needs further expansion.

    “the breadth and depth of its integration with emerging educational technologies (such as AI-based personalized learning tools, gamification, and digital platforms) need to be further expanded, and their role in supporting or hindering independent learning needs to be explored more [8].”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [8] [8] J. Bi and J. Chen, 'Analysis of the effects of the 'pedagogy space technology' framework on university student's learning efficiency', International Journal of Emerging Technologies in Learning (iJET) , vol. 17, no. 15, Art. no. 15, Aug. 2022, doi: 10.3991/ijet.v17i15.33175. - source

      The State of the art section discusses internal motivation and technology-supported learning in the PST framework, while the Conclusion proposes refining strategies to motivate students. Neither section addresses self-determination theory (SDT) or assesses the breadth and depth of its integration with emerging educational technologies. Searches for SDT, gamification, and that proposed research gap found no supporting passage in our copy. (LLM, conf=0.75)

      Experimental — accuracy not yet measured

  • May combine two claims. The quote only partly states this claim.

    Findingcitation
    § 4.3 detailed explanation of clusters · p. 3

    Cluster 6 focused on higher education teaching and included terms such as 'motivation,' 'self-efficacy,' and 'STEM'.

    “The fourth group focused on higher education teaching and included terms such as 'motivation,' 'self-efficacy,' and 'STEM' [31].”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [31] [31] S. Zhou and W. Wu, 'A study on the relationship between higher vocational students' perceived teacher support and learning engagement: The chain mediation ICIEAI 2024, December 20-22, 2024, Kaifeng, China of academic self-efficacy and professional commitment', Nurture , vol. 17, no. 4, pp. 595-606, Sep. 2023, doi: 10.55951/nurture.v17i4.435. Chen Lin et al. source

      The Research Tools (section 3.3) and Conclusion (section 5.1) discuss academic self-efficacy and learning engagement among higher vocational students. Searches for the fourth group, Cluster 6, and STEM found no passage identifying a group of higher-education teaching terms. The overlap in ‘motivation’ and ‘self-efficacy’ does not establish the claimed grouping. (LLM, conf=0.77)

      Experimental — accuracy not yet measured

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

    Premisecitation
    § 4.3 detailed explanation of clusters · p. 3

    Global education shifted to online platforms during the COVID-19 pandemic.

    “with global education shifting to online platforms during the COVID-19 pandemic [27], [28].”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [27] [27] J. Holzer et al. , 'Higher education in times of covid-19: University students' basic need satisfaction, self-regulated learning, and well-being', AERA Open , vol. 7, p. 233285842110031, Jan. 2021, doi: 10.1177/23328584211003164. - source

      The section “Distance Education and Basic Psychological Need Satisfaction in Times of COVID-19” says universities worldwide rapidly moved face-to-face classes to online learning systems. That supports a global shift in higher education, but the claim’s broader wording, “global education,” extends beyond the university setting the passage describes. (LLM, conf=0.85)

      Experimental — accuracy not yet measured
    • cites [28] [28] A. Malik and W. Ahmad, 'Antecedents of Soft-Skills in Higher Education Institutions of Saudi Arabia Study under COVID-19 Pandemic', CE , vol. 11, no. 07, pp. 1152-1161, 2020, doi: 10.4236/ce.2020.117086. - source

      The Problem Statement says COVID-19 shifted regular studies to online studies using platforms such as Zoom and Moodle. The Abstract specifically describes that shift in Saudi higher education; the paper does not establish that education shifted online globally. (LLM, conf=0.82)

      Experimental — accuracy not yet measured

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

    Premisecitation
    § 5 Discussion · p. 3

    The COVID-19 pandemic facilitated the transformation of online and blended learning models.

    “The COVID-19 pandemic has facilitated the transformation of online and blended learning models”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [27] [27] J. Holzer et al. , 'Higher education in times of covid-19: University students' basic need satisfaction, self-regulated learning, and well-being', AERA Open , vol. 7, p. 233285842110031, Jan. 2021, doi: 10.1177/23328584211003164. - source

      The section “Distance Education and Basic Psychological Need Satisfaction in Times of COVID-19” says the pandemic prompted universities to move rapidly from face-to-face classes to online learning systems. That supports the online-learning part of the claim, but the passage does not establish a transformation of blended learning models. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites [28] [28] A. Malik and W. Ahmad, 'Antecedents of Soft-Skills in Higher Education Institutions of Saudi Arabia Study under COVID-19 Pandemic', CE , vol. 11, no. 07, pp. 1152-1161, 2020, doi: 10.4236/ce.2020.117086. - source

      The Problem Statement (page not provided) says COVID-19 shifted regular studies to online studies, and the Abstract says classes moved online. Neither passage supports the claim about blended learning models. (LLM, conf=0.87)

      Experimental — accuracy not yet measured

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

    Premisecitation
    § 1 Introduction · p. 1

    SDT is widely used in higher education, dividing different intrinsic motivations and studying the social and environmental determinants that affect motivational states.

