Mapping from proximity traces to socio‐spatial behaviours and student progression at the school.
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| Title: | Mapping from proximity traces to socio‐spatial behaviours and student progression at the school. |
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| Authors: | Yan, Lixiang1 lixiang.yan@monash.edu, Martinez‐Maldonado, Roberto1, Gallo Cordoba, Beatriz2, Deppeler, Joanne2, Corrigan, Deborah2, Gašević, Dragan1 |
| Source: | British Journal of Educational Technology. Nov2022, Vol. 53 Issue 6, p1645-1664. 20p. 2 Color Photographs, 6 Charts, 2 Graphs. |
| Subject Terms: | *Educational technology, *Classroom design & construction, *Psychology of students, *Interpersonal relations, *Academic achievement, *Machine learning, *School children, *Primary education |
| Abstract: | Identifying students facing difficulties and providing them with timely support is one of the educator's key responsibilities. Yet, this task is becoming increasingly challenging as the complexity of physical learning spaces grows, along with the emergence of novel educational technologies and classroom designs. There has been substantial research and development work focused on identifying student social behaviours in digital platforms (eg, the learning management system) as predictors of academic progression. However, little work has investigated such relationships in physical learning spaces. This study explores the potential of using wearable trackers for the early detection of low‐progress students based on their social and spatial (socio‐spatial) behaviours at the school. Positioning data from 98 primary school students and six teachers were automatically captured over a period of eight weeks. Fourteen socio‐spatial behavioural features were extracted and processed using a set of machine learning classifiers to model students' learning progression. Results illustrate the potential of prospectively identifying low‐progress students from these features and the importance of adapting classroom learning analytics to differences in pedagogical designs. Practitioner notesWhat is already known about this topicLearning analytics research on predicting students' academic progression is emerging in both digital and physical learning spaces.Students' social behaviours in learning activities is a key factor in predicting their academic progression.Emerging sensing technologies can provide opportunities to study students' real‐time social behaviours in physical learning spaces.What this paper addsFourteen progression‐related socio‐spatial behavioural features are extracted from students' physical (x‐y) positioning traces.Predictive learning analytics that achieved 81% accuracy in prospectively identifying low‐progress students from their real‐time socio‐spatial behaviours.Empirical evidence to support the need for classroom learning analytics to have instructional sensitivity (ie, be calibrated according to the learning design).Implications for practice and/or policySensing technologies and machine learning algorithms can be used to capture and generate valuable insights about higher‐order learning constructs (eg, performance and collaboration) from students' physical positioning traces in classrooms.Researchers and practitioners should be cautious with generalised classification algorithms and predictive learning analytics that do not account for the pedagogical differences between different subjects or learning designs.Researchers and practitioners should consider the potentially unforeseen ethical issues that can emerge in using sensing technologies and predictive learning analytics in authentic, physical classroom settings. [ABSTRACT FROM AUTHOR] |
| Copyright of British Journal of Educational Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 159504358 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Mapping from proximity traces to socio‐spatial behaviours and student progression at the school. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yan%2C+Lixiang%22">Yan, Lixiang</searchLink><relatesTo>1</relatesTo><i> lixiang.yan@monash.edu</i><br /><searchLink fieldCode="AR" term="%22Martinez‐Maldonado%2C+Roberto%22">Martinez‐Maldonado, Roberto</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Gallo+Cordoba%2C+Beatriz%22">Gallo Cordoba, Beatriz</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Deppeler%2C+Joanne%22">Deppeler, Joanne</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Corrigan%2C+Deborah%22">Corrigan, Deborah</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Gašević%2C+Dragan%22">Gašević, Dragan</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22British+Journal+of+Educational+Technology%22">British Journal of Educational Technology</searchLink>. Nov2022, Vol. 53 Issue 6, p1645-1664. 20p. 2 Color Photographs, 6 Charts, 2 Graphs. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Classroom+design+%26+construction%22">Classroom design & construction</searchLink><br />*<searchLink fieldCode="DE" term="%22Psychology+of+students%22">Psychology of students</searchLink><br />*<searchLink fieldCode="DE" term="%22Interpersonal+relations%22">Interpersonal relations</searchLink><br />*<searchLink fieldCode="DE" term="%22Academic+achievement%22">Academic achievement</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22School+children%22">School children</searchLink><br />*<searchLink fieldCode="DE" term="%22Primary+education%22">Primary education</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Identifying students facing difficulties and providing them with timely support is one of the educator's key responsibilities. Yet, this task is becoming increasingly challenging as the complexity of physical learning spaces grows, along with the emergence of novel educational technologies and classroom designs. There has been substantial research and development work focused on identifying student social behaviours in digital platforms (eg, the learning management system) as predictors of academic progression. However, little work has investigated such relationships in physical learning spaces. This study explores the potential of using wearable trackers for the early detection of low‐progress students based on their social and spatial (socio‐spatial) behaviours at the school. Positioning data from 98 primary school students and six teachers were automatically captured over a period of eight weeks. Fourteen socio‐spatial behavioural features were extracted and processed using a set of machine learning classifiers to model students' learning progression. Results illustrate the potential of prospectively identifying low‐progress students from these features and the importance of adapting classroom learning analytics to differences in pedagogical designs. Practitioner notesWhat is already known about this topicLearning analytics research on predicting students' academic progression is emerging in both digital and physical learning spaces.Students' social behaviours in learning activities is a key factor in predicting their academic progression.Emerging sensing technologies can provide opportunities to study students' real‐time social behaviours in physical learning spaces.What this paper addsFourteen progression‐related socio‐spatial behavioural features are extracted from students' physical (x‐y) positioning traces.Predictive learning analytics that achieved 81% accuracy in prospectively identifying low‐progress students from their real‐time socio‐spatial behaviours.Empirical evidence to support the need for classroom learning analytics to have instructional sensitivity (ie, be calibrated according to the learning design).Implications for practice and/or policySensing technologies and machine learning algorithms can be used to capture and generate valuable insights about higher‐order learning constructs (eg, performance and collaboration) from students' physical positioning traces in classrooms.Researchers and practitioners should be cautious with generalised classification algorithms and predictive learning analytics that do not account for the pedagogical differences between different subjects or learning designs.Researchers and practitioners should consider the potentially unforeseen ethical issues that can emerge in using sensing technologies and predictive learning analytics in authentic, physical classroom settings. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of British Journal of Educational Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/bjet.13203 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 1645 Subjects: – SubjectFull: Educational technology Type: general – SubjectFull: Classroom design & construction Type: general – SubjectFull: Psychology of students Type: general – SubjectFull: Interpersonal relations Type: general – SubjectFull: Academic achievement Type: general – SubjectFull: Machine learning Type: general – SubjectFull: School children Type: general – SubjectFull: Primary education Type: general Titles: – TitleFull: Mapping from proximity traces to socio‐spatial behaviours and student progression at the school. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yan, Lixiang – PersonEntity: Name: NameFull: Martinez‐Maldonado, Roberto – PersonEntity: Name: NameFull: Gallo Cordoba, Beatriz – PersonEntity: Name: NameFull: Deppeler, Joanne – PersonEntity: Name: NameFull: Corrigan, Deborah – PersonEntity: Name: NameFull: Gašević, Dragan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 00071013 Numbering: – Type: volume Value: 53 – Type: issue Value: 6 Titles: – TitleFull: British Journal of Educational Technology Type: main |
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