An Instrument for Observing Teams to Explicate Regulation Strategies in Computer Science
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| Title: | An Instrument for Observing Teams to Explicate Regulation Strategies in Computer Science |
|---|---|
| Language: | English |
| Authors: | Carolin Wortmann (ORCID |
| Source: | ACM Transactions on Computing Education. 2026 26(2). |
| Availability: | Association for Computing Machinery. 1601 Broadway 10th Floor, New York, NY 10119. Tel: 800-342-6626; Tel: 212-626-0500; Fax: 212-944-1318; e-mail: acmhelp@acm.org; Web site: http://toce.acm.org/ |
| Peer Reviewed: | Y |
| Page Count: | 29 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Computer Science Education, Computer Software, Cooperative Learning, Active Learning, Laboratories, Capstone Experiences, Learning Strategies, Observation, Measures (Individuals), Self Management, Metacognition |
| DOI: | 10.1145/3779308 |
| ISSN: | 1946-6226 |
| Abstract: | Motivation and Objectives: Informed by a long tradition of studying regulation strategies in general education, recent work in Computing Education Research has highlighted the importance and effects of such strategies in computing-related classrooms as well. Most of this work has focused on self-regulation of individuals or dyads, for example, in pair programming. Little, however, is known about regulation in larger groups which naturally occur in, for example, active-learning classrooms, Software Engineering lab courses, or capstone projects. Moreover, research involving regulation strategies, including, but not limited to computing contexts, predominantly relies on self-reported data. While recent work has advocated using trace data from learning management systems to assess self-regulation strategies more objectively, such systems cannot capture regulation as it occurs in many versions of group work, for example, in group discussions and interactions in a classroom. We thus aim to develop an observation instrument that can help researchers to explicate regulation strategies in Computer Science and, more generally, computing-related group work. Together with self-reported data and--where applicable--trace data, such an instrument would facilitate obtaining a more complete picture of where and how regulation strategies emerge and develop. Methods: Using data from semi-structured interviews with students from two capstone projects, we conducted a deductive qualitative analysis to add concrete descriptors to Miller and Hadwin's framework for regulated learning. We refined the resulting coding scheme into an observation instrument that was field-tested for saturation, ease-of-use, and reliability in a Software Engineering lab course. Results: Our main result is OTTERS, an instrument for observing teams to explicate regulation strategies in computing contexts. Initial field-testing indicates that this instrument, despite its granularity, is easy to use and reliable. Furthermore, first proof-of-concept observations lead to patterns of regulation activities that are clearly distinguishable, thus suggesting construct validity. Discussion. Whereas previous work focused on self-reported data or trace data obtained from learning management systems, our observation instrument adds another facet to assessing regulation strategies. Compared to self-reported data, it trades off the bias of possibly unreliable self-assessment and the limitations of external observations. Compared to analyses based on high-resolution, objective log data, it yields observations on a coarser granularity but is applicable not only to self-regulation but to socially shared regulation and co-regulation as well. Our observation instrument thus complements the current methodological portfolio of assessing regulation strategies in Computer Science and related contexts. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1504217 |
| Database: | ERIC |
| FullText | Text: Availability: 0 |
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| Header | DbId: eric DbLabel: ERIC An: EJ1504217 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Instrument for Observing Teams to Explicate Regulation Strategies in Computer Science – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Carolin+Wortmann%22">Carolin Wortmann</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0006-6956-3372">0009-0006-6956-3372</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jan+Vahrenhold%22">Jan Vahrenhold</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8708-4814">0000-0001-8708-4814</externalLink>) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22ACM+Transactions+on+Computing+Education%22"><i>ACM Transactions on Computing Education</i></searchLink>. 2026 26(2). – Name: Avail Label: Availability Group: Avail Data: Association for Computing Machinery. 