    “SDT is widely used in higher education, which divides different intrinsic motivations and studies the social and environmental determinants that affect motivational states [3].”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [3] [3] K. Abduljalil and Y. Zainuddin, 'Intrinsic and Extrinsic Motivation as Attitude Factors towards Adoption of Accounting Information System (AIS) In Libyan SMEs', IJARAFMS , vol. 5, no. 1, p. Pages 161-170, Jun. 2015, doi: 10.6007/IJARAFMS/v5i1/1553. - source

      The indexed abstract describes intrinsic and extrinsic motivation in accounting-information-system adoption by Libyan SMEs, not SDT in higher education. Searches for SDT in higher education and for social and environmental determinants returned only references, with no passage addressing the attributed claim. (LLM, conf=0.72)

      Experimental — accuracy not yet measured

  • Classified as probably not a substantial claim.

    Premisecitation
    § 3.3 data analysis · p. 2

    VOSviewer can create a co-occurrence relationship network of keyword occurrence, citation model, and the literature structure of the whole research field.

    “which was used to create the co-occurrence relationship network of keyword occurrence, citation model, and the literature structure of the whole research field [12].”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [12] [12] H. Arruda, E. R. Silva, M. Lessa, J. Domício Proença, and R. Bartholo, 'VOSviewer and Bibliometrix', Journal of the Medical Library Association: JMLA , vol. 110, no. 3, p. 392, Jul. 2022, doi: 10.5195/jmla.2022.1434. - source

      The VOSviewer section says the software maps co-occurrence and citation links, and the Domain Visualization section describes maps that show the broader structure of a field. The claim’s suggestion of coverage of the *whole* research field goes further: the Data Analysis from multiple sources section cautions that VOSviewer uses one database at a time and that no single database holds all the literature in a field. (LLM, conf=0.83)

      Experimental — accuracy not yet measured

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

    Findingown data
    § 4.3 detailed explanation of clusters · p. 3

    Cluster 2 includes the keywords 'academicmotivation', 'extrinsicmotivation' and 'intrinsicmotivation'.

    “The keywords 'academicmotivation', 'extrinsicmotivation' and 'intrinsicmotivation' in this cluster”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Findingnone given
    § 6 Conclusion · p. 5

    This paper uses bibliometrics to draw a thematic landscape map around SDT core clusters in higher education literature and identifies significant trends and thematic clusters.

    “This paper uses bibliometrics to draw a thematic landscape map around SDT core clusters in higher education literature and identifies significant trends and thematic clusters.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: New teaching models based on SDT such as SOP are in the blank stage.
    • Rests on an assumption: Research on the breadth and depth of co-occurrence analysis literature metrology methods of SDT research has not been fully carried out.
    • Rests on an assumption: Existing studies have not conducted author-based keyword co-occurrence analysis to study cluster analysis of SDT research results.

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

    Findingown data
    § 4.2 explanation of algorithms · p. 2

    The keyword co-occurrence analysis contained 508 co-occurrence relationships.

    “a total of 508 co-occurrence relationships”
    Quote in the paperNot a substantial claim

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

    Findingown data
    § 4.3 detailed explanation of clusters · p. 2

    Cluster 1 is made up of words such as 'academic achievement,' 'cooperation,' and 'post-secondary programs.'

    “The cluster is made up of words such as 'academic achievement,' 'cooperation,' and 'post-secondary programs.'”
    Quote in the paperNot a substantial claim

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

    Findingown data
    § 4.2 explanation of algorithms · p. 2

    In the author keyword co-occurrence analysis, 57 keywords reached the threshold of 10 occurrences.

    “57 keywords reach the threshold in Figure 1”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data
    § 4.3 detailed explanation of clusters · p. 2

    The analysis generated six categories, each classifying research topics involved in SDT in higher education.

    “a total of 6 categories are generated, and each category is a classification of various research topics involved in SDT in HE.”
    Quote in the paperSubstantial claim

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

    Findingown data
    § 3.2 data cleaning and harmonization · p. 2

    The initial Scopus search returned 864 records.

    “The search ended on August 29, 2024, with a total of 864 records in the initial search.”
    Quote in the paperNot a substantial claim

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

    Findingown data
    § 4.3 detailed explanation of clusters · p. 3

    Cluster 3 involves terms such as 'engineering education', 'Online learning', 'autonomy theory' and 'higher education'.

