Pith. sign in

REVIEW 3 major objections 5 minor 77 references

Beyond Self-Regulated Learning Processes: Unveiling Hidden Tactics in Generative AI-Assisted Writing

T0 review · 3 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper argues that unobservable 'hidden tactics'—latent states in a Hidden Markov Model—sit between SRL processes and strategies in GenAI-assisted writing, and that grouping students by these tactics yields significant essay-score differ

desk verdict A transparent, carefully described HMM pipeline for SRL tactics, but the performance claim is thinner than the abstract suggests: one of two pairwise differences survives correction, and the cluster-then-test design could inflate it. read the letter →

arxiv 2508.10310 v1 pith:IGJ3EEP7 submitted 2025-08-14 cs.HC cs.CY

Kaixun Yang , Yizhou Fan , Luzhen Tang , Mladen Raković , Xinyu Li , Dragan Gašević , Guanliang Chen This is my paper · ORCID
classification cs.HCcs.CY
keywords self-regulatedlearninghiddentacticsMarkovModelanalyticsgenerativeAIacademicwritingstrategiestracedata
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to show that self-regulated learning in GenAI-assisted academic writing cannot be adequately captured by clustering observable learning processes directly. It proposes an intermediate layer of 'hidden tactics'—short, purposeful action states inferred as latent variables—and models them with a Hidden Markov Model. From trace data of 139 students writing with ChatGPT help, the model finds nine hidden tactics and three strategy groups (conventional strategic writers, GenAI-integrated writers, and intensive material reviewers). The GenAI-integrated group scored significantly higher on essays than the other two groups, whereas a benchmark that clusters the raw SRL process sequences found no significant group differences. A sympathetic reader would take this as evidence that the hidden-tactic layer carries information about learning strategies that observable processes obscure.

What carries the argument

The central object is the Hidden Markov Model's latent state, reinterpreted as a 'hidden tactic.' The model's emission matrix assigns each hidden tactic a probability distribution over the seven SRL process codes plus a CHATGPT code, so a single tactic can represent intertwined processes—the mechanism that lets the model capture non-linear, overlapping regulation. The transition matrix captures how students move between tactics over time. The tactic sequences are then clustered via Levenshtein distance, radial basis transformation, and k-means, with the number of tactics and clusters chosen by information criteria and elbow/silhouette methods.

What would settle it

Run the same analysis on a new GenAI-writing cohort with think-aloud protocols; if students' stated intentions do not match the tactic labels assigned by the HMM's latent states—for example, a student labeled as using the 'exclusive GenAI interaction' tactic reports careful reading—then the hidden-tactic interpretation is unsupported.

Watch

Extended reading notes

Core claim

The paper's central claim is that between observable self-regulated learning (SRL) processes—the coded click and keystroke events—and broader SRL strategies there is an unobserved layer called hidden tactics, and that modeling this layer is necessary for detecting meaningful strategy differences. Using a Hidden Markov Model on SRL process sequences from 139 higher-education students doing a GenAI-assisted writing task, the study reports nine hidden tactics, each characterized by a probability distribution over SRL processes; some are pure (e.g., initial reading, focused writing, exclusive ChatGPT interaction) while others mix processes (e.g., writing while consulting requirements). Clusterin

Load-bearing premise

The load-bearing premise is that the HMM's nine latent states correspond to genuine, purposeful tactics in students' minds, even though the study never validates that mapping against students' own accounts or other external criteria.

