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REVIEW 3 major objections 5 minor 1 cited by

The FLoRA Engine: Using Analytics to Measure and Facilitate Learners' own Regulation Activities

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read FLoRA is an open-source engine that turns raw learning traces into real-time self-regulated-learning process labels and uses them to deliver personalised scaffolding.

desk verdict A useful, detailed system paper for the SRL community; the open-source FLoRA engine is a real artifact, but the paper itself doesn't report parser validity metrics and overclaims facilitation relative to its own case studies. read the letter →

arxiv 2412.09763 v1 pith:47774MQT submitted 2024-12-12 cs.HC cs.CY

Xinyu Li , Yizhou Fan , Tongguang Li , Mladen Rakovic , Shaveen Singh , Joep van der Graaf , Lyn Lim , Johanna Moore
show 3 more authors
Inge Molenaar Maria Bannert Dragan Gasevic
This is my paper · ORCID
classification cs.HCcs.CY
keywords LearningAnalyticsSelf-RegulatedScaffoldingToolsTracedataProcessminingparserPersonalized
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

FLoRA is an open-source engine that tries to close the gap between self-regulated learning (SRL) theory and real classroom data. The paper's central claim is that the engine can unobtrusively collect fine-grained, time-stamped trace data while learners read, annotate, search, plan, and write; automatically map those traces to SRL processes such as orientation, planning, monitoring, evaluation, and elaboration; and then deliver personalised scaffolds that adapt to each learner's enacted SRL in real time. If that claim holds, researchers gain a reusable measurement instrument that works inside ordinary learning management systems rather than only in laboratory settings, and learners receive help when their own behaviour shows they need it, instead of at fixed intervals. The paper is a design and implementation description, not a new empirical outcome study, so its evidence for validity and effects comes from earlier cited work. Its value would be as a shared, configurable platform that makes SRL processes visible and actionable across tasks, languages, and age groups.

What carries the argument

The trace parser is the load-bearing component. It is a rule-based pipeline that turns raw log events into named learning actions such as RELEVANT READING, WRITE ESSAY, EDIT ANNOTATION, and TIMER, then maps short action patterns to SRL process labels through a process library. For example, a timer check becomes MC.Monitoring, an edit to a note while reading the general instruction page becomes MC.Orientation, and note edits during essay writing become HC.Elaboration/Organisation; actions with no corresponding process are labelled NO PROCESS and discarded. This parser is what makes real-time measurement and scaffold triggering possible, and the paper's validation protocol - comparing parser output against think-aloud coding on two samples of 44 university students each - is what would establish that the labels correspond to what learners are actually doing.

What would settle it

Run the FLoRA trace parser on a new cohort while collecting concurrent think-aloud protocols, code those protocols with the same theory-based scheme, and compute the match rate, sensitivity, specificity, and trace coverage between parser labels and human-coded labels; if these fall materially below the levels achieved with the original 44-participant samples, the real-time SRL measurement and the scaffolds triggered from it are not valid in that context.

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Extended reading notes

Core claim

The paper's contribution is the design and implementation of FLoRA, a web-based, micro-service engine built from three modules. Instrumentation tools - a highlighter, note-taker, annotation search, timer, planner, writing tool, and scaffolding display - let learners work naturally while every keystroke, click, highlight, edit, and timer check is logged in temporal order. A trace parser with an action library of 17 actions and a process library based on an established SRL coding scheme converts these raw traces into SRL process labels in real time, distinguishing metacognitive processes (orientation, planning, monitoring, evaluation), low-cognition processes (first reading, re-reading), and high-cognition processes (elaboration and organisation). A scaffolding module uses the detected processes to decide whether and how to prompt: generalised scaffolds give the same message to everyone, while personalised scaffolds remove options whose associated SRL process has already been enacted, and can suppress the scaffold entirely if all its goals are met. The engine is open-source, configurable through files, and designed to plug into an existing LMS, so the same measurement and support pipeline can be reused across studies and tasks.

Load-bearing premise

The engine's usefulness collapses if the parser's action-to-process mapping is wrong for a new task or learner group, and the paper's validation protocol describes match rate, sensitivity, specificity, and trace coverage as quantitative indicators but does not report their values, so the mapping remains the load-bearing premise.

Editorial extensions

If this is right

  • A researcher can install FLoRA into an existing LMS and obtain a labelled SRL timeline for every learner, with process labels rather than raw clicks, without interrupting the task.
  • Scaffold content and trigger times become evidence-based parameters: the paper reports trigger times derived from how high-performing essay writers enacted orientation, reading, monitoring, and writing in a 45-minute task.
  • Because all tool and scaffold interactions are logged with timestamps, the same data can feed process-mining and other temporal analyses of SRL tactics and strategies.
  • The configuration files and open-source license let other groups reuse the pipeline for tasks of different lengths, languages, and content while keeping the measurement protocol constant, supporting cross-study comparison.
  • If the parser is valid, personalised scaffolding can work at scale: the system suppresses already-mastered suggestions and focuses prompts on SRL processes the learner has not yet enacted.

Reading between the lines

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

  • If the parser's labels generalise beyond the two think-aloud samples, FLoRA could become a common measurement standard for SRL, letting studies report SRL process frequencies and sequences in comparable units; the paper does not yet show this generalisation.
  • The paper reports that personalised scaffolds induced SRL activity but did not significantly change learning performance, which suggests the measurement module may be more decisive than the intervention module; a testable extension is to vary scaffold content while keeping timing constant to isolate the active ingredient.
  • The five-scaffold schedule was tuned to a 45-minute reading-writing task; a natural next step is to re-derive trigger times for other task types (video-based or collaborative) by repeating the same think-aloud-to-trace comparison, which the engine's configuration files make straightforward.
  • The planned multimodal additions (video and facial-expression capture, grammar and writing advisors, chatbots) would each need the same trace-parser validation before their labels can be trusted, because they extend the action library into constructs the current parser does not cover.
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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 presents FLoRA, an open-source engine for self-regulated learning (SRL) support, composed of three modules: instrumentation tools (annotation, search, timer, planner, writing, scaffolding display), a trace parser that maps raw log actions to SRL processes using a theory-driven and data-driven process library, and a scaffolding module that delivers personalised, adaptive scaffolds based on detected SRL processes. The authors describe the architecture, implementation, deployment infrastructure, and case studies in several countries, and compare FLoRA with nStudy, MetaTutor, and Betty's Brain. The central contribution claimed in Notes for Practice is the design and implementation of the engine, while the abstract and Section 5 make stronger claims about detecting, measuring, and facilitating SRL across a wide range of learning activities. The paper is a system description; the main validity evidence for the trace parser is cited to prior work rather than reported here.

