Student and Group Activity Level Assessment in the ELARS Recommender System
This paper presents an original approach to student and group activity level assessment that relies on certainty factors theory. Activity level is used to represent quantity and continuity of student’s contributions in individual and collaborative e‑learning activities (e‑tivities) and is calculated to assist teachers in assessing quantitative aspects of student's achievements. Calculated activity levels are also used to raise awareness and provide recommendations during the learning process. The proposed approach was implemented within the educational recommender system ELARS and validated using data obtained from e‑tivity realized during a blended learning course. The results showed that the proposed approach can be used to estimate activity level in the context of e-tivities realized using Web 2.0 tools as well as to facilitate the assessment of quantitative aspect of students’ participation in e‑tivities.
[1] R. Farzan and P. Brusilovsky, “Encouraging user participation in a course recommender system: An impact on user behavior,” Comput. Human Behav., vol. 27, no. 1, pp. 276–284, Jan. 2011.
[2] P. Anderson, “What is Web 2.0? Ideas, technologies and implications for education,” Technology, vol. 60, no. 1, pp. 1–64, 2007.
[3] M. Holenko Dlab and N. Hoic-Bozic, “An Approach to Adaptivity and Collaboration Support in a Web-Based Learning Environment,” Int. J. Emerg. Technol. Learn., vol. 4, no. 7, pp. 28–30, Dec. 2009.
[4] A. Kobsa, “Generic user modeling systems,” Adapt. Web Methods Strateg. Web Pers., vol. 4321, no. LNCS 4321, pp. 136–154, 2007.
[5] J. Masthoff, “Group Adaptation and Group Modelling,” Intell. Interact. Syst. Knowledge-Based Environ., vol. 173, pp. 157–173, 2008.
[6] “ELARS,” (in Croatian), 2015. (Online). Available: http://elars.uniri.hr/elars. (Accessed: 01-Feb-2017).
[7] N. Hoic-Bozic, M. Holenko Dlab, and V. Mornar, “Recommender System and Web 2.0 Tools to Enhance a Blended Learning Model,” IEEE Trans. Educ., vol. 59, no. 1, pp. 39–44, Feb. 2016.
[8] G. Salmon, E-tivities: the key to active online learning. Psychology Press, 2002.
[9] L. M. Campos, J. M. Fernández-Luna, J. F. Huete, and M. a. Rueda-Morales, “Managing uncertainty in group recommending processes,” User Model. User-adapt. Interact., vol. 19, no. 3, pp. 207–242, Nov. 2008.
[10] H. Beetham, “An approach to learning activity design,” in Rethinking Pedagogy for a Digital Age: Designing for 21st century learning, 2nd ed., H. Beetham and R. Sharpe, Eds. Routledge, 2013, pp. 31–48.
[11] S. Järvelä et al., “Enhancing socially shared regulation in collaborative learning groups: designing for CSCL regulation tools,” Educ. Technol. Res. Dev., vol. 63, no. 1, pp. 125–142, Oct. 2014.
[12] H. Zhao and L. Chen, “How Can Self-regulated Learning Be Supported in E-learning 2.0 Environment: a Comparative Study,” J. Educ. Technol. Dev. Exch., vol. 9, no. 2, pp. 1–20, 2016.
[13] E. Rahimi, J. van den Berg, and W. Veen, “Facilitating student-driven constructing of learning environments using Web 2.0 personal learning environments,” Comput. Educ., vol. 81, pp. 235–246, 2015.
[14] M. M. Woo, S. K. W. Chu, and X. Li, “Peer-feedback and revision process in a wiki mediated collaborative writing,” Educ. Technol. Res. Dev., vol. 61, no. 2, pp. 279–309, Jan. 2013.
[15] H. Chau, J. Barria-Pineda, and P. Brusilovsky, “Content Wizard: Concept-Based Recommender System for Instructors of Programming Courses,” in Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization, 2017, pp. 135–140.
[16] S. Sosnovsky and P. Brusilovsky, “Evaluation of topic-based adaptation and student modeling in QuizGuide,” User Model. User-adapt. Interact., vol. 25, no. 4, pp. 371–424, Oct. 2015.
[17] E. Gaudioso, M. Montero, and F. Hernandez-del-Olmo, “Supporting teachers in adaptive educational systems through predictive models: A proof of concept,” Expert Syst. Appl., vol. 39, no. 1, pp. 621–625, Jan. 2012.
[18] G. Cheng and J. Chau, “Exploring the relationships between learning styles, online participation, learning achievement and course satisfaction: An empirical study of a blended learning course,” Br. J. Educ. Technol., Jan. 2015.