Editorial extensions

If this is right

  • Learning analytics can identify strategy groups in GenAI-assisted writing without assuming SRL processes unfold as clean, non-overlapping linear sequences.
  • Students who integrate ChatGPT throughout writing tended to produce higher-scoring essays, while students who spent long phases re-reading materials scored lowest in this dataset.
  • A benchmark method that clusters observable SRL processes directly found no significant performance differences, suggesting the hidden-tactic layer adds discriminatory power.
  • Adaptive writing tools could in principle monitor hidden-tactic sequences in real time to flag patterns such as persistent re-reading or heavy GenAI use without deep cognitive engagement.
  • The finding that the highest-scoring group used GenAI heavily but engaged less in complex metacognitive activity supports the concern that high performance during GenAI-assisted tasks may not equal deeper learning.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The two-layer HMM-plus-clustering recipe is portable: any task with noisy, overlapping trace labels—programming, inquiry learning, collaborative problem solving—could gain from an intermediate latent-tactic layer before strategy clustering.
  • A stronger test of the hidden-tactic construct would be whether the clusters predict performance on a later, unaided transfer task; the paper only measures essay quality within the GenAI-assisted session.
  • Because the hidden tactics are inferred statistically, the names assigned to them (e.g., 'writing while engaging with GenAI') are interpretations; think-aloud or screen-review data would be needed to confirm that students actually experience these as distinct purposeful tactics.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes a layered model of self-regulated learning (SRL) in which observable SRL processes are generated by hidden tactics—latent states modeled with Hidden Markov Models—that in turn combine into SRL strategies via clustering. Using trace data from 139 higher-education students completing a GenAI-assisted academic writing task in the FLoRA platform, the authors identify nine hidden tactics, three strategy clusters (Conventional Strategic Writers, GenAI-Integrated Writers, Intensive Material Reviewers), and report statistically significant essay-score differences among clusters. A benchmark that clusters observed SRL processes directly without the hidden layer yields no significant performance differences, which the paper interprets as evidence that hidden tactics capture information beyond SRL processes. The paper concludes that HMM-derived hidden tactics better reflect the discontinuous, non-linear, and intertwined nature of SRL and support adaptive learning technologies.

Significance. If the empirical claim is robust, this is a valuable methodological contribution: it introduces a new analytical construct ('hidden tactics') and operationalizes it with a principled sequence model, potentially improving how learning analytics infer SRL strategies from trace data. The paper also engages seriously with a benchmark and openly lists limitations, including the absence of think-aloud data. However, the central validation—performance differences across HMM-derived clusters, and the claimed advantage over the benchmark—rests on statistical comparisons that are not yet adequately controlled, so the significance of the finding is currently conditional on those analyses being redone.

major comments (3)
  1. [Section 4.3, Table 2] The pairwise Mann-Whitney tests are performed on clusters derived from the same data through an unsupervised pipeline (HMM fitting, Viterbi decoding, Levenshtein/RBF/k-means) with the number of clusters selected by elbow on the same data. This cluster-then-test procedure can inflate apparent group differences even when the outcome is independent of cluster labels, particularly with a small cluster such as Cluster 2 (n=16). No omnibus test (e.g., Kruskal-Wallis) is reported, and no multiple-comparison correction is applied; the Cluster 1 vs. Cluster 2 comparison (p=0.039) would not survive a Bonferroni correction for three tests. The paper should report a permutation test that re-assigns cluster labels or re-runs the clustering on permuted outcomes, and should treat p=0.039 as non-significant after correction unless an omnibus test supports an overall group effect.
  2. [Section 3.2.6, Tables 3-4 and Figures 10-11] The benchmark comparison is confounded by different cluster counts: the proposed method selects three clusters (Figure 5) while the benchmark selects four (Figure 8), so the benchmark's null result could reflect the different cluster solution rather than the absence of hidden tactics. The 7-versus-12 student case analysis is descriptive, was selected post hoc after inspecting the clustering output, and lacks any statistical test of whether the hidden-tactic distributions differ significantly from the SRL-process distributions. The claim that hidden tactics 'captured more information' than SRL processes is therefore not yet supported. A formal comparison is needed—for example, testing whether cluster membership from each method predicts essay score while controlling for cluster count, or using a permutation test on the 7/12 split.
  3. [Section 4.1 and Section 5.2] The nine hidden tactics are interpreted as purposeful psychological states (e.g., 'a tactic students used to write while engaging with GenAI') based solely on the HMM emission matrices. The paper explicitly acknowledges in Section 5.2 that no think-aloud or other criterion-related validation data were collected. Without such evidence, the hidden states could be purely statistical latent states of the HMM, and the psychological reality of the proposed 'hidden tactics' construct is not established. This limits the conceptual contribution and weakens the claim that the method models genuine SRL processes. At minimum, the interpretations should be reframed as exploratory and the paper should discuss concrete ways to establish criterion validity (e.g., think-aloud protocols, comparison with self-report measures) before claiming that hidden tactics are a valid analytical layer.
minor comments (5)
  1. [Throughout] Several typographical errors: 'T actic' appears in section headings and figure labels instead of 'Tactic'; Section 5.1 contains 'stduents' for 'students'.
  2. [Section 3.2.3] The text says 'A HMM'—should be 'An HMM' because HMM is pronounced 'aitch-em-em'.
  3. [References] The Winne and Hadwin reference is listed as 'Winne, P. H. and Hadwin, A. F. () Studying as self-regulated learning' with a missing year and incomplete bibliographic details; it should be completed.
  4. [Section 3.2.2] The outlier removal step is described as removing sequences 'too long or too short' based on z-scores within three standard deviations, but it is not stated whether the z-score is computed across all students' sequence lengths and how the threshold is applied to both tails. Please clarify.
  5. [Figures 6 and 7] The hidden-tactic distribution and proportion plots are dense and the labels are small; adding a legend and larger font would improve readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: hidden tactics are fitted without outcome information, and the performance association is an independent post-hoc comparison.