Significance. If the validity of the trace parser were established, FLoRA would be a valuable open-source research platform: it integrates with standard LMSs, records fine-grained event streams unobtrusively, and supports configurable personalised scaffolding, with published empirical work already using it. The architecture is described in sufficient detail to be replicable, and the open-source release is a concrete community contribution. However, the paper's new contribution is limited: the parser validation is invoked through earlier publications, and no quantitative accuracy evidence appears in this manuscript. The significance therefore rests on the strength of prior work that the reader cannot verify here.

major comments (3)
  1. [Section 3.2, Module II – Trace parser] The validation paragraphs name 'match rate, sensitivity, specificity, and trace coverage' but never report their values, nor any confusion matrix or per-process agreement. Since the paper's central claim is that the engine can 'map trace data to corresponding SRL processes' (Notes for Practice), and the scaffolding module makes decisions based on this mapping, the absence of these metrics makes the measurement pipeline unassessable. The authors should report the values from the prior validation studies, or clearly state that the validation is fully deferred to Fan et al. (2022) and provide the relevant numbers in an appendix.
  2. [Section 3.3 vs Section 4.4] The scaffolding module is described as 'instrumental in augmenting students' learning performance' (Section 3.3), yet Section 4.4 reports that 'personalised scaffolds induced SRL activities, they had no significant effect on learning performance' (Lim et al., 2023). This is an internal inconsistency in the facilitation claim. The paper should either soften the causal claim about performance or report effect sizes and explicitly explain the discrepancy.
  3. [Section 4.4, Case Studies] The claim that FLoRA works 'in secondary schools and postgraduate courses in multiple countries' extends the parser and scaffolding beyond the populations and task types on which the parser was validated (university students, 45-minute essay tasks with three fixed topics). No data are provided for these new contexts, so the claim of generalisable measurement is unsupported. The authors should explicitly scope the validity claims to the validated contexts or include transfer evidence for the new populations and task variants.
minor comments (5)
  1. [Section 3.1.3] The phrase 'Base on the theoretical model' should be 'Based on the theoretical model'.
  2. [Section 3.2] The action names in the sentence 'such as RELE-V ANTREADING, WRITE ESSAY , and EDITANNOTATION' contain spacing artifacts; they should be typeset consistently with the action library in Appendix A.
  3. [Figure 10 and Section 3.3] The text refers to the process sequence 'ORIENTATION - MC.O.1' in the process library, but this code is not defined in Appendix A.
  4. [Title page] The DOI in the header is a placeholder (http://dx.doi.org/xxx-xxx-xxx); it should be completed or removed.
  5. [Section 3.1.4] The sentence 'Planner is also hardly used in our study' should be rephrased to 'The planner was also rarely used in our study'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: FLoRA is a system/design paper whose SRL action mapping is an explicit coding scheme and whose scaffold timings are fitted design constants, not predictions.

full rationale

FLoRA's contribution is architectural: instrumentation tools, a rule-based trace parser, and a scaffolding module. The trace parser's action-to-process mapping (e.g., timer check to MC.Monitoring) is an operational coding scheme defined in Appendix A and grounded in Bannert's published framework; it is not derived from or equivalent to the paper's conclusions. The parser's validity is deferred to prior think-aloud validation studies (Fan, van der Graaf, et al., 2022; Lim et al., 2021), which are external empirical evidence rather than in-sample fits, so the self-citations are not load-bearing circularity. Scaffold trigger timings (orientation at 2 min, reading at 7 min, etc.) are explicitly described as determined from how the good essay group learned in previous studies; they are design constants fitted to prior data, not predictions made and tested in this paper, so the 'fitted input called prediction' pattern does not apply. Section 4.4's admission that personalised scaffolds had no significant effect on learning performance is an internal inconsistency with Section 3's promotional framing, and Section 3.2's unnamed match rate/sensitivity/specificity values are an evidence-quality gap, but neither is a circular reduction. The paper therefore contains no step where a claimed result is equivalent by construction to its inputs.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The engine's central claim rests on a chain of domain assumptions: the Bannert ontology is correct; think-aloud verbalizations are a valid ground truth; observable tool actions carry the SRL meanings assigned to them; and validation from two university lab samples transfers to classroom and secondary-school contexts. The five scaffold trigger times are fitted to prior essay-performance data, not independently predicted. No new theoretical entities are introduced, so the invented-entities ledger is empty.

