Analysing Student Performance using Sparse Data of Core Bachelor Courses
Saarela, M., & Kärkkäinen, T. (2015). Analysing Student Performance using Sparse Data of Core Bachelor Courses. Journal of Educational Data Mining, 7(1), 3-32.
Published inJournal of Educational Data Mining
© the Authors. This is a final draft version of an article whose final and definitive form has been published by International Working Group on Educational Data Mining.
Curricula for Computer Science (CS) degrees are characterized by the strong occupational orientation of the discipline. In the BSc degree structure, with clearly separate CS core studies, the learning skills for these and other required courses may vary a lot, which is shown in students’ overall performance. To analyze this situation, we apply nonstandard educational data mining techniques on a preprocessed log file of the passed courses. The joint variation in the course grades is studied through correlation analysis while intrinsic groups of students are created and analyzed using a robust clustering technique. Since not all students attended all courses, there is a nonstructured sparsity pattern to cope with. Finally, multilayer perceptron neural network with cross-validation based generalization assurance is trained and analyzed using analytic mean sensitivity to explain the nonlinear regression model constructed. Local (withinmethods) and global (between-methods) triangulation of different analysis methods is argued to improve the technical soundness of the presented approaches, giving more confidence to our final conclusion that general learning capabilities predict the students’ success better than specific IT skills learned as part of the core studies. ...
PublisherInternational Working Group on Educational Data Mining
Publication in research information system
MetadataShow full item record
Showing items with similar title or keywords.
Niemelä, Marko; Kulmala, Juha-Pekka; Kauppi, Jukka-Pekka; Kosonen, Jukka; Äyrämö, Sami (Springer London, 2017)Both kinematic parameters and ground reaction forces (GRFs) are necessary for understanding the biomechanics of running. Kinematic information of a runner is typically measured by a motion capture system whereas GRF during ...
Student agency analytics : learning analytics as a tool for analysing student agency in higher education Jääskelä, Päivikki; Heilala, Ville; Kärkkäinen, Tommi; Häkkinen, Päivi (Taylor & Francis, 2021)This paper presents a novel approach and a method of learning analytics to study student agency in higher education. Agency is a concept that holistically depicts important constituents of intentional, purposeful, and ...
Saarela, Mirka; Hämäläinen, Joonas; Kärkkäinen, Tommi (Springer International Publishing, 2017)A clustering result needs to be interpreted and evaluated for knowledge discovery. When clustered data represents a sample from a population with known sample-to-population alignment weights, both the clustering and the ...
Nieminen, Paavo (University of Jyväskylä, 2016)Machine learning tasks usually come with several mutually conﬂicting objectives. One example is the simplicity of the learning device contrasted with the accuracy of its performance after learning. Another common example ...
Tuovinen, Tommi (2013)Tämän tutkimuksen tarkoituksena on avata neuroverkon ja moniulotteisen skaalauksen käsitteitä sekä demonstroida kuinka näitä voidaan käyttää yhdessä. Tutkielmassa suoritetaan konstruktio, jossa MLP-verkko koulutetaan ...