research-article

Assessing students' performance using the learning analytics enriched rubrics

Authors:

Ioannis Dimopoulos,

Ourania Petropoulou,

Symeon RetalisAuthors Info & Claims

LAK '13: Proceedings of the Third International Conference on Learning Analytics and Knowledge

Pages 195 - 199

https://doi.org/10.1145/2460296.2460335

Published: 08 April 2013 Publication History

Abstract

The assessment of students' performance in e-learning environments is a challenging and demanding task for the teachers. Focusing on this challenge, a new assessment tool, called Learning Analytics Enriched Rubric (LAe-R) is presented in this paper. LAe-R is based on the concept of assessment rubrics which is a very popular assessment technique in education. LAe-R contains "enriched" criteria and grading levels that are associated to data extracted from the analysis of learners' interaction and learning behavior in an e-learning environment. LAe-R has been developed as a plug-in for the Moodle learning management system. Via an example, we will show how LAe-R can be used by teachers and students.

References

[1]

Lazakidou, G., & Retalis, S. (2010). Using computer supported collaborative learning strategies for helping students acquire self-regulated problem-solving skills in mathematics. Computers & Education 54(1), 3--13.

Digital Library

[2]

Moore, G. (1989). Three types of interaction. The American Journal of Distance Education, 3(2), 1--6.

[3]

Dillenbourg, P., Järvelä, S., & Fischer, F. (2009). The evolution of research on computer-supported collaborative learning. Technology-Enhanced Learning, 3--19. Springer.

[4]

Bliuc, A.-M., Ellis, R. A., & Goodyear, P. (2011). The role of social identification as university student in learning: relationships between students' social identity, approaches to learning, and academic achievement. Educational Psychology, 31(5), 559--574.

[5]

Quellmalz, E., & Kozma, R. (2003). Designing assessments of learning with technology. Assessment in Education, 10(3), 389--406.

[6]

Petropoulou, O., Lazakidou, G., Retalis, S., & Vrasidas, C. (2007). Analysing interaction behaviour in network supported collaborative learning environments: a holistic approach. International Journal of Knowledge and Learning, 3(4/5), 450--464.

[7]

Johnson, R., Penny, J., & Gordon, B. (2009). Assessing performance: designing, scoring, and validating performance tasks. Guilford Press.

[8]

Mazza, R., & Dimitrova, V. (2007). CourseVis: A Graphical Student Monitoring Tool for Facilitating Instructors in Web-Based Distance Courses. International Journal in Human-Computer Studies, 65(2), 125--139.

Digital Library

[9]

Yang, D., Richardson, J., French, B., & Lehman, J. (2011). The development of a content analysis model for assessing students' cognitive learning in asynchronous online discussions. Educational Technology Research & Development, 59(1), 43--70.

[10]

García-Saiz, D., & Zorilla Pantaleóón, M. E. (2011). E-learning web miner: A data mining application to help instructors Involved in virtual courses. In M. Pechenizkiy et al. (Eds.), Proceedings of the 3rd Conference on Educational Data Mining 2011 (323--324). Eindhoven, The Netherlands.

[11]

Romero, C. (2010). Educational Data Mining: A Review of the State of the Art. IEEE Transactions on Systems. Man, and Cybernetics, 40(6), 601--618.

Digital Library

[12]

Strijbos, J. W. (2011). Assessment of (computer-supported) collaborative learning. IEEE Transactions on Learning Technologies, 4(1), 59--73.

Digital Library

[13]

Dyckhoff, A. L., Zielke, D., Bültmann, M., Chatti, M. A., & Schroeder, U. (2012). Design and Implementation of a Learning Analytics Toolkit for Teachers. Educational Technology & Society, 15(3), 58--76.

[14]

Carr, J. F., &. Harris, D. E. (2001). Succeeding with standards linking curriculum, assessment, and action planning. Alexandria, Virginia: Association for Supervision and Curriculum Development.

[15]

Oberg, C. (2009). Guiding classroom instruction through performance assessment. Online Journal of Case Studies in Accreditation and Assessment, 1, 1--11, ISSN: 1941-3386. Retrieved from: http://www.aabri.com/manuscripts/09257.pdf

[16]

Daradoumis T., Martínez-Monés A., & Xhafa F. (2006). A layered framework for evaluatingon-line collaborative learning interactions. International Journal of Human-Computer Studies, 64(7), 622--635.

Digital Library

[17]

Barros, B., & Verdejo, M. F. (1999). An approach to analyse collaboration when shared structured workspaces are used for carrying out group learning processes. In S. P. Lajoie & M. Vivet (Eds.), Artificial intelligence in education: Open learning environments, 449--456. Amsterdam: IOS Press.

