A control system proposal for engineering education

The goal of the work is to improve the teaching-learning process through the inclusion of prediction features in a control system proposal namely Reactive Blended Learning. To achieve this goal, a model of the student has been proposed, whose considered outputs are the performance and a participation index that measures the activity level of the student in the class. The controller is based on fuzzy logic and uses the predictions of the model to anticipate the student's state. An important issue that has been taken into account is the limited time to identify the dynamics of the student learning before the course ends. This limitation has been treated through a three-stage process. It is important to remark that this work is not focused on obtaining a complete student model, but on getting useful information for the detection of trends in the teaching-learning process. Preliminary results on a real course are presented to attest the efficiency of the proposed control strategy.

[1]  P. Pintrich A Manual for the Use of the Motivated Strategies for Learning Questionnaire (MSLQ). , 1991 .

[2]  Xiao-Li Hu,et al.  New Convergence Results for the Least Squares Identification Algorithm , 2008 .

[3]  M. Lowis,et al.  Factors affecting student progression and achievement: prediction and intervention. A two‐year study , 2008 .

[4]  Dimitrios Kalles,et al.  ANALYZING STUDENT PERFORMANCE IN DISTANCE LEARNING WITH GENETIC ALGORITHMS AND DECISION TREES , 2006, Appl. Artif. Intell..

[5]  Convergence conditions of the least squares method , 1994 .

[6]  Ioanna Lykourentzou,et al.  Early and dynamic student achievement prediction in e-learning courses using neural networks , 2009 .

[7]  Victor C. S. Lee,et al.  Learning motivation in e-learning facilitated computer programming courses , 2010, Comput. Educ..

[8]  Michalis Nik Xenos Prediction and assessment of student behaviour in open and distance education in computers using Bayesian networks , 2004, Comput. Educ..

[9]  José-Luis Pérez-de-la-Cruz,et al.  Bayesian networks for student model engineering , 2010, Comput. Educ..

[10]  Antoni Niederliński,et al.  Convergence of least-squares dynamic system identification with finite-accuracy data , 1984 .

[11]  R. Vallerand,et al.  On the Assessment of Situational Intrinsic and Extrinsic Motivation: The Situational Motivation Scale (SIMS) , 2000 .

[12]  Lilly Suriani Affendey,et al.  Improving Academic Performance Prediction using Voting Technique in Data Mining , 2010 .

[13]  Teknik Informatika,et al.  PREDICTION OF STUDENT ACADEMIC PERFORMANCE BY AN APPLICATION OF DATA MINING TECHNIQUES , 2011 .

[14]  Juan A. Méndez,et al.  Implementing Motivational Features in Reactive Blended Learning: Application to an Introductory Control Engineering Course , 2011, IEEE Transactions on Education.

[15]  Diane M. Christophel The relationships among teacher immediacy behaviors, student motivation, and learning , 1990 .

[16]  Karel J. Keesman,et al.  System Identification: An Introduction , 2011 .

[17]  J. T. Gillis,et al.  Conditions for the equivalence of ARMAX and ARX systems , 1993, IEEE Trans. Autom. Control..

[18]  R. Felder,et al.  Learning and Teaching Styles in Engineering Education. , 1988 .

[19]  Al Cripps,et al.  Using artificial neural nets to predict academic performance , 1996, SAC '96.

[20]  M. Angélica Pinninghoff Junemann,et al.  Neural Networks to Predict Schooling Failure/Success , 2007, IWINAC.

[21]  Andrew J. Martin The Student Motivation Scale: A Tool for Measuring and Enhancing Motivation , 2001, Journal of Psychologists and Counsellors in Schools.

[22]  Karl Johan Åström,et al.  Computer-Controlled Systems: Theory and Design , 1984 .

[23]  Juan A. Méndez,et al.  A reactive blended learning proposal for an introductory control engineering course , 2010, Comput. Educ..

[24]  E. L. Burgess,et al.  Application of the superposition principle to solar-cell analysis , 1979, IEEE Transactions on Electron Devices.

[25]  Analía Amandi,et al.  Evaluating Bayesian networks' precision for detecting students' learning styles , 2007, Comput. Educ..

[26]  Lars Schmidt-Thieme,et al.  Matrix and Tensor Factorization for Predicting Student Performance , 2011, CSEDU.

[27]  C. Weinstein,et al.  Learning and Study Strategies Inventory (LASSI) , 1987 .

[28]  Junbin Gao,et al.  Prediction of student actions using weighted Markov models , 2008, 2008 IEEE International Symposium on IT in Medicine and Education.

[29]  Martha W. Evens,et al.  A practical student model in an intelligent tutoring system , 1999, Proceedings 11th International Conference on Tools with Artificial Intelligence.