Utilizing artificial intelligence to support analyzing self-regulated learning: : A preliminary mixed-methods evaluation from a human-centered perspective
Authors: 
C.-Y. Wang, John J.H. LinAuthors Info & Claims
References
[1]
E. Alyahyan, D. Düştegör, Predicting academic success in higher education: Literature review and best practices, International Journal of Educational Technology in Higher Education 17 (1) (2020) 3,.
[2]
D. Anguita, L. Ghelardoni, A. Ghio, L. Oneto, S. Ridella, The ‘k’in k-fold cross validation, in: Paper presented at the 20th European symposium on artificial neural networks, Computational Intelligence and Machine Learning (ESANN), 2012.
[3]
A.R. Artino Jr., T.J. Cleary, T. Dong, P.A. Hemmer, S.J. Durning, Exploring clinical reasoning in novices: A self-regulated learning microanalytic assessment approach, Medical Education 48 (3) (2014) 280–291,.
[4]
R. Azevedo, F. Bouchet, M. Duffy, J. Harley, M. Taub, G. Trevors, …., R. Cerezo, Lessons learned and future directions of metatutor: Leveraging multichannel data to scaffold self-regulated learning with an intelligent tutoring system, Frontiers in Psychology 13 (2022),.
[5]
R. Azevedo, J.G. Cromley, Does training on self-regulated learning facilitate students' learning with hypermedia?, Journal of Educational Psychology 96 (3) (2004) 523–535,.
[6]
R. Azevedo, D.C. Moos, A.M. Johnson, A.D. Chauncey, Measuring cognitive and metacognitive regulatory processes during hypermedia learning: Issues and challenges, Educational Psychologist 45 (4) (2010) 210–223,.
[7]
R. Balyan, K.S. McCarthy, D.S. McNamara, Applying natural language processing and hierarchical machine learning approaches to text difficulty classification, International Journal of Artificial Intelligence in Education 30 (3) (2020) 337–370,.
[8]
S. Bonner, P. Chen, K. Jones, B. Milonovich, Formative assessment of computational thinking: Cognitive and metacognitive processes, Applied Measurement in Education 34 (1) (2021) 27–45,.
[9]
R. Bybee, B. McCrae, R. Laurie, Pisa 2006: An assessment of scientific literacy, Journal of Research in Science Teaching 46 (8) (2009) 865–883,.
[10]
G. Canbek, S. Sagiroglu, T.T. Temizel, N. Baykal, Binary classification performance measures/metrics: A comprehensive visualized roadmap to gain new insights, 5-8 Oct. 2017 in: Paper presented at the 2017 international conference on computer science and engineering (UBMK), 2017.
[11]
T. Cheuk, J. Osborne, K. Cunningham, K. Haudek, M. Santiago, M. Urban-Lurain, …., B. Donovan, Towards an equitable design framework of developing argumentation in science tasks and rubrics for machine learning, in: Paper presented at the annual meeting of the national association for research in science teaching (NARST). Baltimore, MD, 2019.
[12]
G.G. Chowdhury, Natural language processing, Annual Review of Information Science & Technology 37 (1) (2003) 51–89,.
[13]
J.T. Connor, R.D. Martin, L.E. Atlas, Recurrent neural networks and robust time series prediction, IEEE Transactions on Neural Networks 5 (2) (1994) 240–254,.
[14]
R. Dabare, K.W. Wong, P. Koutsakis, M.F. Shiratuddin, A study of the effect of dropout on imbalanced data classification using deep neural networks, J Multidiscip Eng Sci Technol (JMEST) 5 (10) (2018) 2458–9403.
[15]
D. Dessì, G. Fenu, M. Marras, D. Reforgiato Recupero, Bridging learning analytics and cognitive computing for big data classification in micro-learning video collections, Computers in Human Behavior 92 (2019) 468–477,.
[16]
N.A. Diamantidis, D. Karlis, E.A. Giakoumakis, Unsupervised stratification of cross-validation for accuracy estimation, Artificial Intelligence 116 (1) (2000) 1–16,.
[17]
T. Duncan, p. Pintrich, d. Smith, W. McKeachie, Motivated strategies for learning questionnaire (mslq) manual, 2015.
[18]
A. Elsheikh, S. Yacout, M.-S. Ouali, Bidirectional handshaking lstm for remaining useful life prediction, Neurocomputing 323 (2019) 148–156,.
[19]
T. Fawcett, An introduction to roc analysis, Pattern Recognition Letters 27 (8) (2006) 861–874,.
[20]
R. Gandomkar, K. Yazdani, L. Fata, R. Mehrdad, A. Mirzazadeh, M. Jalili, et al., Using multiple self-regulated learning measures to understand medical students' biomedical science learning, Medical Education 54 (8) (2020) 727–737,.
