Abstract
This paper addresses the issue of designing a learning-based analytics indicator system using domain-specific modeling languages. The proposed system incorporates a performance management component for monitoring and enhancing the learning process for computer systems engineering students. Using the proposed system, the instructors and learning policymakers will define indicators in a qualitative and/or quantitative manner, and the system will automatically compute the values of these indicators recommending a set of actions to assist the stakeholders of the learning-teaching process. Accordingly, they will be able to make appropriate decisions to amend and update the learning resources and processes. Additionally, the system will classify and cluster the learners according to their learning levels and interests using different data mining techniques. Another important component is the acquisition of learning sources from heterogeneous data and information sources available on the Web. In this context, unlike traditional approaches that rely on a single data source for constructing the learning sources, we will exploit multiple Web-based data learning sources.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baker, R.S., Inventado, P.S.: Educational data mining and learning analytics. In: Learning Analytics, pp. 61–75. Springer, New York (2014)
Baker, R., Yacef, K.: The state of educational data mining in 2009: a review and future visions (2009)
Bao, J., Kendall, E., McGuinness, D., Patel-Schneider, P. (eds.): OWL 2 Web Ontology Language: Quick Reference Guide. W3C Recommendation, 27 Oct 2009
Bienkowski, M., Feng, M., Means, B.: Enhancing teaching and learning through educational data mining and learning analytics: an issue brief, Washington, D.C., 2012, p. 45 (2012)
Bouras, A., Gouvas, P., Mentzas, G.: ENIO.: an enterprise application integration ontology. In: 18th International Workshop on Database and Expert Systems Applications (2007)
Chatti, M.A., Dyckhoff, A.L., Schroeder, U., Thüs, H.: A reference model for learning analytics Int. J. Technol. Enhanced Learn. 4(5), 318–331 (2012)
Fellbaum, C.: WordNet: An Electronic Lexical Database. Bradford Books (1998)
Cui, W., Wu, H.: Using ontology to achieve the semantic integration and interoperation of GIS. In: Geoscience and Remote Sensing Symposium, IGARSS ’05 (2005)
Dyckhoff, A.L., Zielke, D., Bültmann, M., Chatti, M.A., Schroeder, U.: Design and implementation of a learning analytics toolkit for teachers. Educ. Technol. Soc 15(3), 58–76 (2012)
Deaton, C., Shepard, B., Klein, C., Mayans, C., Summers, B., Brusseau, A., Witbrock, M.: The comprehensive terrorism knowledge base in cyc. In: Proceedings of the 2005 International Conference on Intelligence Analysis (2005)
Drachsler, H., Bogers, T., Vuorikari, R., Verbert, K., Duval, E., Manouselis, N., Wolpers, M.: Issues and considerations regarding sharable data sets for recommender systems in technology enhanced learning. Elsevier Procedia Comput. Sci. 1(2), 2849–2858 (2010)
Elias, T.: Learning Analytics: Definitions, Processes and Potential (2011)
Etzioni, O., Banko, M., Soderland, S., Weld, S.D.: Open information extraction from the web. Commun. ACM 51(12), 68–74 (2008)
Etzioni, O., Cafarella, M., Downey, D., Popescu, A.-M., Shaked, T., Soderland, S., Weld, D.S., Yates, A.: Unsupervised named-entity extraction from the Web: an experimental study. Artif. Intell. 165(1), 91–134 (2005)
Fensel, D.: Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce. Springer, Berlin (2004)
Frank, U., Heise, D., Kattenstroth, H.: Use of a domain specific modeling language for realizing versatile dashboards (2009)
Gagnon, M.: Ontology-based integration of data sources. In: 2007 10th International Conference On Information Fusion, pp. 1–8 (2007)
Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquisition 5(2), 199–220 (1993)
Halpin, T.: Information Modeling and Relational Databases. Morgan-Kaufmann (2001)
Jurisica, I., Mylopoulos, J., Yu, E.: Using ontologies for knowledge management: an information systems perspective. In: Proceedings of the 62nd Annual Meeting of the American Society for Information Science (ASIS ’99), pp 482–496 (1999)
Siemens, G.: Learning analytics: envisioning a research discipline and a domain of practice. In: LAK12: 2nd International Conference on Learning Analytics & Knowledge (2012)
Veal, D., Hartman, N., Springer, J.: Implementing ontology-based information sharing in product lifecycle management. In: Proceedings of 65th Midyear Conference Engineering Design Graphics Division of ASEE, Houghton, Michigan (2010)
Zhai, J., Li, J., Wang, O.: Using ontology and XML for semantic integration of electricity information systems. In: Electric Utility Deregulation and Restructuring and Power Technologies, DRPT 2008. Third International Conference (2008)
Zhao, S., Betz, J.: Corroborate and learn facts from the web. In KDD ’07: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge discovery and data mining, pp. 995–1003. ACM (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hodrob, R., Ewais, A., Maree, M. (2020). On Developing a Framework for Knowledge-Based Learning Indicator System in the Context of Learning Analytics. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2019. Smart Innovation, Systems and Technologies, vol 142. Springer, Singapore. https://doi.org/10.1007/978-981-13-8311-3_3
Download citation
DOI: https://doi.org/10.1007/978-981-13-8311-3_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8310-6
Online ISBN: 978-981-13-8311-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)