free parameters (3)
  • Scaffold trigger times = 2, 7, 16, 21, 35 minutes for a 45-minute task
    Section 3.3 states these timings were determined by comparing how the good essay group oriented, read, and wrote in prior think-aloud data, then implemented as fixed scaffold triggers. The values are configurable but were fitted to earlier performance data.
  • Off-task inactivity threshold = 5 minutes
    Section 3.2 defines an OFF TASK action after 5 minutes of no interaction, a value chosen based on prior studies and changed via the config file.
  • Number of scaffolds = 5
    Section 3.3 states five scaffolds were used because prior lab studies found that five distinct scaffolds maximized performance, and that more scaffolds could impede learning.
assumptions (4)
  • domain assumption Bannert's (2007) SRL coding framework is a valid ontology for mapping trace actions to metacognitive, low-cognition, and high-cognition processes.
    Section 3.2 and Appendix A construct the action and process libraries around this framework. If the ontology is wrong, all parser output is wrong.
  • domain assumption Think-aloud protocols provide a reliable ground truth for learners' SRL processes.
    Section 3.2 validates the trace parser by aligning coded think-aloud data with trace data. This assumes verbalizations reflect latent regulation processes.
  • domain assumption Observable tool actions such as highlighting, timer checks, writing, note edits, and searches correspond to the assigned SRL processes.
    Appendix A maps actions such as TIMER to MC.Monitoring and EDIT ANNOTATION on instruction pages to MC.Orientation. This mapping is the core inference of the engine and is contestable.
  • domain assumption Trace validation results from lab studies with university students generalize to the new tasks, institutions, and age groups listed in Section 4.4.
    The think-aloud validation used 44 German and 44 Dutch university students, while FLoRA is claimed for secondary classrooms in several countries and postgraduate courses in China.

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Cite this review

Pith. "Pith review of The FLoRA Engine: Using Analytics to Measure and Facilitate Learners' own Regulation Activities." pith.science (2026). https://pith.science/paper/47774MQT

@misc{pith2026241209763,
  author       = {Pith},
  title        = {Pith review of: The FLoRA Engine: Using Analytics to Measure and Facilitate Learners' own Regulation Activities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/47774MQT}},
  note         = {Machine review of arXiv:2412.09763}
}
read the original abstract

The focus of education is increasingly set on learners' ability to regulate their own learning within technology-enhanced learning environments (TELs). Prior research has shown that self-regulated learning (SRL) leads to better learning performance. However, many learners struggle to self-regulate their learning productively, as they typically need to navigate a myriad of cognitive, metacognitive, and motivational processes that SRL demands. To address these challenges, the FLoRA engine is developed to assist students, workers, and professionals in improving their SRL skills and becoming productive lifelong learners. FLoRA incorporates several learning tools that are grounded in SRL theory and enhanced with learning analytics (LA), aimed at improving learners' mastery of different SRL skills. The engine tracks learners' SRL behaviours during a learning task and provides automated scaffolding to help learners effectively regulate their learning. The main contributions of FLoRA include (1) creating instrumentation tools that unobtrusively collect intensively sampled, fine-grained, and temporally ordered trace data about learners' learning actions, (2) building a trace parser that uses LA and related analytical technique (e.g., process mining) to model and understand learners' SRL processes, and (3) providing a scaffolding module that presents analytics-based adaptive, personalised scaffolds based on students' learning progress. The architecture and implementation of the FLoRA engine are also discussed in this paper.

Figures

Figures reproduced from arXiv: 2412.09763 by the authors.

Figure 1
Figure 1. The user interface of the FLoRA integrated into Moodle LMS A high level architecture of the FLoRA engine can be found in [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. The architecture of the FLoRA engine 3.1 Module I – Instrumentation Tools As described in the background, there remains a significant gap in understanding the specific SRL processes from trace logs. This gap is particularly noteworthy given that instrumentation tools are specifically designed to capture these elusive SRL processes. The FLoRA engine exemplifies this by offering a suite of instrumentation tools design… view at source ↗
Figure 3
Figure 3. Annotation tool frequencies of the timer, search and planner usage was positively correlated, as they share the SRL processes they captured. But the search function is used least often compare with other tools. An explanation for the low usage of search tool is that the learning materials in our studies were limited to three topics, which might have reduced the need for goal-directed search behaviour (van der Graaf … view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Annotation search tool 3.1.3 Timer Tool As shown in [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Timer tool 3.1.4 Planner Tool Given that planning constitutes a principal facet of metacognition (Winne et al., 2019), the FLoRA engine incorporates a planner tool (as shown in [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Planner tool This tool is engineered to offer a drag-and-drop functionality, thereby significantly reducing the need for manual text or time entry for scheduling tasks. Users can effortlessly relocate predefined plan items to specific time slots, with the added functio…
Figure 7
Figure 7. Figure 7: Writing tool 3.1.6 Scaffolding Display Tool The scaffolding display tool, as depicted in [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Scaffolding display tool opening the writing tool. When students commence typing in the writing tool, all keystroke events are captured and mapped to the WRITE ESSAY action. Further information on the complete action definitions and the labelling procedure is provided …
Figure 9
Figure 9. Figure 9: Parsing trace data to SRL process The process library employs the SRL process definitions from Appendix A as a framework to translate various actions (as described in the paragraph above) into SRL events (as depicted in [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]
Figure 10
Figure 10. Figure 10: Data parsing example and one scaffold rule software, FLoRA aims to significantly contribute to the advancement of SRL research. The subsequent sections clarify several key aspects of FLoRA’s deployment and technological choices, specifically: 1) the FLoRA Engine, 2) t…
Figure 11
Figure 11. Figure 11: Log server interface 4.3 Infrastructure The FLoRA engine, now implemented in over ten universities worldwide and serving a significant student demographic, requires a robust, scalable, and reliable infrastructure (as displayed in [PITH_FULL_IMAGE:figures/full_fig_p01…
Figure 12
Figure 12. Figure 12: FLoRA engine infrastructure in-memory computing and SQL support but is complex to configure. Hazelcast provides strong distributed caching and rich data structures but requires significant memory and can be complex to set up. Redis stands out due to its support for di…
Figure 13
Figure 13. Figure 13: Process label library - Metacognition 27 [PITH_FULL_IMAGE:figures/full_fig_p027_13.png]
Figure 14
Figure 14. Figure 14: Process label library - Cognition 28 [PITH_FULL_IMAGE:figures/full_fig_p028_14.png]

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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

    cs.HC 2025-08 conditional novelty 5.0 of 10

    A hidden Markov model with nine latent tactics detects three GenAI-writing strategy groups whose essay scores differ, a distinction not found when clustering observed SRL processes directly.