[21]
T. van Gog, F. Paas, J.J.G. van Merrienboer, P. Witte, Uncovering the problem-solving process: Cued retrospective reporting versus concurrent and retrospective reporting, Journal of Experimental Psychology: Applied 11 (4) (2005) 237–244,.
[22]
T. van Gog, F. Paas, J.J. van Merriënboer, P. Witte, Uncovering the problem-solving process: Cued retrospective reporting versus concurrent and retrospective reporting, Journal of Experimental Psychology: Applied 11 (4) (2005) 237–244,.
[23]
A. Graves, S. Fernández, J. Schmidhuber, Bidirectional lstm networks for improved phoneme classification and recognition, 2005// in: Paper presented at the artificial neural networks: Formal models and their applications – ICANN 2005, berlin, heidelberg, 2005.
[24]
A. Graves, S. Fernández, J. Schmidhuber, Bidirectional lstm networks for improved phoneme classification and recognition, in: Paper presented at the international conference on artificial neural networks, 2005.
[25]
J.A. Greene, K.R. Muis, S. Pieschl, The role of epistemic beliefs in students' self-regulated learning with computer-based learning environments: Conceptual and methodological issues, Educational Psychologist 45 (4) (2010) 245–257,.
[26]
A. Gulli, S. Pal, Deep learning with keras, Packt Publishing Ltd, 2017.
[27]
S. Heirweg, M. De Smul, G. Devos, H. Van Keer, Profiling upper primary school students' self-regulated learning through self-report questionnaires and think-aloud protocol analysis, Learning and Individual Differences 70 (2019) 155–168,.
[28]
S. Hochreiter, The vanishing gradient problem during learning recurrent neural nets and problem solutions, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 6 (2) (1998) 107–116,.
[29]
S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Computation 9 (8) (1997) 1735–1780.
[30]
J. Huang, C. Ling, Using auc and accuracy in evaluating learning algorithms, Knowledge and Data Engineering, IEEE Transactions on 17 (2005) 299–310,.
[31]
S. Huang, H. Wang, Bi-lstm neural networks for Chinese grammatical error diagnosis, dec 2016, (Osaka, Japan).
[32]
J. Hu, X. Gao, Using think-aloud protocol in self-regulated reading research, Educational Research Review 22 (2017) 181–193,.
[33]
G. Jebur, H. Al-Samarraie, A.I. Alzahrani, An adaptive metalearner-based flow: A tool for reducing anxiety and increasing self-regulation, User Modeling and User-Adapted Interaction 32 (3) (2022) 469–501,.
[34]
Z. Kastrati, F. Dalipi, A.S. Imran, K. Pireva Nuci, M.A. Wani, Sentiment analysis of students' feedback with nlp and deep learning: A systematic mapping study, Applied Sciences 11 (9) (2021) 3986,.
[35]
Keras, Text preprocessing, 2015, Retrieved from https://keras.io/api/preprocessing/text/.
[36]
Keras, Timeseries data preprocessing, 2015, Retrieved from https://keras.io/api/preprocessing/timeseries/.
[37]
J. Kiesewetter, R. Ebersbach, A. Görlitz, M. Holzer, M.R. Fischer, R. Schmidmaier, Cognitive problem solving patterns of medical students correlate with success in diagnostic case solutions, Plos One 8 (8) (2013),.
[38]
J. Kiesewetter, R. Ebersbach, N. Tsalas, M. Holzer, R. Schmidmaier, M. Fischer, Knowledge is not enough to solve the problems – the role of diagnostic knowledge in clinical reasoning activities, BMC Medical Education 16 (2016),.
[39]
A.G. Lalkhen, A. McCluskey, Clinical tests: Sensitivity and specificity, Continuing Education in Anaesthesia, Critical Care & Pain 8 (6) (2008) 221–223,.
[40]
J.R. Landis, G.G. Koch, The measurement of observer agreement for categorical data, Biometrics 33 (1) (1977) 159–174,.
[41]
O.L. Liu, C. Brew, J. Blackmore, L. Gerard, J. Madhok, M.C. Linn, Automated scoring of constructed‐response science items: Prospects and obstacles, Educational Measurement: Issues and Practice 33 (2) (2014) 19–28,.
[42]
G. Liu, J. Guo, Bidirectional lstm with attention mechanism and convolutional layer for text classification, Neurocomputing 337 (2019) 325–338,.
[43]
M. Lombard, J. Snyder-Duch, C.C. Bracken, Content analysis in mass communication: Assessment and reporting of intercoder reliability, Human Communication Research 28 (4) (2002) 587–604,.
