Detection of Learning Strategies: A Comparison of Process, Sequence and Network Analytic Approaches

Wannisa Matcha; Dragan Gašević; N. Ahmad Uzir; Jelena Jovanović; Abelardo Pardo; Jorge Maldonado-Mahauad; Mar Pérez-Sanagustín

doi:10.1007/978-3-030-29736-7_39

Detection of Learning Strategies: A Comparison of Process, Sequence and Network Analytic Approaches

Wannisa Matcha
, Dragan Gašević
, N. Ahmad Uzir
, Jelena Jovanović
, Abelardo Pardo
, Jorge Maldonado-Mahauad
, Mar Pérez-Sanagustín

University of Edinburgh
Monash University
Universiti Teknologi MARA
University of Belgrade
University of South Australia
Pontificia Universidad Católica de Chile
Université Tolouse III Paul Sabatier

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

46 Scopus citations

Abstract

Research in learning analytics proposed different computational techniques to detect learning tactics and strategies adopted by learners in digital environments through the analysis of students’ trace data. While many promising insights have been produced, there has been much less understanding about how and to what extent different data analytic approaches influence results. This paper presents a comparison of three analytic approaches including process, sequence, and network approaches for detection of learning tactics and strategies. The analysis was performed on a dataset collected in a massive open online course on software programming. All three approaches produced four tactics and three strategy groups. The tactics detected by using the sequence analysis approach differed from those identified by the other two methods. The process and network analytic approaches had more than 66% of similarity in the detected tactics. Learning strategies detected by the three approaches proved to be highly similar.

Original language	English
Title of host publication	Transforming Learning with Meaningful Technologies - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Proceedings
Editors	Maren Scheffel, Julien Broisin, Viktoria Pammer-Schindler, Andri Ioannou, Jan Schneider
Publisher	Springer Verlag
Pages	525-540
Number of pages	16
ISBN (Print)	9783030297350
DOIs	https://doi.org/10.1007/978-3-030-29736-7_39
State	Published - 2019
Event	14th European Conference on Technology Enhanced Learning, EC-TEL 2019 - Delft, Netherlands Duration: 16 Sep 2019 → 19 Sep 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11722 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th European Conference on Technology Enhanced Learning, EC-TEL 2019
Country/Territory	Netherlands
City	Delft
Period	16/09/19 → 19/09/19

Keywords

Data analytics
Learning analytics
Learning strategy

Access to Document

10.1007/978-3-030-29736-7_39

Cite this

Matcha, W., Gašević, D., Ahmad Uzir, N., Jovanović, J., Pardo, A., Maldonado-Mahauad, J., & Pérez-Sanagustín, M. (2019). Detection of Learning Strategies: A Comparison of Process, Sequence and Network Analytic Approaches. In M. Scheffel, J. Broisin, V. Pammer-Schindler, A. Ioannou, & J. Schneider (Eds.), Transforming Learning with Meaningful Technologies - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Proceedings (pp. 525-540). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11722 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-29736-7_39

Matcha, Wannisa ; Gašević, Dragan ; Ahmad Uzir, N. et al. / Detection of Learning Strategies : A Comparison of Process, Sequence and Network Analytic Approaches. Transforming Learning with Meaningful Technologies - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Proceedings. editor / Maren Scheffel ; Julien Broisin ; Viktoria Pammer-Schindler ; Andri Ioannou ; Jan Schneider. Springer Verlag, 2019. pp. 525-540 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f60c00b0d6154c13bddfec15de09a8ef,

title = "Detection of Learning Strategies: A Comparison of Process, Sequence and Network Analytic Approaches",

abstract = "Research in learning analytics proposed different computational techniques to detect learning tactics and strategies adopted by learners in digital environments through the analysis of students{\textquoteright} trace data. While many promising insights have been produced, there has been much less understanding about how and to what extent different data analytic approaches influence results. This paper presents a comparison of three analytic approaches including process, sequence, and network approaches for detection of learning tactics and strategies. The analysis was performed on a dataset collected in a massive open online course on software programming. All three approaches produced four tactics and three strategy groups. The tactics detected by using the sequence analysis approach differed from those identified by the other two methods. The process and network analytic approaches had more than 66\% of similarity in the detected tactics. Learning strategies detected by the three approaches proved to be highly similar.",

keywords = "Data analytics, Learning analytics, Learning strategy",

author = "Wannisa Matcha and Dragan Ga{\v s}evi{\'c} and \{Ahmad Uzir\}, N. and Jelena Jovanovi{\'c} and Abelardo Pardo and Jorge Maldonado-Mahauad and Mar P{\'e}rez-Sanagust{\'i}n",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 14th European Conference on Technology Enhanced Learning, EC-TEL 2019 ; Conference date: 16-09-2019 Through 19-09-2019",

year = "2019",

doi = "10.1007/978-3-030-29736-7\_39",

language = "Ingl{\'e}s",

isbn = "9783030297350",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "525--540",

editor = "Maren Scheffel and Julien Broisin and Viktoria Pammer-Schindler and Andri Ioannou and Jan Schneider",

booktitle = "Transforming Learning with Meaningful Technologies - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Proceedings",

}

Matcha, W, Gašević, D, Ahmad Uzir, N, Jovanović, J, Pardo, A, Maldonado-Mahauad, J & Pérez-Sanagustín, M 2019, Detection of Learning Strategies: A Comparison of Process, Sequence and Network Analytic Approaches. in M Scheffel, J Broisin, V Pammer-Schindler, A Ioannou & J Schneider (eds), Transforming Learning with Meaningful Technologies - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11722 LNCS, Springer Verlag, pp. 525-540, 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Delft, Netherlands, 16/09/19. https://doi.org/10.1007/978-3-030-29736-7_39

Detection of Learning Strategies: A Comparison of Process, Sequence and Network Analytic Approaches. / Matcha, Wannisa; Gašević, Dragan; Ahmad Uzir, N. et al.
Transforming Learning with Meaningful Technologies - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Proceedings. ed. / Maren Scheffel; Julien Broisin; Viktoria Pammer-Schindler; Andri Ioannou; Jan Schneider. Springer Verlag, 2019. p. 525-540 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11722 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Detection of Learning Strategies

T2 - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019

AU - Matcha, Wannisa

AU - Gašević, Dragan

AU - Ahmad Uzir, N.

AU - Jovanović, Jelena

AU - Pardo, Abelardo

AU - Maldonado-Mahauad, Jorge

AU - Pérez-Sanagustín, Mar

PY - 2019

Y1 - 2019

N2 - Research in learning analytics proposed different computational techniques to detect learning tactics and strategies adopted by learners in digital environments through the analysis of students’ trace data. While many promising insights have been produced, there has been much less understanding about how and to what extent different data analytic approaches influence results. This paper presents a comparison of three analytic approaches including process, sequence, and network approaches for detection of learning tactics and strategies. The analysis was performed on a dataset collected in a massive open online course on software programming. All three approaches produced four tactics and three strategy groups. The tactics detected by using the sequence analysis approach differed from those identified by the other two methods. The process and network analytic approaches had more than 66% of similarity in the detected tactics. Learning strategies detected by the three approaches proved to be highly similar.

AB - Research in learning analytics proposed different computational techniques to detect learning tactics and strategies adopted by learners in digital environments through the analysis of students’ trace data. While many promising insights have been produced, there has been much less understanding about how and to what extent different data analytic approaches influence results. This paper presents a comparison of three analytic approaches including process, sequence, and network approaches for detection of learning tactics and strategies. The analysis was performed on a dataset collected in a massive open online course on software programming. All three approaches produced four tactics and three strategy groups. The tactics detected by using the sequence analysis approach differed from those identified by the other two methods. The process and network analytic approaches had more than 66% of similarity in the detected tactics. Learning strategies detected by the three approaches proved to be highly similar.

KW - Data analytics

KW - Learning analytics

KW - Learning strategy

UR - https://www.scopus.com/pages/publications/85072953731

U2 - 10.1007/978-3-030-29736-7_39

DO - 10.1007/978-3-030-29736-7_39

M3 - Contribución a la conferencia

AN - SCOPUS:85072953731

SN - 9783030297350

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 525

EP - 540

BT - Transforming Learning with Meaningful Technologies - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Proceedings

A2 - Scheffel, Maren

A2 - Broisin, Julien

A2 - Pammer-Schindler, Viktoria

A2 - Ioannou, Andri

A2 - Schneider, Jan

PB - Springer Verlag

Y2 - 16 September 2019 through 19 September 2019

ER -

Matcha W, Gašević D, Ahmad Uzir N, Jovanović J, Pardo A, Maldonado-Mahauad J et al. Detection of Learning Strategies: A Comparison of Process, Sequence and Network Analytic Approaches. In Scheffel M, Broisin J, Pammer-Schindler V, Ioannou A, Schneider J, editors, Transforming Learning with Meaningful Technologies - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Proceedings. Springer Verlag. 2019. p. 525-540. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-29736-7_39

Detection of Learning Strategies: A Comparison of Process, Sequence and Network Analytic Approaches

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