Collaboration Based Simulation Model for Predicting Students’ Performance in Blended Learning

Due to the positive influence on students’ learning outcomes, the interests of studying effective knowledge management has been risen recently. Developing and implementing effective strategies ensures to promote learning outcomes. By reviewing and examining various influence factors, this research study has predicted the major factors that may influence of learning outcomes in blended learning environment. A series of simulation experiments and factor analyses have been conducted in order to investigate collaboration during group learning process. The simulation model for blended learning environment employed in this research has drawn on the characteristics of the Structural Equation Model (SEM) of the blended learning process of 128 students. Both randomness of those student learning behaviors and the reaction to information overload have been considered during simulation modeling. The simulation model enables for greatly increasing statistical samples of student learning behavior analysis. Besides, this research has studied the impact of multiple factors to blended learning mode, these factors include: the size of learning group, the group composition according to previous performance, teaching material amount, and the teacher influence. Experimental results predict that the factors mentioned above can enhance collaborative interaction among students during writing and reading activity. The research results of the optimization restriction factors for blended learning environment achieved in this study can be useful reference for a teacher who are facing the similar challenges. The results of this paper can also be used to reveal and eliminate the problem of inefficient collaboration and poor student performance in blended learning environment. The model proposed in this paper can be integrated with most of decision support systems of universities.

[1]  Dennis Zielke,et al.  Design and Implementation of a Learning Analytics Toolkit for Teachers , 2012, J. Educ. Technol. Soc..

[2]  Sebastián Ventura,et al.  Predicting students' final performance from participation in on-line discussion forums , 2013, Comput. Educ..

[3]  George Siemens,et al.  Penetrating the fog: analytics in learning and education , 2014 .

[4]  Umar Manzoor,et al.  Modeling and Predicting Students' Academic Performance Using Data Mining Techniques , 2016 .

[5]  D. Garrison,et al.  Blended learning: Uncovering its transformative potential in higher education , 2004, Internet High. Educ..

[6]  Mahnaz Moallem,et al.  An interactive online course: A collaborative design model , 2003 .

[7]  Yong Zhao,et al.  What Makes the Difference? A Practical Analysis of Research on the Effectiveness of Distance Education , 2005, Teachers College Record: The Voice of Scholarship in Education.

[8]  Mingzhu Qiu,et al.  A Mixed Methods Study of Class Size and Group Configuration in Online Graduate Course Discussions , 2010 .

[9]  Sabeur Elkosantini,et al.  Toward a new generic behavior model for human centered system simulation , 2015, Simul. Model. Pract. Theory.

[10]  Barbara Means,et al.  Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies , 2009 .

[11]  Nurzeatul Hamimah Abdul Hamid,et al.  Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms , 2019 .

[12]  B. Means,et al.  The Effectiveness of Online and Blended Learning: A Meta-Analysis of the Empirical Literature , 2013, Teachers College Record: The Voice of Scholarship in Education.

[13]  Jim Hewitt,et al.  The relationship between class size and online activity patterns in asynchronous computer conferencing environments , 2007, Comput. Educ..

[14]  Ton de Jong,et al.  Comparing the effects of representational tools in collaborative and individual inquiry learning , 2011, Int. J. Comput. Support. Collab. Learn..

[15]  Hans-Rüdiger Pfister,et al.  The impact of goal focus, task type and group size on synchronous net-based collaborative learning discourses , 2009, J. Comput. Assist. Learn..

[16]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[17]  A. S. M. Badrudduza,et al.  Educational Performance Analytics of Undergraduate Business Students , 2019, International Journal of Modern Education and Computer Science.

[18]  Ben Daniel,et al.  Big Data and analytics in higher education: Opportunities and challenges , 2015, Br. J. Educ. Technol..

[19]  Denis Gien,et al.  Integration of human behavioural aspects in a dynamic model for a manufacturing system , 2009 .

[20]  George Karypis,et al.  Collaborative multi-regression models for predicting students' performance in course activities , 2015, LAK.

[21]  R. Gillies,et al.  Teachers' and students' verbal behaviours during cooperative and small-group learning. , 2006, The British journal of educational psychology.

[22]  Solomon A. Adepoju,et al.  A Decision Tree Approach for Predicting Students Academic Performance , 2015 .

[23]  Emmanuel Gbenga Dada,et al.  An Investigation into the Effectiveness of Asynchronous and Synchronous E-learning Mode on Students’ Academic Performance in National Open University (NOUN), Maiduguri Centre , 2019, International Journal of Modern Education and Computer Science.

[24]  Farshid Marbouti,et al.  Models for early prediction of at-risk students in a course using standards-based grading , 2016, Comput. Educ..

[25]  Sotiris B. Kotsiantis Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades , 2011, Artificial Intelligence Review.

[26]  Ali Farhan AbuSeileek,et al.  The effect of computer-assisted cooperative learning methods and group size on the EFL learners' achievement in communication skills , 2012, Comput. Educ..

[27]  Margaret Mazzolini,et al.  Sage, guide or ghost? The effect of instructor intervention on student participation in online discussion forums , 2003, Comput. Educ..

[28]  Lin Lu,et al.  Detecting leadership in peer-moderated online collaborative learning through text mining and social network analysis , 2018, Internet High. Educ..