full rationale

The paper's derivation chain is not circular. HMM parameters, state-number selection, Viterbi decoding, and clustering are all performed on SRL process sequences only; essay scores are not used in any fitting step (Sections 3.2.3, 3.2.4, 4.1, 4.2). The RQ3 association between clusters and essay scores is therefore an independent post-hoc statistical test, not a fitted-input-called-prediction. The benchmark method is implemented in the paper rather than imported by self-citation, and its null result is an observed outcome, not an assumption. The '7 vs. 12' case analysis is descriptive and selected post hoc, but it compares two different representations of the same process data; this may be statistically fragile, but it is not circular because the hidden-tactic representation is not equivalent to the benchmark representation by construction. The paper explicitly acknowledges the absence of think-aloud data for criterion-related validity (Section 5.2), which limits construct interpretation but does not make the analysis self-referential. No equation is defined in terms of the target conclusion, and no fitted parameter is renamed as a prediction. Statistical concerns such as cluster-then-test inflation and lack of multiple-comparison correction are correctness risks, not circularity.

Assumptions & free parameters 5 free parameters · 6 assumptions · 1 invented entities

The central claim depends on the HMM latent states being meaningful tactics, and these are fitted parameters plus a post-hoc interpretation. The four cited methodological constants (mapping libraries, model selection criteria, clustering choices, grading) are assumptions imported from prior work or chosen heuristically.

free parameters (5)
  • Number of HMM hidden states = 9
    Selected via AIC/BIC/LL on the study data (Figure 3); the resulting hidden states are the central construct, so this choice drives all downstream results.
  • Number of strategy clusters = 3
    Selected via elbow method and silhouette score (Figure 5), K-means clustering of hidden tactic sequences.
  • Z-score outlier threshold = 3
    Used to remove 26 of 165 students based on process sequence length (Section 3.2.2); affects sample composition.
  • HMM parameters (transition, emission, initial state probabilities) = estimated via maximum likelihood, not reported numerically
    These define the hidden tactics; they are fit to the same data that is later analyzed, so the tactic interpretations inherit any estimation error.
  • RBF kernel width and k-means hyperparameters = not reported
    The similarity embedding step in RQ2 requires a width parameter for the radial basis function; it is unspecified, limiting exact replication.
assumptions (6)
  • domain assumption Trace events can be validly mapped to learning actions and SRL processes via the Fan et al. (2022) action and process libraries.
    Invoked in Section 3.2.1; if the mapping is invalid, all process sequences, hidden tactics, and strategies inherit the error.
  • domain assumption The Bannert (2007) tripartite SRL model and the seven defined SRL processes correctly operationalize self-regulated learning in this writing task.
    Section 2.1 and Table 1; relies on theoretical validity not tested in this study.
  • ad hoc to paper Hidden states of the fitted HMM correspond to psychologically meaningful, short, purposeful 'hidden tactics' rather than merely statistical latent states.
    Core interpretive assumption throughout Section 4.1; authors acknowledge in Section 5.2 that no think-aloud data were collected to support criterion-related validity.
  • standard math First-order Markov assumption for hidden tactic sequences is appropriate for modeling SRL processes.
    Inherent to the HMM methodology used in Section 3.2.3.
  • domain assumption Clustering of Levenshtein-distance-embedded hidden tactic sequences via k-means yields meaningful strategy groups.
    Methodological choice in Section 3.2.4; cluster stability across resampling is not reported.
  • domain assumption Essay scores are a valid measure of task performance.
    Section 3.1; based on grading criteria and ICC > 0.85, but no independent validation of the rubric itself is provided.
invented entities (1)
  • Hidden tactics as an intermediate analytical layer between SRL processes and SRL strategies
    purpose: To capture the discontinuous, non-linear, intertwined nature of SRL and bridge the two levels
    The latent states are defined entirely by the HMM fit on this single dataset; no falsifiable prediction outside the study is provided. Interpretive naming relies on emission probability inspection, not external validation. The authors state in Section 5.2 that think-aloud data for criterion-related validity are absent.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Beyond Self-Regulated Learning Processes: Unveiling Hidden Tactics in Generative AI-Assisted Writing." pith.science (2026). https://pith.science/paper/IGJ3EEP7

@misc{pith2026250810310,
  author       = {Pith},
  title        = {Pith review of: Beyond Self-Regulated Learning Processes: Unveiling Hidden Tactics in Generative AI-Assisted Writing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IGJ3EEP7}},
  note         = {Machine review of arXiv:2508.10310}
}
read the original abstract

The integration of Generative AI (GenAI) into education is reshaping how students learn, making self-regulated learning (SRL) - the ability to plan, monitor, and adapt one's learning - more important than ever. To support learners in these new contexts, it is essential to understand how SRL unfolds during interaction with GenAI tools. Learning analytics offers powerful techniques for analyzing digital trace data to infer SRL behaviors. However, existing approaches often assume SRL processes are linear, segmented, and non-overlapping-assumptions that overlook the dynamic, recursive, and non-linear nature of real-world learning. We address this by conceptualizing SRL as a layered system: observable learning patterns reflect hidden tactics (short, purposeful action states), which combine into broader SRL strategies. Using Hidden Markov Models (HMMs), we analyzed trace data from higher education students engaged in GenAI-assisted academic writing. We identified three distinct groups of learners, each characterized by different SRL strategies. These groups showed significant differences in performance, indicating that students' use of different SRL strategies in GenAI-assisted writing led to varying task outcomes. Our findings advance the methodological toolkit for modeling SRL and inform the design of adaptive learning technologies that more effectively support learners in GenAI-enhanced educational environments.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

77 extracted references · 66 canonical work pages

  1. [1]

    Akata, Z., Balliet, D., De Rijke, M., Dignum, F., Dignum, V., Eiben, G., Fokkens, A., Grossi, D., Hindriks, K., Hoos, H. et al. (2020) A research agenda for hybrid intelligence: augmenting human intellect with collaborative, adaptive, responsible, and explainable artificial intelligence. Computer, 53, 18--28

    work page 2020

  2. [2]

    R., Rakovi \'c , M., Yang, K., Fan, Y., Ga s evi \'c , D

    Alghamdi, S. R., Rakovi \'c , M., Yang, K., Fan, Y., Ga s evi \'c , D. and Chen, G. (2025) Analytics of temporal patterns of self-regulated learners: A time series approach. In Proceedings of the 15th International Learning Analytics and Knowledge Conference, 283--292

    work page 2025

  3. [3]

    (2015) Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues

    Azevedo, R. (2015) Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues. Educational Psychologist, 50, 84--94

    work page 2015

  4. [4]

    Azevedo, R., Aleven, V. et al. (2013) International handbook of metacognition and learning technologies, vol. 26. Springer

    work page 2013

  5. [5]

    (2007) Metakognition beim lernen mit hypermedien

    Bannert, M. (2007) Metakognition beim lernen mit hypermedien. Waxmann Verlag

    work page 2007

  6. [6]

    and Sonnenberg, C

    Bannert, M., Reimann, P. and Sonnenberg, C. (2014) Process mining techniques for analysing patterns and strategies in students’ self-regulated learning. Metacognition and learning, 9, 161--185

    work page 2014

  7. [7]

    Bartko, J. J. (1966) The intraclass correlation coefficient as a measure of reliability. Psychological reports, 19, 3--11

    work page 1966

  8. [8]

    and Kumar, A

    Bholowalia, P. and Kumar, A. (2014) Ebk-means: A clustering technique based on elbow method and k-means in wsn. International Journal of Computer Applications, 105

    work page 2014

Show all 77 references
  1. [9]

    Biswas, G., Baker, R. S. and Paquette, L. (2017) Data mining methods for assessing self-regulated learning. In Handbook of self-regulation of learning and performance, 388--403. Routledge

    2017

  2. [10]

    and Fan, Y

    Chen, A., Xiang, M., Zhou, J., Jia, J., Shang, J., Li, X., Ga s evi \'c , D. and Fan, Y. (2025) Unpacking help-seeking process through multimodal learning analytics: A comparative study of chatgpt vs human expert. Computers & Education, 226, 105198

    2025

  3. [11]

    and Swiecki, Z

    Cheng, Y., Guan, R., Li, T., Rakovi \'c , M., Li, X., Fan, Y., Jin, F., Tsai, Y.-S., Ga s evi \'c , D. and Swiecki, Z. (2025) Self-regulated learning processes in secondary education: A network analysis of trace-based measures. In Proceedings of the 15th International Learning...

    2025

  4. [12]

    and Swiecki, Z

    Cheng, Y., Lyons, K., Chen, G., Ga s evic, D. and Swiecki, Z. (2024) Evidence-centered assessment for writing with generative AI . In Proceedings of the 14th Learning Analytics and Knowledge Conference (LAK’24)

    2024

  5. [13]

    Colan, S. D. (2013) The why and how of z scores. Journal of the American Society of Echocardiography, 26, 38--40

    2013

  6. [14]

    Dent, A. L. and Koenka, A. C. (2016) The relation between self-regulated learning and academic achievement across childhood and adolescence: A meta-analysis. Educational psychology review, 28, 425--474

    2016

  7. [15]

    A., Sonnenfeld, N

    Dever, D. A., Sonnenfeld, N. A., Wiedbusch, M. D., Schmorrow, S. G., Amon, M. J. and Azevedo, R. (2023) A complex systems approach to analyzing pedagogical agents’ scaffolding of self-regulated learning within an intelligent tutoring system. Metacognition and Learning, 18, 659--691

    2023

  8. [16]

    and Merhav, N

    Ephraim, Y. and Merhav, N. (2002) Hidden markov processes. IEEE Transactions on information theory, 48, 1518--1569

    2002

  9. [17]

    and Ga s evi \'c , D

    Fan, Y., van der Graaf, J., Lim, L., Rakovi \'c , M., Singh, S., Kilgour, J., Moore, J., Molenaar, I., Bannert, M. and Ga s evi \'c , D. (2022) Towards investigating the validity of measurement of self-regulated learning based on trace data. Metacognition and Learning, 17, 949--987

    2022

  10. [18]

    and Ga s evi \'c , D

    Fan, Y., Saint, J., Singh, S., Jovanovic, J. and Ga s evi \'c , D. (2021) A learning analytic approach to unveiling self-regulatory processes in learning tactics. In LAK21: 11th international learning analytics and knowledge conference, 184--195

    2021

  11. [19]

    Fan, Y., Tan, Y., Rakovi \'c , M., Wang, Y., Cai, Z., Shaffer, D. W. and Ga s evi \'c , D. (2023) Dissecting learning tactics in mooc using ordered network analysis. Journal of Computer Assisted Learning, 39, 154--166

    2023

  12. [20]

    and Ga s evi \'c , D

    Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X. and Ga s evi \'c , D. (2025) Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, ...

    2025

  13. [21]

    and Pardo, A

    Fincham, E., Ga s evi \'c , D., Jovanovi \'c , J. and Pardo, A. (2018) From study tactics to learning strategies: An analytical method for extracting interpretable representations. IEEE Transactions on Learning Technologies, 12, 59--72

    2018

  14. [22]

    and Dawson, S

    Gasevic, D., Jovanovic, J., Pardo, A. and Dawson, S. (2017) Detecting learning strategies with analytics: Links with self-reported measures and academic performance. Journal of Learning Analytics, 4, 113--128

    2017

  15. [23]

    J., Dunlosky, J

    Hacker, D. J., Dunlosky, J. and Graesser, A. C. (1998) Metacognition in educational theory and practice. Routledge

    1998

  16. [24]

    and Naaman, M

    Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L. and Naaman, M. (2023) Co-writing with opinionated language models affects users’ views. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems , 1--15

    2023

  17. [25]

    a rvel \

    J \"a rvel \"a , S., J \"a rvenoja, H. and Malmberg, J. (2019) Capturing the dynamic and cyclical nature of regulation: Methodological progress in understanding socially shared regulation in learning. International journal of computer-supported collaborative learning, 14, 425--441