1601 Broadway 10th Floor, New York, NY 10119. Tel: 800-342-6626; Tel: 212-626-0500; Fax: 212-944-1318; e-mail: acmhelp@acm.org; Web site: http://toce.acm.org/ – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 29 – Name: DatePubCY Label: Publication Date Group: Date Data: 2026 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Reports - Research – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+Science+Education%22">Computer Science Education</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Software%22">Computer Software</searchLink><br /><searchLink fieldCode="DE" term="%22Cooperative+Learning%22">Cooperative Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Active+Learning%22">Active Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Laboratories%22">Laboratories</searchLink><br /><searchLink fieldCode="DE" term="%22Capstone+Experiences%22">Capstone Experiences</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Strategies%22">Learning Strategies</searchLink><br /><searchLink fieldCode="DE" term="%22Observation%22">Observation</searchLink><br /><searchLink fieldCode="DE" term="%22Measures+%28Individuals%29%22">Measures (Individuals)</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Management%22">Self Management</searchLink><br /><searchLink fieldCode="DE" term="%22Metacognition%22">Metacognition</searchLink> – Name: DOI Label: DOI Group: ID Data: 10.1145/3779308 – Name: ISSN Label: ISSN Group: ISSN Data: 1946-6226 – Name: Abstract Label: Abstract Group: Ab Data: Motivation and Objectives: Informed by a long tradition of studying regulation strategies in general education, recent work in Computing Education Research has highlighted the importance and effects of such strategies in computing-related classrooms as well. Most of this work has focused on self-regulation of individuals or dyads, for example, in pair programming. Little, however, is known about regulation in larger groups which naturally occur in, for example, active-learning classrooms, Software Engineering lab courses, or capstone projects. Moreover, research involving regulation strategies, including, but not limited to computing contexts, predominantly relies on self-reported data. While recent work has advocated using trace data from learning management systems to assess self-regulation strategies more objectively, such systems cannot capture regulation as it occurs in many versions of group work, for example, in group discussions and interactions in a classroom. We thus aim to develop an observation instrument that can help researchers to explicate regulation strategies in Computer Science and, more generally, computing-related group work. Together with self-reported data and--where applicable--trace data, such an instrument would facilitate obtaining a more complete picture of where and how regulation strategies emerge and develop. Methods: Using data from semi-structured interviews with students from two capstone projects, we conducted a deductive qualitative analysis to add concrete descriptors to Miller and Hadwin's framework for regulated learning. We refined the resulting coding scheme into an observation instrument that was field-tested for saturation, ease-of-use, and reliability in a Software Engineering lab course. Results: Our main result is OTTERS, an instrument for observing teams to explicate regulation strategies in computing contexts. Initial field-testing indicates that this instrument, despite its granularity, is easy to use and reliable. Furthermore, first proof-of-concept observations lead to patterns of regulation activities that are clearly distinguishable, thus suggesting construct validity. Discussion. Whereas previous work focused on self-reported data or trace data obtained from learning management systems, our observation instrument adds another facet to assessing regulation strategies. Compared to self-reported data, it trades off the bias of possibly unreliable self-assessment and the limitations of external observations. Compared to analyses based on high-resolution, objective log data, it yields observations on a coarser granularity but is applicable not only to self-regulation but to socially shared regulation and co-regulation as well. Our observation instrument thus complements the current methodological portfolio of assessing regulation strategies in Computer Science and related contexts. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2026 – Name: AN Label: Accession Number Group: ID Data: EJ1504217 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1504217 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1145/3779308 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 29 Subjects: – SubjectFull: Computer Science Education Type: general – SubjectFull: Computer Software Type: general – SubjectFull: Cooperative Learning Type: general – SubjectFull: Active Learning Type: general – SubjectFull: Laboratories Type: general – SubjectFull: Capstone Experiences Type: general – SubjectFull: Learning Strategies Type: general – SubjectFull: Observation Type: general – SubjectFull: Measures (Individuals) Type: general – SubjectFull: Self Management Type: general – SubjectFull: Metacognition Type: general Titles: – TitleFull: An Instrument for Observing Teams to Explicate Regulation Strategies in Computer Science Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Carolin Wortmann – PersonEntity: Name: NameFull: Jan Vahrenhold IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2026 Identifiers: – Type: issn-electronic Value: 1946-6226 Numbering: – Type: volume Value: 26 – Type: issue Value: 2 Titles: – TitleFull: ACM Transactions on Computing Education Type: main |
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