[19] A. Paramythis and J. R. Mühlbacher, “Towards New Approaches in Adaptive Support for Collaborative e-Learning,” in Proceedings of the 11th IASTED International Conference, 2008, vol. 614, no. 95, pp. 95–100.
[20] M. Kim and J. Ryu, “The development and implementation of a web-based formative peer assessment system for enhancing students’ metacognitive awareness and performance in ill-structured tasks,” Educ. Technol. Res. Dev., vol. 61, no. 4, pp. 549–561, Jun. 2013.
[21] M. H. Dlab, “Experiences in Using Educational Recommender System ELARS to Support E-Learning,” in 40th International Convention MIPRO 2017, 2017, pp. 778–783.
[22] N. D. Fleming, “I’m different; not dumb. Modes of presentation (VARK) in the tertiary classroom,” in Research and Development in Higher Education, Proceedings of the Annual Conference of the Higher Education and Research Development Society of Australasi, 1995, pp. 308–313.
[23] P. Brusilovsky and E. Millán, “User Models for Adaptive Hypermedia and Adaptive Educational Systems,” Adapt. Web Methods Strateg. Web Pers., vol. 4321, no. LNCS 4321, pp. 3–53, 2007.
[24] M. Holenko Dlab and N. Hoic-Bozic, “Increasing students’ academic results in e-course using educational recommendation strategy,” in Proceedings of the 17th International Conference on Computer Systems and Technologies 2016 - CompSysTech ’16, 2016, pp. 391–398.
[25] P. Bouvier, K. Sehaba, and É. Lavoué, “A trace-based approach to identifying users’ engagement and qualifying their engaged-behaviours in interactive systems: application to a social game,” User Model. User-adapt. Interact., vol. 24, no. 5, pp. 413–451, Dec. 2014.
[26] D. Heckerman and E. Shortliffe, “From certainty factors to belief networks,” Artif. Intell. Med., vol. 4, no. 1, pp. 35–52, 1992.
[27] M. Negnevitsky, Artificial Intelligence: a Guide to Intelligent Systems, 2nd ed., vol. 52, no. 2. New York: Addison-Wesley, 2005.
[1] R. Farzan and P. Brusilovsky, “Encouraging user participation in a course recommender system: An impact on user behavior,” Comput. Human Behav., vol. 27, no. 1, pp. 276–284, Jan. 2011.
[2] P. Anderson, “What is Web 2.0? Ideas, technologies and implications for education,” Technology, vol. 60, no. 1, pp. 1–64, 2007.
[3] M. Holenko Dlab and N. Hoic-Bozic, “An Approach to Adaptivity and Collaboration Support in a Web-Based Learning Environment,” Int. J. Emerg. Technol. Learn., vol. 4, no. 7, pp. 28–30, Dec. 2009.
[4] A. Kobsa, “Generic user modeling systems,” Adapt. Web Methods Strateg. Web Pers., vol. 4321, no. LNCS 4321, pp. 136–154, 2007.
[5] J. Masthoff, “Group Adaptation and Group Modelling,” Intell. Interact. Syst. Knowledge-Based Environ., vol. 173, pp. 157–173, 2008.
[6] “ELARS,” (in Croatian), 2015. (Online). Available: http://elars.uniri.hr/elars. (Accessed: 01-Feb-2017).
[7] N. Hoic-Bozic, M. Holenko Dlab, and V. Mornar, “Recommender System and Web 2.0 Tools to Enhance a Blended Learning Model,” IEEE Trans. Educ., vol. 59, no. 1, pp. 39–44, Feb. 2016.
[8] G. Salmon, E-tivities: the key to active online learning. Psychology Press, 2002.
[9] L. M. Campos, J. M. Fernández-Luna, J. F. Huete, and M. a. Rueda-Morales, “Managing uncertainty in group recommending processes,” User Model. User-adapt. Interact., vol. 19, no. 3, pp. 207–242, Nov. 2008.
[10] H. Beetham, “An approach to learning activity design,” in Rethinking Pedagogy for a Digital Age: Designing for 21st century learning, 2nd ed., H. Beetham and R. Sharpe, Eds. Routledge, 2013, pp. 31–48.
[11] S. Järvelä et al., “Enhancing socially shared regulation in collaborative learning groups: designing for CSCL regulation tools,” Educ. Technol. Res. Dev., vol. 63, no. 1, pp. 125–142, Oct. 2014.
[12] H. Zhao and L. Chen, “How Can Self-regulated Learning Be Supported in E-learning 2.0 Environment: a Comparative Study,” J. Educ. Technol. Dev. Exch., vol. 9, no. 2, pp. 1–20, 2016.