[44]
S. Maestrales, X. Zhai, I. Touitou, Q. Baker, B. Schneider, J. Krajcik, Using machine learning to score multi-dimensional assessments of chemistry and physics, Journal of Science Education and Technology 30 (2) (2021) 239–254,.
[45]
J. Malmberg, S. Järvelä, P.A. Kirschner, Elementary school students' strategic learning: Does task-type matter?, Metacognition and Learning 9 (2) (2014) 113–136,.
[46]
W. Matcha, N.A. Uzir, D. Gašević, A. Pardo, A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective, IEEE Transactions on Learning Technologies 13 (2) (2020) 226–245,.
[47]
M. Miljanovic, Comparative analysis of recurrent and finite impulse response neural networks in time series prediction, Indian Journal of Computer Science and Engineering 3 (2012).
[48]
I. Molenaar, The concept of hybrid human-ai regulation: Exemplifying how to support young learners' self-regulated learning, Computers and Education: Artificial Intelligence 3 (2022),.
[49]
D.C. Moos, R. Azevedo, Self-regulated learning with hypermedia: The role of prior domain knowledge, Contemporary Educational Psychology 33 (2) (2008) 270–298,.
[50]
S. Mutasa, S. Sun, R. Ha, Understanding artificial intelligence based radiology studies: What is overfitting?, Clinical Imaging 65 (2020) 96–99,.
[51]
R. Nehm, H. Härtig, Human vs. Computer diagnosis of students' natural selection knowledge: Testing the efficacy of text analytic software, Journal of Science Education and Technology 21 (2012) 56–73,.
[52]
Z. Papamitsiou, A.A. Economides, Exploring autonomous learning capacity from a self-regulated learning perspective using learning analytics, British Journal of Educational Technology 50 (6) (2019) 3138–3155,.
[53]
P. Pintrich, D. Smith, T. Duncan, W. McKeachie, Reliability and predictive validity of the motivated strategies for learning questionnaire (mslq), Educational and Psychological Measurement - EDUC PSYCHOL MEAS 53 (1993) 801–813,.
[54]
M.M. Rahman, Y. Watanobe, K. Nakamura, Source code assessment and classification based on estimated error probability using attentive lstm language model and its application in programming education, Applied Sciences 10 (8) (2020) 2973,.
[55]
A. Rogiers, E. Merchie, H. Van Keer, What they say is what they do? Comparing task-specific self-reports, think-aloud protocols, and study traces for measuring secondary school students' text-learning strategies, European Journal of Psychology of Education 35 (2) (2020) 315–332,.
[56]
J.M. Rosenberg, C. Krist, Correction to: Combining machine learning and qualitative methods to elaborate students' ideas about the generality of their model-based explanations, Journal of Science Education and Technology 30 (2) (2021),. 268-268.
[57]
A. Roth, S. Ogrin, B. Schmitz, Assessing self-regulated learning in higher education: A systematic literature review of self-report instruments, Educational Assessment, Evaluation and Accountability 28 (3) (2016) 225–250,.
[58]
D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning internal representations by error propagation, 1985, (Retrieved from).
[59]
J. Saint, A. Whitelock-Wainwright, D. Gašević, A. Pardo, Trace-srl: A framework for analysis of microlevel processes of self-regulated learning from trace data, IEEE Transactions on Learning Technologies 13 (4) (2020) 861–877,.
[60]
T. Saito, M. Rehmsmeier, The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets, PLoS One 10 (3) (2015),.
[61]
G. Schellings, Applying learning strategy questionnaires: Problems and possibilities, Metacognition and Learning 6 (2) (2011) 91–109,.
[62]
A. Sherstinsky, Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network, Physica D: Nonlinear Phenomena 404 (2020),.
[63]
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, Journal of Machine Learning Research 15 (2014) 1929–1958.
[64]
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, Journal of Machine Learning Research 15 (1) (2014) 1929–1958.
[65]
J. Sun, Jieba Chinese word segmentation tool, 2012.
[66]
W. Sun, C.-P. Chou, A.W. Stacy, H. Ma, J. Unger, P. Gallaher, Sas and spss macros to calculate standardized cronbach's alpha using the upper bound of the phi coefficient for dichotomous items, Behavior Research Methods 39 (1) (2007) 71–81.
[67]
S.H. Sung, C. Li, G. Chen, X. Huang, C. Xie, J. Massicotte, et al., How does augmented observation facilitate multimodal representational thinking? Applying deep learning to decode complex student construct, Journal of Science Education and Technology 30 (2) (2021) 210–226,.