    “This cluster involves terms such as 'engineering education', 'Online learning', 'autonomy theory' and 'higher education'”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationreasoning
    § 6 Conclusion · p. 5

    These clusters represent how the industry is responding to challenges such as the COVID-19 pandemic.

    “These clusters represent how the industry is responding to challenges such as the COVID-19 pandemic”
    Quote in the paperNot a substantial claim

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

    Premisecitation
    § 1 Introduction · p. 1

    Self-determination theory is a macro theory of human motivation, happiness, and personality development proposed by Deci and Ryan.

    “Self-determinism theory (SDT) is a macro theory of human motivation, happiness, and personality development proposed by Deci and Ryan [1].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [1] [1] E. L. Deci and R. M. Ryan, 'Self-determination theory: A macrotheory of human motivation, development, and health.', Canadian Psychology / Psychologie canadienne , vol. 49, no. 3, pp. 182-185, Aug. 2008, doi: 10.1037/a0012801. - source

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

    Premisecitation
    § 4.2 explanation of algorithms · p. 2

    The fractional counting method weakens the impact of the occurrence of keywords in too many documents.

    “the fractional counting method weakens the impact of the occurrence of keywords [14] in too many documents.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [14] [14] S. K. Sood, N. Kumar, and M. Saini, 'Scientometric analysis of literature on distributed vehicular networks: VOSViewer visualization techniques', Artif Intell Rev , vol. 54, no. 8, pp. 6309-6341, Dec. 2021, doi: 10.1007/s10462-021-09980-4. - source

  • Classified as probably not a substantial claim.

    Premisecitation
    § 1 Introduction · p. 1

    Optimal functional performance and well-being depend on meeting three basic psychological needs: autonomy, competence, and relationships.

    “This theory holds that optimal functional performance and well-being depend on meeting three basic psychological needs - autonomy, competence, and relationships [2].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [2] [2] R. M. Ryan and E. L. Deci, 'Self-determination theory', in Encyclopedia of Quality of Life and Well-Being Research , F. Maggino, Ed., Cham: Springer International Publishing, 2023, pp. 6229-6235. doi: 10.1007/978-3-031-17299-1\_2630. - source

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given
    § 6 Conclusion · p. 5

    SDT can support people to actively develop suitable learning environments.

    “The contribution of this paper is that SDT can support people to actively develop suitable learning environments.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationreasoning
    § Abstract · p. 1

    SDT can be implemented in targeted applications in various educational scenarios.

    “It is not difficult to infer that SDT can be implemented in targeted applications in various educational scenarios.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationreasoning
    § 5 Discussion · p. 3

    STEM education and inclusive practices in higher education reflect growing interest in applying SDT to specialized and diverse learning environments.

    “STEM education and inclusive practices in higher education reflect the growing interest in applying SDT to specialized and diverse learning environments.”
    Quote in the paperNot a substantial claim

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

    Interpretationreasoning
    § 5 Discussion · p. 3

    The cluster of terms 'online learning', 'autonomous' and 'self-regulated learning' illustrates the education sector's response to digital transformation and the need for transformational adjustments in teaching methods to maintain students' intrinsic motivation.

    “The cluster of terms 'online learning', 'autonomous' and 'self-regulated learning' illustrates the response of the education sector to digital transformation and the need for transformational adjustments in teaching methods to maintain the intrinsic motivation of students.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation
    § 1 Introduction · p. 1

    Academic environments with greater autonomy, competence, and connectivity are positively correlated with higher learning, higher retention and completion rates, and improved satisfaction of students' graduates.

    “Many empirical studies have shown that academic environments with greater autonomy, competence, and connectivity are positively correlated with higher learning, higher retention and completion rates, and improved satisfaction of students' graduates [5].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [5] [5] Y. J. Joo, H.-J. So, and N. H. Kim, 'Examination of relationships among students' self-determination, technology acceptance, satisfaction, and continuance intention to use K-Moocs', Computers & Education , vol. 122, pp. 260-272, Jul. 2018, doi: 10.1016/j.compedu.2018.01.003. - source

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

    Premisecitation
    § 3.1 data source and search strategy · p. 2

    Scopus is a large and widely recognized multidisciplinary database and covers a large number of academic literature.

    “The data for this bibliometrics study come from the Scopus database, which is a large and widely recognized multidisciplinary database and covers a large number of academic literature [11].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [11] [11] A. Ahmi, 'OpenRefine: An approachable tool for cleaning and harmonizing bibliographical data', AIP Conference Proceedings , vol. 2827, no. 1, p. 030006, Sep. 2023, doi: 10.1063/5.0164724. - source

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

    Premisecitation
    § 2 Literature review · p. 2

    SDT mainly focuses on revealing intrinsic motivation related to learning situations, extrinsic motivation of autonomy, and other factors affecting individual mental health.

