2016 |
Guenaga, Mariluz; Menchaca, Iratxe; Solabarrieta, J Using learning analytics to assess project management skills on engineering degree courses Journal Article Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality, pp. 369-376, 2016, ISBN: 978-1-4503-4747-1. Abstract | Links | BibTeX | Tags: Collaborative Learning, Computational Science and Engineering Education, computing education programs, data analytics, database management system engines, decision support systems, Engineering Education, Formative Assessment, human computer interaction (hci), information systems applications, interaction paradigms @article{Guenaga2016b, title = {Using learning analytics to assess project management skills on engineering degree courses}, author = {Mariluz Guenaga and Iratxe Menchaca and J. Solabarrieta}, editor = {Francisco José García-Peñalvo}, url = {https://dl.acm.org/citation.cfm?doid=3012430.3012542}, doi = {10.1145/3012430.3012542}, isbn = {978-1-4503-4747-1}, year = {2016}, date = {2016-11-02}, journal = {Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality}, pages = {369-376}, abstract = {Learning analytics is a field of study that has been evolving since the outset in attempting to meet various needs. The use of learning analytics techniques has helped us ascertain the level of students' participation and their degree of satisfaction in order to learn how they use resources or identify students at risk. Research currently focuses on applying these techniques to find out how the student learns and to improve teaching/learning processes. A key aspect in improving these processes is the assessment of general competences, which constitutes key learning in engineering students and has thus been identified as a need that can be met by learning analytics. An experiment was conducted on 93 students from different engineering groups at the University of Deusto with a view to assessing the extent to which students have developed the project management competence, using learning analytics techniques. The model designed for analysis is described in this paper, in addition to the methodology and research carried out. Results have shown that by combining an automatic analysis and exploratory learning analytics techniques, conclusions can effectively be drawn about the extent to which a given student has developed a competence based on data obtained via use of a technological tool.}, keywords = {Collaborative Learning, Computational Science and Engineering Education, computing education programs, data analytics, database management system engines, decision support systems, Engineering Education, Formative Assessment, human computer interaction (hci), information systems applications, interaction paradigms}, pubstate = {published}, tppubtype = {article} } Learning analytics is a field of study that has been evolving since the outset in attempting to meet various needs. The use of learning analytics techniques has helped us ascertain the level of students' participation and their degree of satisfaction in order to learn how they use resources or identify students at risk. Research currently focuses on applying these techniques to find out how the student learns and to improve teaching/learning processes. A key aspect in improving these processes is the assessment of general competences, which constitutes key learning in engineering students and has thus been identified as a need that can be met by learning analytics. An experiment was conducted on 93 students from different engineering groups at the University of Deusto with a view to assessing the extent to which students have developed the project management competence, using learning analytics techniques. The model designed for analysis is described in this paper, in addition to the methodology and research carried out. Results have shown that by combining an automatic analysis and exploratory learning analytics techniques, conclusions can effectively be drawn about the extent to which a given student has developed a competence based on data obtained via use of a technological tool. |