Reference graph

Works this paper leans on

81 extracted references · 53 canonical work pages · cited by 1 Pith paper

  1. [1]

    , Ga s evi \'c , D

    ahmad2019discovering APACrefauthors Ahmad Uzir, N. , Ga s evi \'c , D. , Matcha, W. , Jovanovi \'c , J. , Pardo, A. , Lim, L A. \ Gentili, S. APACrefauthors \ 2019 . Discovering time management strategies in learning processes using process mining techniques Discovering time management strategies in learning processes using process mining techniques . Eur...

    doi:10.1007/9 2019

  2. [2]

    \ Paraskeva, F

    alexiou2015managing APACrefauthors Alexiou, A. \ Paraskeva, F. APACrefauthors \ 2015 . Managing time through a self-regulated oriented ePortfolio for undergraduate students Managing time through a self-regulated oriented eportfolio for undergraduate students . Design for Teaching and Learning in a Networked World: 10th European Conference on Technology En...

    2015

  3. [3]

    , Jivet, I

    alvarez2022tools APACrefauthors Alvarez, R P. , Jivet, I. , P \'e rez-Sanagustin, M. , Scheffel, M. \ Verbert, K. APACrefauthors \ 2022 . Tools designed to support self-regulated learning in online learning environments: A systematic review Tools designed to support self-regulated learning in online learning environments: A systematic review . IEEE Transa...

    arXiv 2022

  4. [4]

    , Bouchet, F

    azevedo2022lessons APACrefauthors Azevedo, R. , Bouchet, F. , Duffy, M. , Harley, J. , Taub, M. , Trevors, G. others APACrefauthors \ 2022 . Lessons learned and future directions of metatutor: leveraging multichannel data to scaffold self-regulated learning with an intelligent tutoring system Lessons learned and future directions of metatutor: leveraging ...

    arXiv 2022

  5. [5]

    , Bouchet, F

    azevedo2011metatutor APACrefauthors Azevedo, R. , Bouchet, F. , Harley, J M. , Feyzi-Behnagh, R. , Trevors, G. , Duffy, M. others APACrefauthors \ 2011 . MetaTutor: An Intelligent Multi-Agent Tutoring System Designed to Detect, Track, Model Foster Self-Regulated Learning Metatutor: An intelligent multi-agent tutoring system designed to detect, track, mode...

    work page arXiv 2011

  6. [6]

    \ Cromley, J G

    azevedo2004does APACrefauthors Azevedo, R. \ Cromley, J G. APACrefauthors \ 2004 . Does training on self-regulated learning facilitate students' learning with hypermedia? Does training on self-regulated learning facilitate students' learning with hypermedia? Journal of educational psychology 96 3 523 . https://doi.org/10.1037/0022-0663.96.3.523 blue https...

    work page doi:10.1037/0022-0663.96.3.523 2004

  7. [7]

    \ Ga s evi \'c , D

    azevedo2019analyzing APACrefauthors Azevedo, R. \ Ga s evi \'c , D. APACrefauthors \ 2019 . Analyzing multimodal multichannel data about self-regulated learning with advanced learning technologies: Issues and challenges Analyzing multimodal multichannel data about self-regulated learning with advanced learning technologies: Issues and challenges \ ( 96). ...

    work page doi:10.1016/j.chb.2019.03.025 2019

  8. [8]

    , Moos, D C

    azevedo2008externally APACrefauthors Azevedo, R. , Moos, D C. , Greene, J A. , Winters, F I. \ Cromley, J G. APACrefauthors \ 2008 . Why is externally-facilitated regulated learning more effective than self-regulated learning with hypermedia? Why is externally-facilitated regulated learning more effective than self-regulated learning with hypermedia? Educ...

    doi:10.1007/s11423-007-9067-0 2008

Show all 81 references
  1. [9]

    a ten. M \

    bannert2007metakognition APACrefauthors Bannert, M. APACrefauthors \ 2007 . Metakognition beim Lernen mit Hypermedia Metakognition beim lernen mit hypermedia . Erfassung, Beschreibung und Vermittlung wirksamer metakognitiver Lernstrategien und Regulationsaktivit \"a ten. M \"u...

    2007

  2. [10]

    APACrefauthors \ 2009

    bannert2009promoting APACrefauthors Bannert, M. APACrefauthors \ 2009 . Promoting self-regulated learning through prompts Promoting self-regulated learning through prompts . Zeitschrift f \"u r P \"a dagogische Psychologie 23 2 139--145 . https://doi.org/10.1024/1010-0652.23.2...

    2009 doi

  3. [11]

    \ Mengelkamp, C

    bannert2013scaffolding APACrefauthors Bannert, M. \ Mengelkamp, C. APACrefauthors \ 2013 . Scaffolding hypermedia learning through metacognitive prompts Scaffolding hypermedia learning through metacognitive prompts . International handbook of metacognition and learning technol...

    2013 doi

  4. [12]

    , Reimann, P

    bannert2014process APACrefauthors Bannert, M. , Reimann, P. \ Sonnenberg, C. APACrefauthors \ 2014 . Process mining techniques for analysing patterns and strategies in students’ self-regulated learning Process mining techniques for analysing patterns and strategies in students...

    2014 doi

  5. [13]

    , Segedy, J R

    biswas2016design APACrefauthors Biswas, G. , Segedy, J R. \ Bunchongchit, K. APACrefauthors \ 2016 . From design to implementation to practice a learning by teaching system: Betty’s Brain From design to implementation to practice a learning by teaching system: Betty’s brain . ...

    2016 doi

  6. [14]

    , Dunlosky, J

    bjork2013self APACrefauthors Bjork, R A. , Dunlosky, J. \ Kornell, N. APACrefauthors \ 2013 . Self-regulated learning: Beliefs, techniques, and illusions Self-regulated learning: Beliefs, techniques, and illusions . Annual review of psychology 64 417--444 . https://doi.org/10....