[13] E. Rahimi, J. van den Berg, and W. Veen, “Facilitating student-driven constructing of learning environments using Web 2.0 personal learning environments,” Comput. Educ., vol. 81, pp. 235–246, 2015.
[14] M. M. Woo, S. K. W. Chu, and X. Li, “Peer-feedback and revision process in a wiki mediated collaborative writing,” Educ. Technol. Res. Dev., vol. 61, no. 2, pp. 279–309, Jan. 2013.
[15] H. Chau, J. Barria-Pineda, and P. Brusilovsky, “Content Wizard: Concept-Based Recommender System for Instructors of Programming Courses,” in Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization, 2017, pp. 135–140.
[16] S. Sosnovsky and P. Brusilovsky, “Evaluation of topic-based adaptation and student modeling in QuizGuide,” User Model. User-adapt. Interact., vol. 25, no. 4, pp. 371–424, Oct. 2015.
[17] E. Gaudioso, M. Montero, and F. Hernandez-del-Olmo, “Supporting teachers in adaptive educational systems through predictive models: A proof of concept,” Expert Syst. Appl., vol. 39, no. 1, pp. 621–625, Jan. 2012.
[18] G. Cheng and J. Chau, “Exploring the relationships between learning styles, online participation, learning achievement and course satisfaction: An empirical study of a blended learning course,” Br. J. Educ. Technol., Jan. 2015.
[19] A. Paramythis and J. R. Mühlbacher, “Towards New Approaches in Adaptive Support for Collaborative e-Learning,” in Proceedings of the 11th IASTED International Conference, 2008, vol. 614, no. 95, pp. 95–100.
[20] M. Kim and J. Ryu, “The development and implementation of a web-based formative peer assessment system for enhancing students’ metacognitive awareness and performance in ill-structured tasks,” Educ. Technol. Res. Dev., vol. 61, no. 4, pp. 549–561, Jun. 2013.
[21] M. H. Dlab, “Experiences in Using Educational Recommender System ELARS to Support E-Learning,” in 40th International Convention MIPRO 2017, 2017, pp. 778–783.
[22] N. D. Fleming, “I’m different; not dumb. Modes of presentation (VARK) in the tertiary classroom,” in Research and Development in Higher Education, Proceedings of the Annual Conference of the Higher Education and Research Development Society of Australasi, 1995, pp. 308–313.
[23] P. Brusilovsky and E. Millán, “User Models for Adaptive Hypermedia and Adaptive Educational Systems,” Adapt. Web Methods Strateg. Web Pers., vol. 4321, no. LNCS 4321, pp. 3–53, 2007.
[24] M. Holenko Dlab and N. Hoic-Bozic, “Increasing students’ academic results in e-course using educational recommendation strategy,” in Proceedings of the 17th International Conference on Computer Systems and Technologies 2016 - CompSysTech ’16, 2016, pp. 391–398.
[25] P. Bouvier, K. Sehaba, and É. Lavoué, “A trace-based approach to identifying users’ engagement and qualifying their engaged-behaviours in interactive systems: application to a social game,” User Model. User-adapt. Interact., vol. 24, no. 5, pp. 413–451, Dec. 2014.
[26] D. Heckerman and E. Shortliffe, “From certainty factors to belief networks,” Artif. Intell. Med., vol. 4, no. 1, pp. 35–52, 1992.
[27] M. Negnevitsky, Artificial Intelligence: a Guide to Intelligent Systems, 2nd ed., vol. 52, no. 2. New York: Addison-Wesley, 2005.
@article{"International Journal of Business, Human and Social Sciences:76452", author = "Martina Holenko Dlab and Natasa Hoic-Bozic", title = "Student and Group Activity Level Assessment in the ELARS Recommender System", abstract = "This paper presents an original approach to student and group activity level assessment that relies on certainty factors theory. Activity level is used to represent quantity and continuity of student’s contributions in individual and collaborative e‑learning activities (e‑tivities) and is calculated to assist teachers in assessing quantitative aspects of student's achievements. Calculated activity levels are also used to raise awareness and provide recommendations during the learning process. The proposed approach was implemented within the educational recommender system ELARS and validated using data obtained from e‑tivity realized during a blended learning course. The results showed that the proposed approach can be used to estimate activity level in the context of e-tivities realized using Web 2.0 tools as well as to facilitate the assessment of quantitative aspect of students’ participation in e‑tivities.
", keywords = "Assessment, ELARS, e-learning, recommender systems, student model. ", volume = "11", number = "10", pages = "2436-8", }