[18]

Martinez, A., Dimitriadis, Y., De La Fuente, P. (2003). Contributions to analysis of interactions for formative evaluation in CSCL. In: Llamas M, Fernandez M J, Anido L E (eds.) Computers and education: towards of lifelong learning society, 227--238. Kluwer, The Netherlands.

[19]

Ho, C., & Swan, K. (2007). Evaluating online conversation in an asynchronous learning environment: An application of Grice's cooperative principle. Internet and Higher Education, 10, 3--14.

[20]

Petropoulou, O., Vasilikopoulou, M., & Retalis, S. (2009). Enriched Assessment Rubrics: A new medium for enabling teachers easily assess students' performance when participating to complex interactive learning scenarios. Operational Research International Journal, 11(2), 171--186.

[21]

Petropoulou, O., Retalis, S., & Lazakidou, G. (2012). Measuring Students' Performance in e-Learning Environments via Enriched Assessment Rubrics. In Psaromiligkos, Spyridakos, Retalis(eds): Evaluation in e-Learning. Nova Science Publishers, ISBN: 978-1-61942-942-0.

[22]

Mazza, R., & Botturi, L. (2007) Monitoring an Online Course with the GISMO Tool: A Case Study. Journal of Interactive Learning Research, 18(2), 251--265. Chesapeake, VA: AACE.

[23]

Saltz J., Hiltz S., Turoff M., & Passerini K. (2007). Increasing participation in distance learning courses. IEEE Internet Computing, 11(3), 36--44.

Digital Library

[24]

Zhang, H., Almeroth, K., Knight, A., Bulger, M., & Mayer, R. (2007). Moodog: Tracking students' Online Learning Activities. In C. Montgomerie & J. Seale (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications, 4415--4422. Chesapeake, VA: AACE.

[25]

Mazza R., Bettoni M., Fare M., Mazzola L. (2012). MOCLog -- Monitoring Online Courses with log data. In Retalis, S., and Dougiamas, M. (2012). 1st Moodle Research Conference Proceedings. Online: http://research.moodle.net/MoodleCon_Proceedings_program

[26]

Sharkey, M. (2011) Academic analytics landscape at the University of Phoenix. In Proceedings of the 1 ^st International Conference on Learning Analytics and Knowledge (LAK '11). ACM, New York, NY, USA, 122--126.

Digital Library

[27]

Petropoulou, O., Altanis, I., Retalis, S., Nicolaou, C. A., Kannas, C., Vasiliadou, M., & Pattis, I. (2010). Building a tool to help teachers analyse learners' interactions in a networked learning environment. Educational Media International, 47(3), 231--246.

[28]

Gobert, J., Sao Pedro, M., Baker, R., Toto, E., & Montalvo, O. (2012). Leveraging Educational Data Mining for Real-time Performance Assessment of Scientific Inquiry Skills within Microworlds. Journal of Educational Data Mining, Article 5, 4 (1), 153--185.

Cited By

da Silva LDias LRigo SBarbosa JLeithardt DLeithardt V(2021)A Literature Review on Intelligent Services Applied to Distance LearningEducation Sciences10.3390/educsci1111066611:11(666)Online publication date: 21-Oct-2021
https://doi.org/10.3390/educsci11110666
Yilmaz YLal STong XHoward MBal SBayer IMonteiro SChan T(2020)Technology-Enhanced Faculty Development: Future Trends and Possibilities for Health Sciences EducationMedical Science Educator10.1007/s40670-020-01100-1Online publication date: 14-Oct-2020
https://doi.org/10.1007/s40670-020-01100-1
Zotou MTambouris ETarabanis K(2020)Data-driven problem based learning: enhancing problem based learning with learning analyticsEducational Technology Research and Development10.1007/s11423-020-09828-8Online publication date: 21-Sep-2020
https://doi.org/10.1007/s11423-020-09828-8
Show More Cited By

Index Terms

Assessing students' performance using the learning analytics enriched rubrics
1. Applied computing
  1. Education
  2. Enterprise computing

Recommendations

Supporting competency-assessment through a learning analytics approach using enriched rubrics
TEEM '14: Proceedings of the Second International Conference on Technological Ecosystems for Enhancing Multiculturality

Universities have increasingly emphasized competencies as central elements of students' development. However, the assessment of these competencies is not an easy task. The availability of data that learners generate in computer mediated learning offers ...
Effects of Learning Analytics on Students' Self-Regulated Learning in Flipped Classroom