    2019

  18. [26]

    and Mirriahi, N

    Jovanovi \'c , J., Ga s evi \'c , D., Dawson, S., Pardo, A. and Mirriahi, N. (2017) Learning analytics to unveil learning strategies in a flipped classroom. The Internet and Higher Education, 33, 74--85

    2017

  19. [27]

    and Yang, Q

    Lee, M., Liang, P. and Yang, Q. (2022) Coauthor: Designing a human-ai collaborative writing dataset for exploring language model capabilities. In Proceedings of the 2022 CHI conference on human factors in computing systems, 1--19

    2022

  20. [28]

    and Xie, C

    Li, S., Du, H., Xing, W., Zheng, J., Chen, G. and Xie, C. (2020) Examining temporal dynamics of self-regulated learning behaviors in stem learning: A network approach. Computers & Education, 158, 103987

    2020

  21. [29]

    and Ga s evi \'c , D

    Li, T., Fan, Y., Srivastava, N., Zeng, Z., Li, X., Khosravi, H., Tsai, Y.-S., Swiecki, Z. and Ga s evi \'c , D. (2024 a ) Analytics of planning behaviours in self-regulated learning: Links with strategy use and prior knowledge. In Proceedings of the 14th learning analytics and...

    2024

  22. [30]

    Li, X., Fan, Y., Li, T., Rakovic, M., Singh, S., van der Graaf, J., Lim, L., Moore, J., Molenaar, I., Bannert, M. et al. (2024 b ) The flora engine: Using analytics to measure and facilitate learners' own regulation activities. arXiv preprint arXiv:2412.09763

    2024 arXiv

  23. [31]

    and Ga s evi \'c , D

    Lim, L., Bannert, M., van der Graaf, J., Fan, Y., Rakovic, M., Singh, S., Molenaar, I. and Ga s evi \'c , D. (2024) How do students learn with real-time personalized scaffolds? British Journal of Educational Technology, 55, 1309--1327

    2024

  24. [32]

    Liu, M., Zhang, L. J. and Biebricher, C. (2024) Investigating students’ cognitive processes in generative ai-assisted digital multimodal composing and traditional writing. Computers & Education, 211, 104977

    2024

  25. [33]

    F., Morales, N

    Maldonado-Mahauad, J., P \'e rez-Sanagust \' n, M., Kizilcec, R. F., Morales, N. and Munoz-Gama, J. (2018) Mining theory-based patterns from big data: Identifying self-regulated learning strategies in massive open online courses. Computers in Human Behavior, 80, 179--196

    2018

  26. [34]

    A., Jovanovi \'c , J

    Matcha, W., Ga s evi \'c , D., Uzir, N. A., Jovanovi \'c , J. and Pardo, A. (2019) Analytics of learning strategies: Associations with academic performance and feedback. In Proceedings of the 9th international conference on learning analytics & knowledge, 461--470

    2019

  27. [35]

    (2022) The concept of hybrid human-ai regulation: Exemplifying how to support young learners’ self-regulated learning

    Molenaar, I. (2022) The concept of hybrid human-ai regulation: Exemplifying how to support young learners’ self-regulated learning. Computers and Education: Artificial Intelligence, 3, 100070

    2022

  28. [36]

    a rvel \

    Molenaar, I., de Mooij, S., Azevedo, R., Bannert, M., J \"a rvel \"a , S. and Ga s evi \'c , D. (2023) Measuring self-regulated learning and the role of ai: Five years of research using multimodal multichannel data. Computers in Human Behavior, 139, 107540

    2023

  29. [37]

    U., Ayub, N., Rasool, A., Abbas, T

    Naseer, F., Khalid, M. U., Ayub, N., Rasool, A., Abbas, T. and Afzal, M. W. (2024) Automated assessment and feedback in higher education using generative ai. In Transforming Education With Generative AI: Prompt Engineering and Synthetic Content Creation, 433--461. IGI Global S...

    2024

  30. [38]

    and Huang, X

    Nguyen, A., Hong, Y., Dang, B. and Huang, X. (2024 a ) Human-ai collaboration patterns in ai-assisted academic writing. Studies in Higher Education, 49, 847--864

    2024

  31. [39]

    a ms \"a , J., Dwiarie, A. and J \

    Nguyen, A., L \"a ms \"a , J., Dwiarie, A. and J \"a rvel \"a , S. (2024 b ) Lifelong learner needs for human-centered self-regulated learning analytics. Information and Learning Sciences, 125, 68--108

    2024

  32. [40]

    Rakovi \'c , M., Bannert, M., Molenaar, I., Winne, P. H. and Ga s evi \'c , D. (2024) In conversation: Bannert, molenaar, & winne--multiple perspectives on researching and supporting self-regulated learning via analytics. In Theory Informing and Arising from Learning Analytics...