    2013 doi

  7. [15]

    \ Cascallar, E

    boekaerts2006far APACrefauthors Boekaerts, M. \ Cascallar, E. APACrefauthors \ 2006 . How far have we moved toward the integration of theory and practice in self-regulation? How far have we moved toward the integration of theory and practice in self-regulation? Educational psy...

    2006 doi

  8. [16]

    , Jang, N

    bowman2020impact APACrefauthors Bowman, N A. , Jang, N. , Kivlighan, D M. , Schneider, N. \ Ye, X. APACrefauthors \ 2020 . The impact of a goal-setting intervention for engineering students on academic probation The impact of a goal-setting intervention for engineering student...

    2020 doi

  9. [17]

    , Bogar \' n, A

    cerezo2020process APACrefauthors Cerezo, R. , Bogar \' n, A. , Esteban, M. \ Romero, C. APACrefauthors \ 2020 . Process mining for self-regulated learning assessment in e-learning Process mining for self-regulated learning assessment in e-learning . Journal of Computing in Hig...

    2020 doi

  10. [18]

    , Guan, R

    cheng2025self APACrefauthors Cheng, Y. , Guan, R. , Li, T. , Raković, M. , Li, X. , Fan, Y. Swiecki, Z. APACrefauthors \ 2025 . Self-regulated Learning Processes in Secondary Education: A Network Analysis of Trace-based Measures Self-regulated learning processes in secondary e...

    2025

  11. [19]

    APACrefauthors \ 2021

    chi2021translating APACrefauthors Chi, M T. APACrefauthors \ 2021 . Translating a Theory of Active Learning: An Attempt to Close the Research-Practice Gap in Education Translating a theory of active learning: An attempt to close the research-practice gap in education . Topics ...

    2021 doi

  12. [20]

    , Elen, J

    clarebout2013metacognition APACrefauthors Clarebout, G. , Elen, J. , Collazo, N A J. , Lust, G. \ Jiang, L. APACrefauthors \ 2013 . Metacognition and the use of tools Metacognition and the use of tools . International handbook of metacognition and learning technologies 187--19...

    2013 doi

  13. [21]

    \ Mayer, R E

    clark2023learning APACrefauthors Clark, R C. \ Mayer, R E. APACrefauthors \ 2023 . E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning E-learning and the science of instruction: Proven guidelines for consumers and des...

    2023 doi

  14. [22]

    APACrefauthors \ 1960

    cohen1960coefficient APACrefauthors Cohen, J. APACrefauthors \ 1960 . A coefficient of agreement for nominal scales A coefficient of agreement for nominal scales . Educational and psychological measurement 20 1 37--46 . https://doi.org/10.1177/001316446002000104 blue https://d...

    1960 doi

  15. [23]

    , Triglianos, V

    davis2018srlx APACrefauthors Davis, D. , Triglianos, V. , Hauff, C. \ Houben, G J. APACrefauthors \ 2018 . SRLx: A personalized learner interface for MOOCs Srlx: A personalized learner interface for moocs . Lifelong Technology-Enhanced Learning: 13th European Conference on Tec...

    2018

  16. [24]

    , van Braak, J

    devolder2012supporting APACrefauthors Devolder, A. , van Braak, J. \ Tondeur, J. APACrefauthors \ 2012 . Supporting self-regulated learning in computer-based learning environments: systematic review of effects of scaffolding in the domain of science education Supporting self-r...

    2012

  17. [25]

    \ Gray, G S

    di1973listening APACrefauthors Di Vesta, F J. \ Gray, G S. APACrefauthors \ 1973 . Listening and note taking: II. Immediate and delayed recall as functions of variations in thematic continuity, note taking, and length of listening-review intervals. Listening and note taking: I...

    1973 doi

  18. [26]

    , Lim, L

    fan2022improving APACrefauthors Fan, Y. , Lim, L. , van der Graaf, J. , Kilgour, J. , Rakovi \'c , M. , Moore, J. Ga s evi \'c , D. APACrefauthors \ 2022 . Improving the measurement of self-regulated learning using multi-channel data Improving the measurement of self-regulated...

    2022 doi

  19. [27]

    , Matcha, W

    fan2021learning APACrefauthors Fan, Y. , Matcha, W. , Uzir, N A. , Wang, Q. \ Ga s evi \'c , D. APACrefauthors \ 2021 . Learning analytics to reveal links between learning design and self-regulated learning Learning analytics to reveal links between learning design and self-re...

    2021 doi

  20. [28]

    , Rakovic, M

    fan2023towards APACrefauthors Fan, Y. , Rakovic, M. , van Der Graaf, J. , Lim, L. , Singh, S. , Moore, J. Ga s evi \'c , D. APACrefauthors \ 2023 . Towards a fuller picture: Triangulation and integration of the measurement of self-regulated learning based on trace and think al...

    2023 doi

  21. [29]

    , van der Graaf, J

    fan2022towards APACrefauthors Fan, Y. , van der Graaf, J. , Lim, L. , Rakovi \'c , M. , Singh, S. , Kilgour, J. Ga s evi \'c , D. APACrefauthors \ 2022 . Towards investigating the validity of measurement of self-regulated learning based on trace data Towards investigating the ...

    2022 doi

  22. [30]

    , Ga s evi \'c , D

    fincham2018study APACrefauthors Fincham, E. , Ga s evi \'c , D. , Jovanovi \'c , J. \ Pardo, A. APACrefauthors \ 2018 . From study tactics to learning strategies: An analytical method for extracting interpretable representations From study tactics to learning strategies: An an...

    2018

  23. [31]

    , Dawson, S

    gavsevic2015let APACrefauthors Ga s evi \'c , D. , Dawson, S. \ Siemens, G. APACrefauthors \ 2015 . Let’s not forget: Learning analytics are about learning Let’s not forget: Learning analytics are about learning . TechTrends 59 1 64--71 . https://doi.org/10.1007/s11528-014-082...

