Modern Techniques on Learning Strategies Supported by Data Mining Analysis
Modern Techniques on Learning Strategies Supported by Data Mining Analysis

Summary/Abstract: An attractive learning environment represents one of the key requirements for the students in order to accomplish their educational objectives. Not only the physical location and context influence their capacity to achieve knowledge and to use it creatively, but also the culture in which they learn. Switching from one learning environment to another proves to be a serious challenge for young students like those graduating the high-school and acceding to the university. The two learning environments expose different schedules, requirements, teaching methods, types of interaction (teacher - student and student - student), ways of life (for people who change their social environment), evaluation criteria and consequences of not passing the examinations. Furthermore, within the framework of an undergraduate environment, teaching and learning disciplines like "Computer Programming" or "Introduction to Operating Systems" become even more challenging due to the significant differences in prior knowledge achieved by the students. In order to overcome this issue, teaching strategies should consider active and differentiated learning, otherwise student's motivation could decrease and, as a result, their personal and professional skills might not be developed to their full potential. This paper outlines some particular aspects involved in teaching first year students from the Faculty of Automatic Control and Computer Science - UPB, Systems Engineering section. In addition, the study tries to identify whether there exist strong and objective indicators (besides the direct feedback from the students and the intuition of the tenured professors) for supporting differentiated learning. To establish this, we continue and develop a previous study based on Data Mining analysis performed over various data related to "Computer Programming" course. This time, we investigate if students' results are somehow correlated by extending the Data Warehouse with information regarding the "Introduction to Operating Systems" course. As suggested above, the students' target group is the same, the courses are taught in the same semester and they belong to the same field, so we try to identify if previous background is of some importance and how does it affect current and future learning behaviour of the students. The conducted analysis reveals interesting facts which might be used as a baseline for implementing student-oriented learning strategies.

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