    2024

  33. [41]

    Roll, I., Aleven, V., McLaren, B. M. and Koedinger, K. R. (2011) Improving students’ help-seeking skills using metacognitive feedback in an intelligent tutoring system. Learning and instruction, 21, 267--280

    2011

  34. [42]

    and Hirschberg, J

    Rosenberg, A. and Hirschberg, J. (2007) V-measure: A conditional entropy-based external cluster evaluation measure. In Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL), 410--420

    2007

  35. [43]

    Saint, J., Ga s evi \'c , D., Matcha, W., Uzir, N. A. and Pardo, A. (2020) Combining analytic methods to unlock sequential and temporal patterns of self-regulated learning. In Proceedings of the tenth international conference on learning analytics & knowledge, 402--411

    2020

  36. [44]

    and Ga s evi \'c , D

    Saqr, M., L \'o pez-Pernas, S., Jovanovi \'c , J. and Ga s evi \'c , D. (2023) Intense, turbulent, or wallowing in the mire: A longitudinal study of cross-course online tactics, strategies, and trajectories. The Internet and Higher Education, 57, 100902

    2023

  37. [45]

    Schunk, D. H. and Zimmerman, B. (2011) Handbook of self-regulation of learning and performance. Taylor & Francis

    2011

  38. [46]

    and Knight, S

    Shibani, A., Rajalakshmi, R., Mattins, F., Selvaraj, S. and Knight, S. (2023) Visual representation of co-authorship with GPT-3 : Studying human-machine interaction for effective writing. In Proceedings of the 16th International Conference on Educational Data Mining , 183--193

    2023

  39. [47]

    Srivastava, N., Fan, Y., Rakovic, M., Singh, S., Jovanovic, J., Van Der Graaf, J., Lim, L., Surendrannair, S., Kilgour, J., Molenaar, I. et al. (2022) Effects of internal and external conditions on strategies of self-regulated learning: A learning analytics study. In LAK22: 12...

    2022

  40. [48]

    Tabari, M. A. (2017) Investigating the effects of planning time on the complexity of l2 argumentative writing. Tesl-Ej, 21, n1

    2017

  41. [49]

    and Pottie, G

    Tadayon, M. and Pottie, G. J. (2020) Predicting student performance in an educational game using a hidden markov model. IEEE Transactions on Education, 63, 299--304

    2020

  42. [50]

    and Buchanan, M

    Taranto, D. and Buchanan, M. T. (2020) Sustaining lifelong learning: A self-regulated learning (srl) approach. Discourse and Communication for Sustainable Education, 11, 5--15

    2020

  43. [51]

    P., Steiss, J., Bailey, D., Graham, S., Moon, Y., Ritchie, D., Tseng, W

    Tate, T. P., Steiss, J., Bailey, D., Graham, S., Moon, Y., Ritchie, D., Tseng, W. and Warschauer, M. (2024) Can ai provide useful holistic essay scoring? Computers and Education: Artificial Intelligence, 7, 100255

    2024

  44. [52]

    and Kuczera, G

    Thyer, M. and Kuczera, G. (2003) A hidden markov model for modelling long-term persistence in multi-site rainfall time series. 2. real data analysis. Journal of Hydrology, 275, 27--48

    2003

  45. [53]

    and Broisin, J

    Villalobos, E., P \'e rez-Sanagust \' n, M., Azevedo, R., Sanza, C. and Broisin, J. (2024) Exploring manifestations of learners’ self-regulated tactics and strategies across blended learning courses. IEEE Transactions on Learning Technologies

    2024

  46. [54]

    and Ga s evi \'c , D

    Weidlich, J. and Ga s evi \'c , D. (2025) Chatgpt in education: An effect in search of a cause

    2025

  47. [55]

    and Hadwin, A

    Winne, P. and Hadwin, A. (1998) Studying as Self-Regulated Learning, vol. 93, 277--304

    1998

  48. [56]

    Winne, P. H. (2017) Learning analytics for self-regulated learning. Handbook of learning analytics, 754, 241--249