The present article is aimed at analyzing the effects of learning analytics on students' self-regulated learning in a flipped classroom. An experiment was conducted with 96 engineering students, enrolled in a subject offered in the Flipped Classroom ...
Using Learning Analytics to Promote Student Engagement and Achievement in Blended Learning: An Empirical Study
ICEBT '18: Proceedings of the 2018 2nd International Conference on E-Education, E-Business and E-Technology

The emergence of blended learning has huge impact on traditional learning. Blended learning has its own unique characteristics combining the advantages of traditional learning and online learning. However, some problems of blended learning have also ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

LAK '13: Proceedings of the Third International Conference on Learning Analytics and Knowledge

April 2013

300 pages

ISBN:9781450317856

DOI:10.1145/2460296

Editors:
Dan Suthers
University of Hawaii, USA
,
Katrien Verbert
Eindhoven University of Technology, The Netherlands
,
Erik Duval
University of Leuven, Belgium
,
Xavier Ochoa
Escuela Superior Politécnica del Litoral, Ecuador

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
K.U. Leuven: K.U. Leuven
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 April 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Seventh Framework Programme

Conference

LAK '13

Sponsor:

SIGWEB
K.U. Leuven
SIGCHI

LAK '13: Third Conference on Learning Analytics and Knowledge

April 8 - 13, 2013

Leuven, Belgium

Acceptance Rates

LAK '13 Paper Acceptance Rate 16 of 58 submissions, 28%;

Overall Acceptance Rate 236 of 782 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
882
Total Downloads

Downloads (Last 12 months)22
Downloads (Last 6 weeks)1

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

da Silva LDias LRigo SBarbosa JLeithardt DLeithardt V(2021)A Literature Review on Intelligent Services Applied to Distance LearningEducation Sciences10.3390/educsci1111066611:11(666)Online publication date: 21-Oct-2021
https://doi.org/10.3390/educsci11110666
Yilmaz YLal STong XHoward MBal SBayer IMonteiro SChan T(2020)Technology-Enhanced Faculty Development: Future Trends and Possibilities for Health Sciences EducationMedical Science Educator10.1007/s40670-020-01100-1Online publication date: 14-Oct-2020
https://doi.org/10.1007/s40670-020-01100-1
Zotou MTambouris ETarabanis K(2020)Data-driven problem based learning: enhancing problem based learning with learning analyticsEducational Technology Research and Development10.1007/s11423-020-09828-8Online publication date: 21-Sep-2020
https://doi.org/10.1007/s11423-020-09828-8
Drlik MMunk M(2019)Understanding Time-Based Trends in Stakeholders’ Choice of Learning Activity Type Using Predictive ModelsIEEE Access10.1109/ACCESS.2018.28870577(3106-3121)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2018.2887057
Munk MDrlik MBenko LReichel J(2017)Quantitative and Qualitative Evaluation of Sequence Patterns Found by Application of Different Educational Data Preprocessing TechniquesIEEE Access10.1109/ACCESS.2017.27063025(8989-9004)Online publication date: 2017
https://doi.org/10.1109/ACCESS.2017.2706302
Gañán DCaballé SClarisó RConesa JBañeres D(2017)ICT-FLAG: a web-based e-assessment platform featuring learning analytics and gamificationInternational Journal of Web Information Systems10.1108/IJWIS-12-2016-007413:1(25-54)Online publication date: 18-Apr-2017
https://doi.org/10.1108/IJWIS-12-2016-0074
Eugene KStringfellow CHalverson R(2016)The usefulness of rubrics in computer scienceJournal of Computing Sciences in Colleges10.5555/2904127.290412931:4(5-20)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.5555/2904127.2904129
Munk MDrlík M(2016)Methodology of Predictive Modeling of Students’ Behavior in Virtual Learning EnvironmentFormative Assessment, Learning Data Analytics and Gamification10.1016/B978-0-12-803637-2.00010-5(187-216)Online publication date: 2016
https://doi.org/10.1016/B978-0-12-803637-2.00010-5
Sergis SSampson D(2016)School Analytics: A Framework for Supporting School Complexity LeadershipCompetencies in Teaching, Learning and Educational Leadership in the Digital Age10.1007/978-3-319-30295-9_6(79-122)Online publication date: 22-Jul-2016
https://doi.org/10.1007/978-3-319-30295-9_6
Tzelepi M(2014)Personalizing Learning Analytics to Support Collaborative Learning Design and Community BuildingProceedings of the 2014 IEEE 14th International Conference on Advanced Learning Technologies10.1109/ICALT.2014.226(771-773)Online publication date: 7-Jul-2014
https://dl.acm.org/doi/10.1109/ICALT.2014.226
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten