View in EDS PDF Full Text

Using learning analytics in the Amazonas: understanding students' behaviour in introductory programming.

Saved in:
Bibliographic Details
Title: Using learning analytics in the Amazonas: understanding students' behaviour in introductory programming.
Authors: Pereira, Filipe D. filipedwan@gmail.com, Oliveira, Elaine H. T., Oliveira, David B. F., Cristea, Alexandra I., Carvalho, Leandro S. G., Fonseca, Samuel C., Toda, Armando, Isotani, Seiji
Source: British Journal of Educational Technology. Jul2020, Vol. 51 Issue 4, p955-972. 18p. 3 Color Photographs, 1 Black and White Photograph, 2 Diagrams, 3 Charts, 2 Graphs.
Subject Terms: *Mathematical models of learning, *Computer science education, *Computer programming, *Educational technology, *Psychology of students, *Teenagers, *Higher education
Geographic Terms: Brazil
Abstract: Tools for automatic grading programming assignments, also known as Online Judges, have been widely used to support computer science (CS) courses. Nevertheless, few studies have used these tools to acquire and analyse interaction data to better understand the students' performance and behaviours, often due to data availability or inadequate granularity. To address this problem, we propose an Online Judge called CodeBench, which allows for fine‐grained data collection of student interactions, at the level of, eg, keystrokes, number of submissions, and grades. We deployed CodeBench for 3 years (2016–18) and collected data from 2058 students from 16 introductory computer science (CS1) courses, on which we have carried out fine‐grained learning analytics, towards early detection of effective/ineffective behaviours regarding learning CS concepts. Results extract clear behavioural classes of CS1 students, significantly differentiated both semantically and statistically, enabling us to better explain how student behaviours during programming have influenced learning outcomes. Finally, we also identify behaviours that can guide novice students to improve their learning performance, which can be used for interventions. We believe this work is a step forward towards enhancing Online Judges and helping teachers and students improve their CS1 teaching/learning practices. [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
FullText Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: ehh
DbLabel: Education Research Complete
An: 143852232
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Using learning analytics in the Amazonas: understanding students' behaviour in introductory programming.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Pereira%2C+Filipe+D%2E%22">Pereira, Filipe D.</searchLink><i> filipedwan@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Oliveira%2C+Elaine+H%2E+T%2E%22">Oliveira, Elaine H. T.</searchLink><br /><searchLink fieldCode="AR" term="%22Oliveira%2C+David+B%2E+F%2E%22">Oliveira, David B. F.</searchLink><br /><searchLink fieldCode="AR" term="%22Cristea%2C+Alexandra+I%2E%22">Cristea, Alexandra I.</searchLink><br /><searchLink fieldCode="AR" term="%22Carvalho%2C+Leandro+S%2E+G%2E%22">Carvalho, Leandro S. G.</searchLink><br /><searchLink fieldCode="AR" term="%22Fonseca%2C+Samuel+C%2E%22">Fonseca, Samuel C.</searchLink><br /><searchLink fieldCode="AR" term="%22Toda%2C+Armando%22">Toda, Armando</searchLink><br /><searchLink fieldCode="AR" term="%22Isotani%2C+Seiji%22">Isotani, Seiji</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22British+Journal+of+Educational+Technology%22">British Journal of Educational Technology</searchLink>. Jul2020, Vol. 51 Issue 4, p955-972. 18p. 3 Color Photographs, 1 Black and White Photograph, 2 Diagrams, 3 Charts, 2 Graphs.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Mathematical+models+of+learning%22">Mathematical models of learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Computer+science+education%22">Computer science education</searchLink><br />*<searchLink fieldCode="DE" term="%22Computer+programming%22">Computer programming</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Psychology+of+students%22">Psychology of students</searchLink><br />*<searchLink fieldCode="DE" term="%22Teenagers%22">Teenagers</searchLink><br />*<searchLink fieldCode="DE" term="%22Higher+education%22">Higher education</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Brazil%22">Brazil</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Tools for automatic grading programming assignments, also known as Online Judges, have been widely used to support computer science (CS) courses. Nevertheless, few studies have used these tools to acquire and analyse interaction data to better understand the students' performance and behaviours, often due to data availability or inadequate granularity. To address this problem, we propose an Online Judge called CodeBench, which allows for fine‐grained data collection of student interactions, at the level of, eg, keystrokes, number of submissions, and grades. We deployed CodeBench for 3 years (2016–18) and collected data from 2058 students from 16 introductory computer science (CS1) courses, on which we have carried out fine‐grained learning analytics, towards early detection of effective/ineffective behaviours regarding learning CS concepts. Results extract clear behavioural classes of CS1 students, significantly differentiated both semantically and statistically, enabling us to better explain how student behaviours during programming have influenced learning outcomes. Finally, we also identify behaviours that can guide novice students to improve their learning performance, which can be used for interventions. We believe this work is a step forward towards enhancing Online Judges and helping teachers and students improve their CS1 teaching/learning practices. [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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=143852232
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/bjet.12953
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 955
    Subjects:
      – SubjectFull: Mathematical models of learning
        Type: general
      – SubjectFull: Computer science education
        Type: general
      – SubjectFull: Computer programming
        Type: general
      – SubjectFull: Educational technology
        Type: general
      – SubjectFull: Psychology of students
        Type: general
      – SubjectFull: Teenagers
        Type: general
      – SubjectFull: Higher education
        Type: general
      – SubjectFull: Brazil
        Type: general
    Titles:
      – TitleFull: Using learning analytics in the Amazonas: understanding students' behaviour in introductory programming.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Pereira, Filipe D.
      – PersonEntity:
          Name:
            NameFull: Oliveira, Elaine H. T.
      – PersonEntity:
          Name:
            NameFull: Oliveira, David B. F.
      – PersonEntity:
          Name:
            NameFull: Cristea, Alexandra I.
      – PersonEntity:
          Name:
            NameFull: Carvalho, Leandro S. G.
      – PersonEntity:
          Name:
            NameFull: Fonseca, Samuel C.
      – PersonEntity:
          Name:
            NameFull: Toda, Armando
      – PersonEntity:
          Name:
            NameFull: Isotani, Seiji
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Text: Jul2020
              Type: published
              Y: 2020
          Identifiers:
            – Type: issn-print
              Value: 00071013
          Numbering:
            – Type: volume
              Value: 51
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: British Journal of Educational Technology
              Type: main
ResultId 1