    2017

  49. [57]

    British Journal of Educational Psychology, 88, 9--20

    --- (2018) Theorizing and researching levels of processing in self-regulated learning. British Journal of Educational Psychology, 88, 9--20

    2018

  50. [58]

    Computers in Human Behavior, 112, 106457

    --- (2020) Construct and consequential validity for learning analytics based on trace data. Computers in Human Behavior, 112, 106457

    2020

  51. [59]

    Metacognition and Learning, 17, 773--791

    --- (2022) Modeling self-regulated learning as learners doing learning science: How trace data and learning analytics help develop skills for self-regulated learning. Metacognition and Learning, 17, 773--791

    2022

  52. [60]

    Winne, P. H. and Hadwin, A. F. () Studying as self-regulated learning. In Metacognition in educational theory and practice (eds. D. J. Hacker, J. Dunlosky and A. C. Graesser), The educational psychology series., 277--304. Lawrence Erlbaum Associates Publishers

  53. [61]

    and Ga s evi \'c , D

    Yan, L., Greiff, S., Teuber, Z. and Ga s evi \'c , D. (2024) Promises and challenges of generative artificial intelligence for human learning. Nature Human Behaviour, 8, 1839--1850

    2024

  54. [62]

    and Chen, G

    Yang, K., Cheng, Y., Zhao, L., Rakovi \'c , M., Swiecki, Z., Ga s evi \'c , D. and Chen, G. (2024) Ink and algorithm: Exploring temporal dynamics in human-ai collaborative writing. arXiv preprint arXiv:2406.14885

    2024 arXiv

  55. [63]

    and Chen, G

    Yang, K., Rakovi \'c , M., Liang, Z., Yan, L., Zeng, Z., Fan, Y., Ga s evi \'c , D. and Chen, G. (2025) Modifying ai, enhancing essays: How active engagement with generative ai boosts writing quality. In Proceedings of the 15th International Learning Analytics and Knowledge Co...

    2025

  56. [64]

    Cloude, E., Li, X., Ga s evi \'c , D

    Zhao, L., Rakovi \'c , M., B. Cloude, E., Li, X., Ga s evi \'c , D. and Bardach, L. (2025) The effect of sequential transition of self-regulated learning processes on performance: Insights from ordered network analysis. In Proceedings of the 15th International Learning Analyti...

    2025

  57. [65]

    Zimmerman, B. J. (2000) Attaining self-regulation: A social cognitive perspective. In Handbook of self-regulation, 13--39. Elsevier

    2000

  58. [66]

    Self-regulated learning and academic achievement, 1--36

    --- (2013) Theories of self-regulated learning and academic achievement: An overview and analysis. Self-regulated learning and academic achievement, 1--36

    2013

  59. [67]

    , " * write output.state after.block = add.period write newline

    ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type url volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.senten...

  60. [68]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

  61. [69]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

  62. [70]

    write newline

    " write newline "" before.all 'output.state := FUNCTION output.internal 'delimiter := duplicate empty 'pop 's := output.state mid.sentence = delimiter * write output.state before.all = 'write add.period " " * write if mid.sentence 'output.state := if s if FUNCTION output.check...

  63. [71]

    write newline

    " write newline "" before.all 'output.state := FUNCTION blank.sep after.quote 'output.state := FUNCTION fin.entry doi empty output.state after.quoted.block = 'skip 'add.period if if write newline FUNCTION new.block output.state before.all = 'skip output.state after.quote = aft...

  64. [72]

    , " * write output.state after.block = add.period write newline

    ENTRY address author booktitle chapter edition editor howpublished institution journal doi key month note number organization pages publisher school series title type url volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.se...

  65. [73]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

  66. [74]

    write newline

    " write newline " cite write " FUNCTION editor.postfix editor num.names #1 > "( )" "( )" if FUNCTION editor.trans.postfix editor num.names #1 > "( )" "( )" if FUNCTION trans.postfix translator num.names #1 > "( )" "( )" if FUNCTION authors.editors.reflist.apa5 'field := 'dot :...

  67. [75]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

  68. [76]

    Available from:

    ENTRY address assignee author booktitle chapter cartographer day edition editor howpublished institution inventor journal key month note number organization pages part publisher school series title type volume word year eprint doi url lastchecked updated label INTEGERS output....

  69. [77]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.