[68]
E. Valuations, A review on evaluation metrics for data classification evaluations, 2015.
[69]
S. Vandevelde, H. Keer, G. Schellings, B. Van Hout-Wolters, Using think-aloud protocol analysis to gain in-depth insights into upper primary school children's self-regulated learning, Learning and Individual Differences 43 (2015) 11–30,.
[70]
S. Vandevelde, H. Van Keer, G. Schellings, B. Van Hout-Wolters, Using think-aloud protocol analysis to gain in-depth insights into upper primary school children's self-regulated learning, Learning and Individual Differences 43 (2015) 11–30,.
[71]
M.V.J. Veenman, B.H.A.M. van Hout-Wolters, P. Afflerbach, Metacognition and learning: Conceptual and methodological considerations, Metacognition and Learning 1 (1) (2006) 3–14,.
[72]
H. Waheed, S.-U. Hassan, N.R. Aljohani, J. Hardman, S. Alelyani, R. Nawaz, Predicting academic performance of students from vle big data using deep learning models, Computers in Human Behavior 104 (2020),.
[73]
C. Wang, X. Liu, L. Wang, Y. Sun, H. Zhang, Automated scoring of Chinese grades 7–9 students' competence in interpreting and arguing from evidence, Journal of Science Education and Technology 30 (2) (2021) 269–282,.
[74]
C.-T. Wen, C.-J. Chang, M.-H. Chang, S.-H.F. Chiang, C.-C. Liu, F.-K. Hwang, et al., The learning analytics of model-based learning facilitated by a problem-solving simulation game, Instructional Science 46 (6) (2018) 847–867.
[75]
P.H. Winne, Improving measurements of self-regulated learning, Educational Psychologist 45 (4) (2010) 267–276,.
[76]
P.H. Winne, A.F. Hadwin, Studying as self-regulated learning, in: D.J. Hacker, J. Dunlosky, A.C. Graesser (Eds.), Metacognition in educational theory and practice, Lawrence Erlbaum, Mahwah, NJ, 1998, pp. 277–304.
[77]
P.H. Winne, N.E. Perry, Measuring self-regulated learning, in: M. Boekaerts, M. Zeidner, P.R. Pintrich (Eds.), Handbook of self-regulation, Academic Press, San Diego, CA, 2000, pp. 531–566.
[78]
W. Xing, H.-S. Lee, A. Shibani, Identifying patterns in students' scientific argumentation: Content analysis through text mining using latent dirichlet allocation, Educational Technology Research & Development 68 (5) (2020) 2185–2214,.
[79]
M.-H. Yen, S. Chen, C.-Y. Wang, H.-L. Chen, Y.-S. Hsu, T.-C. Liu, A framework for self-regulated digital learning (srdl), Journal of Computer Assisted Learning 34 (5) (2018) 580–589,.
[80]
C.K. Yeung, D.-Y. Yeung, Incorporating features learned by an enhanced deep knowledge tracing model for stem/non-stem job prediction, International Journal of Artificial Intelligence in Education 29 (2019),.
[81]
Y. Yu, X. Si, C. Hu, J. Zhang, A review of recurrent neural networks: Lstm cells and network architectures, Neural Computation 31 (7) (2019) 1235–1270,.
[82]
W.-X. Zhang, Y.-S. Hsu, C.-Y. Wang, Y.-T. Ho, Exploring the impacts of cognitive and metacognitive prompting on students' scientific inquiry practices within an e-learning environment, International Journal of Science Education 37 (3) (2015) 529–553,.
[83]
B.J. Zimmerman, Becoming a self-regulated learner: An overview, Theory into Practice 41 (2) (2002) 64–70.
Recommendations
Utilizing the Generalized Intelligent Framework for Tutoring to Encourage Self-Regulated Learning
8th International Conference on Foundations of Augmented Cognition. Advancing Human Performance and Decision-Making through Adaptive Systems - Volume 8534Self-regulated learning is of particular importance to computer-based and online instruction, as students need to manage their own time and their interactions with the system. Elements of self-regulatory learning traditionally include the metacognitive ...
Promoting self-regulated learning in web-based learning environments
Self-regulated learning with the Internet or hypermedia requires not only cognitive learning strategies, but also specific and general meta-cognitive strategies. The purposes of the Study2000 project, carried out at the TU Dresden, were to develop and ...
Self-Regulated Learning and Scientific Research Using Artificial Intelligence for Higher Education Systems
Self-regulated learning (SRL) is a very significant ability for students in the learning process, and SRL strategies are used to assist students in learning efficiently in higher education. Many SRL environments face challenges and barriers, including ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Elsevier Ltd.
Publisher
Elsevier Science Publishers B. V.
Netherlands
Publication History
Published: 01 July 2023
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 30 Dec 2024