    “SDT mainly focuses on revealing the intrinsic motivation related to learning situations, the extrinsic motivation of autonomy, and other factors affecting individual mental health [10].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [10] [10] G. Rayner and T. Papakonstantinou, 'The use of self-determination theory to investigate career aspiration, choice of major and academic achievement of tertiary science students', International Journal of Science Education , vol. 42, no. 10, pp. 1635-1652, Jul. 2020, doi: 10.1080/09500693.2020.1774092. - source

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

    Premisecitation
    § 4.3 detailed explanation of clusters · p. 3

    Early learning experiences, especially their influence on first-year students' motivation, have a positive effect on later academic achievement.

    “related researchers [21] have proposed that early learning experiences (especially the important influence on first-year students' motivation) have a positive effect on later academic achievement.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [21] [21] D. Noyens, V. Donche, L. Coertjens, T. Van Daal, and P. Van Petegem, 'The directional links between students' academic motivation and social integration during the first year of higher education', Eur J Psychol Educ , vol. 34, no. 1, pp. 67-86, Jan. 2019, doi: 10.1007/s10212-017-0365-6. - source

  • Probably not a substantial claim: may not pass S2 (checkable).

    Study design and conductnone given
    § 3.2 data cleaning and harmonization · p. 2

    The data cleaning and matching process is essential to ensure consistent and reliable data for subsequent bibliometric analysis.

    “The data cleaning and matching process is essential to ensure consistent and reliable data for subsequent bibliometric analysis.”
    Quote in the paperNot a substantial claim
30 of 30

  • The paper relies on this without support.

    New teaching models based on SDT such as SOP are in the blank stage.

    “those new teaching models based on SDT such as SOP are in the blank stage.”

  • The paper relies on this without support.

    Research on the breadth and depth of co-occurrence analysis literature metrology methods of SDT research has not been fully carried out.

    “the research on the breadth and depth of the co-occurrence analysis literature metrology research methods of SDT research has not been fully carried out.”

  • The paper relies on this without support.

    Existing studies have not conducted author-based keyword co-occurrence analysis to study cluster analysis of SDT research results.

    “Existing studies have not conducted author-based keyword co-occurrence analysis to study cluster analysis of SDT research results.”

  • The paper relies on this without support.

    The search strategy captures comprehensive relevant SDT literature in higher education.

    “The search used all fields covered by the database, including title, abstract, and keywords, in order to integrate the comprehensive relevant SDT literature in the context of HE.”
4 of 4

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.

  • Cluster 6 is called the fourth group

    Minor

    The discussion under the Cluster 6 heading calls it the fourth group.

  • Cluster 4 is called the second cluster

    Minor

    The discussion under the Cluster 4 heading calls it the second cluster.

  • Keywords occurring exactly ten times conflict with the stated cutoff

    Minor

    The method says only keywords appearing more than ten times are shown, while Table 1 includes keywords with exactly ten occurrences.

  • Six clusters are divided into two plus five

    Minor

    The abstract says there are six theme clusters, but its breakdown of two clusters and the other five totals seven.

4 of 4

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. D. Wood, ‘The impact of students’ perceived relatedness and competence upon their motivated engagement with learning activities: a self-determination theory perspective’, Jul. 2016. Accessed: Feb. 16, 2024. [Online]. Available: https://www.semanticscholar.org/paper/The-impact-of-students'-perceived-relatedness-and-%3A-Wood/1a8bbb98694f005ce620a915e4fe4d456e0b65b8
  2. 10.29252/LRR.14.3.10 · DOI

Cited by

Still being checked.

Related

Authors
Lin, C., Zhang, Y., Wang, L., & Wu, J.
Affiliations
  • Officers College of PAP, Chengdu, Sichuan, China
  • Chengdu Polytechnic (CN)
ORCID
Venue
Proceedings of the 2024 2nd International Conference on Information Education and Artificial Intelligence
Publisher
ACM
Published
2024-12-20
Pages
436-441
DOI
10.1145/3724504.3724576
OpenAlex
W4410210390
Semantic Scholar
d6c12c5418a6d02d68b0cb30f930854605dcc8ec
Cited by
0
References
30
FWCI
0.00
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
International Conference on Information Education and Artificial Intelligence (ICIEAI) (publishedVersion)
Topics
  • Social Sciences › Psychology › Social Psychology › Motivation and Self-Concept in Sports
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Education, Achievement, and Giftedness
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
Keywords
self-determination theory, higher education, student motivation, intrinsic motivation, academic performance, digital learning platforms, online learning, COVID-19 pandemic, research trends, teaching model

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 2026-10-25.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-25.

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.

What counts as a claim ›How we measure confidence ›How we check this ›

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