    2015 doi

  24. [32]

    , Siemens, G

    gavsevic2023empowering APACrefauthors Ga s evi \'c , D. , Siemens, G. \ Sadiq, S. APACrefauthors \ 2023 . Empowering learners for the age of artificial intelligence Empowering learners for the age of artificial intelligence . Computers and Education: Artificial Intelligence 4 ...

    2023

  25. [33]

    O'Reilly Media, Inc

    gormley2015elasticsearch APACrefauthors Gormley, C. \ Tong, Z. APACrefauthors \ 2015 . Elasticsearch: the definitive guide: a distributed real-time search and analytics engine Elasticsearch: the definitive guide: a distributed real-time search and analytics engine . " O'Reilly...

    2015 doi

  26. [34]

    \ Azevedo, R

    greene2009macro APACrefauthors Greene, J A. \ Azevedo, R. APACrefauthors \ 2009 . A macro-level analysis of SRL processes and their relations to the acquisition of a sophisticated mental model of a complex system A macro-level analysis of srl processes and their relations to t...

    2009 doi

  27. [35]

    APACrefauthors \ 2022

    guo2022using APACrefauthors Guo, L. APACrefauthors \ 2022 . Using metacognitive prompts to enhance self-regulated learning and learning outcomes: A meta-analysis of experimental studies in computer-based learning environments Using metacognitive prompts to enhance self-regulat...

    2022 doi

  28. [36]

    \ Azevedo, R

    hmelo2006understanding APACrefauthors Hmelo-Silver, C E. \ Azevedo, R. APACrefauthors \ 2006 . Understanding complex systems: Some core challenges Understanding complex systems: Some core challenges . The Journal of the learning sciences 15 1 53--61 . https://doi.org/10.1207/s...

    2006 doi

  29. [37]

    \ Lajoie, S P

    huang2021process APACrefauthors Huang, L. \ Lajoie, S P. APACrefauthors \ 2021 . Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development Process analysis of teachers’ self-regulated learning patterns in technolo...

    2021

  30. [38]

    a rvel \

    jarvela2023human APACrefauthors J \"a rvel \"a , S. , Nguyen, A. \ Hadwin, A. APACrefauthors \ 2023 . Human and artificial intelligence collaboration for socially shared regulation in learning Human and artificial intelligence collaboration for socially shared regulation in le...

    2023 doi

  31. [39]

    APACrefauthors \ 2021

    lajoie2021student APACrefauthors Lajoie, S P. APACrefauthors \ 2021 . Student modeling for individuals and groups: The BioWorld and HOWARD platforms Student modeling for individuals and groups: The bioworld and howard platforms . International Journal of Artificial Intelligenc...

    2021 doi

  32. [40]

    , Watson, S L

    lee2019systematic APACrefauthors Lee, D. , Watson, S L. \ Watson, W R. APACrefauthors \ 2019 . Systematic literature review on self-regulated learning in massive open online courses Systematic literature review on self-regulated learning in massive open online courses . Austra...

    2019 doi

  33. [41]

    , Fan, Y

    li2024analytics APACrefauthors Li, T. , Fan, Y. , Srivastava, N. , Zeng, Z. , Li, X. , Khosravi, H. Ga s evi \'c , D. APACrefauthors \ 2024 . Analytics of Planning Behaviours in Self-Regulated Learning: Links with Strategy Use and Prior Knowledge Analytics of planning behaviou...

    2024

  34. [42]

    , Lin, J

    li2023learners APACrefauthors Li, T. , Lin, J. , Iqbal, S. , Swiecki, Z. , Tsai, Y S. , Fan, Y. \ Ga s evi \'c , D. APACrefauthors \ 2023 . Do Learners Appreciate Adaptivity? An Epistemic Network Analysis of How Learners Perceive Adaptive Scaffolding Do learners appreciate ada...

    2023

  35. [43]

    , Bannert, M

    lim2024students APACrefauthors Lim, L. , Bannert, M. , van der Graaf, J. , Fan, Y. , Rakovic, M. , Singh, S. Ga s evi \'c , D. APACrefauthors \ 2024 . How do students learn with real-time personalized scaffolds? How do students learn with real-time personalized scaffolds? Brit...

    2024 doi

  36. [44]

    , Bannert, M

    lim2021temporal APACrefauthors Lim, L. , Bannert, M. , van der Graaf, J. , Molenaar, I. , Fan, Y. , Kilgour, J. Ga s evi \'c , D. APACrefauthors \ 2021 . Temporal assessment of self-regulated learning by mining students’ think-aloud protocols Temporal assessment of self-regula...

    2021

  37. [45]

    , Bannert, M

    lim2023effects APACrefauthors Lim, L. , Bannert, M. , van der Graaf, J. , Singh, S. , Fan, Y. , Surendrannair, S. Ga s evi \'c , D. APACrefauthors \ 2023 . Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning Effects of real-time ana...

    2023

  38. [46]

    \ Klotz, C

    maier2022personalized APACrefauthors Maier, U. \ Klotz, C. APACrefauthors \ 2022 . Personalized feedback in digital learning environments: Classification framework and literature review Personalized feedback in digital learning environments: Classification framework and litera...

    2022

  39. [47]

    , Rakovic, M

    marzouk2016if APACrefauthors Marzouk, Z. , Rakovic, M. , Liaqat, A. , Vytasek, J. , Samadi, D. , Stewart-Alonso, J. Nesbit, J C. APACrefauthors \ 2016 . What if learning analytics were based on learning science? What if learning analytics were based on learning science? Austra...

    2016 doi

  40. [48]

    , Ga s evi \'c , D

    matcha2019detection APACrefauthors Matcha, W. , Ga s evi \'c , D. , Ahmad Uzir, N. , Jovanovi \'c , J. , Pardo, A. , Maldonado-Mahauad, J. \ P \'e rez-Sanagust \' n, M. APACrefauthors \ 2019 . Detection of learning strategies: A comparison of process, sequence and network anal...

    2019

  41. [49]

    , Ga s evi\' c , D

    matcha2020analytics APACrefauthors Matcha, W. , Ga s evi\' c , D. , Uzir, N A. , Jovanovic, J. , Pardo, A. , Lim, L. Tsai, Y S. APACrefauthors \ 2020 . Analytics of Learning Strategies: Role of Course Design and Delivery Modality. Analytics of learning strategies: Role of cour...

    2020 doi

  42. [50]

    , Uzir, N A

    matcha2019systematic APACrefauthors Matcha, W. , Uzir, N A. , Gašević, D. \ Pardo, A. APACrefauthors \ 2020 . A Systematic Review of Empirical Studies on Learning Analytics Dashboards: A Self-Regulated Learning Perspective A systematic review of empirical studies on learning a...

    2020

  43. [51]

    , Roda, C

    molenaar2012dynamic APACrefauthors Molenaar, I. , Roda, C. , van Boxtel, C. \ Sleegers, P. APACrefauthors \ 2012 . Dynamic scaffolding of socially regulated learning in a computer-based learning environment Dynamic scaffolding of socially regulated learning in a computer-based...

    2012 doi

  44. [52]

    , Biswas, G

    munshi2023analysing APACrefauthors Munshi, A. , Biswas, G. , Baker, R. , Ocumpaugh, J. , Hutt, S. \ Paquette, L. APACrefauthors \ 2023 . Analysing adaptive scaffolds that help students develop self-regulated learning behaviours Analysing adaptive scaffolds that help students d...

    2023 doi

  45. [53]

    \ Narens, L

    nelson1994investigate APACrefauthors Nelson, T. \ Narens, L. APACrefauthors \ 1994 . Why investigate metacognition. Metacognition: Knowing about knowing, 13, 1-25. Why investigate metacognition. metacognition: Knowing about knowing, 13, 1-25. https://doi.org/10.7551/mitpress/4...

    1994 doi

  46. [54]

    , Kravcik, M

    nussbaumer2014framework APACrefauthors Nussbaumer, A. , Kravcik, M. , Renzel, D. , Klamma, R. , Berthold, M. \ Albert, D. APACrefauthors \ 2014 . A framework for facilitating self-regulation in responsive open learning environments A framework for facilitating self-regulation ...

    2014 doi arXiv

  47. [55]

    APACrefauthors \ 2014

    oluwatosin2014client APACrefauthors Oluwatosin, H S. APACrefauthors \ 2014 . Client-server model Client-server model . IOSR Journal of Computer Engineering 16 1 67--71 . http://dx.doi.org/10.9790/0661-16195771 blue http://dx.doi.org/10.9790/0661-16195771

    2014 doi

  48. [56]

    , Molenaar, I

    paans2019temporal APACrefauthors Paans, C. , Molenaar, I. , Segers, E. \ Verhoeven, L. APACrefauthors \ 2019 . Temporal variation in children's self-regulated hypermedia learning Temporal variation in children's self-regulated hypermedia learning . Computers in Human Behavior ...

    2019 doi

  49. [57]

    APACrefauthors \ 2017

    panadero2017review APACrefauthors Panadero, E. APACrefauthors \ 2017 . A review of self-regulated learning: Six models and four directions for research A review of self-regulated learning: Six models and four directions for research . Frontiers in psychology 422 . https://doi....

    2017

  50. [58]

    \ Economides, A A

    papamitsiou2014learning APACrefauthors Papamitsiou, Z. \ Economides, A A. APACrefauthors \ 2014 . Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence Learning analytics and educational data mining in practice: A syst...

    2014

  51. [59]

    APACrefauthors \ 2009

    reimann2009time APACrefauthors Reimann, P. APACrefauthors \ 2009 . Time is precious: Variable-and event-centred approaches to process analysis in CSCL research Time is precious: Variable-and event-centred approaches to process analysis in cscl research . International Journal ...

    2009 doi

  52. [60]

    , Fan, Y

    saint2022temporally APACrefauthors Saint, J. , Fan, Y. , Ga s evi \'c , D. \ Pardo, A. APACrefauthors \ 2022 . Temporally-focused analytics of self-regulated learning: A systematic review of literature Temporally-focused analytics of self-regulated learning: A systematic revie...

    2022

  53. [62]

    , Whitelock-Wainwright, A

    saint2020trace APACrefauthors Saint, J. , Whitelock-Wainwright, A. , Ga s evi \'c , D. \ Pardo, A. APACrefauthors \ 2020 . Trace-SRL: a framework for analysis of microlevel processes of self-regulated learning from trace data Trace-srl: a framework for analysis of microlevel p...

    2020

  54. [63]

    , Collier, W

    shaffer2016tutorial APACrefauthors Shaffer, D W. , Collier, W. \ Ruis, A R. APACrefauthors \ 2016 . A tutorial on epistemic network analysis: Analyzing the structure of connections in cognitive, social, and interaction data A tutorial on epistemic network analysis: Analyzing t...

    2016 doi

  55. [64]

    \ Hannafin, M J

    sharma2007scaffolding APACrefauthors Sharma, P. \ Hannafin, M J. APACrefauthors \ 2007 . Scaffolding in technology-enhanced learning environments Scaffolding in technology-enhanced learning environments . Interactive learning environments 15 1 27--46 . https://doi.org/10.1080/...

    2007 doi

  56. [65]

    , Ga s evi\' c , D

    siadaty2016trace APACrefauthors Siadaty, M. , Ga s evi\' c , D. \ Hatala, M. APACrefauthors \ 2016 . Trace-based micro-analytic measurement of self-regulated learning processes Trace-based micro-analytic measurement of self-regulated learning processes . Journal of Learning An...

    2016 doi

  57. [66]

    , Fan, Y

    srivastava2022effects APACrefauthors Srivastava, N. , Fan, Y. , Rakovic, M. , Singh, S. , Jovanovic, J. , Van Der Graaf, J. others APACrefauthors \ 2022 . Effects of Internal and External Conditions on Strategies of Self-regulated Learning: A Learning Analytics Study Effects o...

    2022

  58. [67]

    , Ga s evi \'c , D

    uzir2020analytics APACrefauthors Uzir, N A. , Ga s evi \'c , D. , Jovanovi \'c , J. , Matcha, W. , Lim, L A. \ Fudge, A. APACrefauthors \ 2020 . Analytics of time management and learning strategies for effective online learning in blended environments Analytics of time managem...

    2020

  59. [68]

    , Lim, L

    van2021instrumentation APACrefauthors van der Graaf, J. , Lim, L. , Fan, Y. , Kilgour, J. , Moore, J. , Bannert, M. Molenaar, I. APACrefauthors \ 2021 . Do instrumentation tools capture self-regulated learning? Do instrumentation tools capture self-regulated learning? LAK21: 1...

    2021

  60. [69]

    , Lim, L

    van2022dynamics APACrefauthors van der Graaf, J. , Lim, L. , Fan, Y. , Kilgour, J. , Moore, J. , Ga s evi \'c , D. Molenaar, I. APACrefauthors \ 2022 . The dynamics between self-regulated learning and learning outcomes: An exploratory approach and implications The dynamics bet...

    2022 doi

  61. [70]

    , Rakovi \'c , M

    van2023design APACrefauthors van der Graaf, J. , Rakovi \'c , M. , Fan, Y. , Lim, L. , Singh, S. , Bannert, M. Molenaar, I. APACrefauthors \ 2023 . How to design and evaluate personalized scaffolds for self-regulated learning How to design and evaluate personalized scaffolds f...

    2023 doi

  62. [71]

    APACrefauthors \ 2007

    veenman2007assessment APACrefauthors Veenman, M V. APACrefauthors \ 2007 . The assessment and instruction of self-regulation in computer-based environments: a discussion The assessment and instruction of self-regulation in computer-based environments: a discussion . Metacognit...

    2007 doi

  63. [72]

    APACrefauthors \ 2017

    winne2017learning APACrefauthors Winne, P H. APACrefauthors \ 2017 . Learning analytics for self-regulated learning Learning analytics for self-regulated learning . Handbook of learning analytics 241--249 . https://doi.org/10.18608/hla17.021 blue https://doi.org/10.18608/hla17.021

    2017 doi

  64. [73]

    APACrefauthors \ 2018

    winne2018theorizing APACrefauthors Winne, P H. APACrefauthors \ 2018 . Theorizing and researching levels of processing in self-regulated learning Theorizing and researching levels of processing in self-regulated learning . British Journal of Educational Psychology 88 1 9--20 ....

    2018 doi

  65. [74]

    \ Hadwin, A

    winne1998studying APACrefauthors Winne, P H. \ Hadwin, A. APACrefauthors \ 1998 . Studying as self-regulated learning. Metacognition in educational theory and practice, D. HACKER, J. DUNLOSKY, AND A. GRAESSER, Eds. Studying as self-regulated learning. metacognition in educatio...

    1998

  66. [75]

    \ Perry, N E

    winne2000measuring APACrefauthors Winne, P H. \ Perry, N E. APACrefauthors \ 2000 . Measuring self-regulated learning Measuring self-regulated learning . Handbook of self-regulation Handbook of self-regulation \ ( \ 531--566). Elsevier . https://doi.org/10.1016/B978-012109890-...

    2000 doi

  67. [76]

    , Teng, K

    winne2019nstudy APACrefauthors Winne, P H. , Teng, K. , Chang, D. , Lin, M P C. , Marzouk, Z. , Nesbit, J C. Vytasek, J. APACrefauthors \ 2019 . nStudy: Software for learning analytics about processes for self-regulated learning nstudy: Software for learning analytics about pr...

    2019 doi

  68. [77]

    , Zierer, K

    wisniewski2020power APACrefauthors Wisniewski, B. , Zierer, K. \ Hattie, J. APACrefauthors \ 2020 . The power of feedback revisited: A meta-analysis of educational feedback research The power of feedback revisited: A meta-analysis of educational feedback research . Frontiers i...

    2020

  69. [78]

    \ Joy, M

    yau2009mobile APACrefauthors Yau, J K. \ Joy, M. APACrefauthors \ 2009 . A mobile context-aware framework for supporting self-regulated learners A mobile context-aware framework for supporting self-regulated learners . https://wrap.warwick.ac.uk/id/eprint/60327/ blue https://w...

    2009

  70. [79]

    , Chatti, M A

    yousef2015video APACrefauthors Yousef, A M F. , Chatti, M A. , Danoyan, N. , Th \"u s, H. \ Schroeder, U. APACrefauthors \ 2015 . Video-mapper: A video annotation tool to support collaborative learning in moocs Video-mapper: A video annotation tool to support collaborative lea...

    2015

  71. [80]

    APACrefauthors \ 1986

    zimmerman1986becoming APACrefauthors Zimmerman, B J. APACrefauthors \ 1986 . Becoming a self-regulated learner: Which are the key subprocesses? Becoming a self-regulated learner: Which are the key subprocesses? Contemporary educational psychology 11 4 307--313 . https://doi.or...

    1986 doi

  72. [81]

    APACrefauthors \ 2000

    zimmerman2000attaining APACrefauthors Zimmerman, B J. APACrefauthors \ 2000 . Attaining self-regulation: A social cognitive perspective Attaining self-regulation: A social cognitive perspective . Handbook of self-regulation Handbook of self-regulation \ ( \ 13--39). Elsevier ....

    2000 doi

  73. [82]

    APACrefauthors \ 2013

    zimmerman2013cognitive APACrefauthors Zimmerman, B J. APACrefauthors \ 2013 . From cognitive modeling to self-regulation: A social cognitive career path From cognitive modeling to self-regulation: A social cognitive career path . Educational psychologist 48 3 135--147 . http:/...

    2013

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.