Analyzing and Fostering Learning for the development of Competence

Project Description

Data generated during the processing of digital learning units shall get evaluated automatically in order to allow teachers to identify unproductive learning processes in a timely manner, to identify causal learning difficulties and to support instructional interventions that transform unproductive learning processes into productive ones. 

Questions and hypotheses:

1a) Which (process) data from the learning environment are needed to what extent in order to draw reliable and valid conclusions about learning or learning difficulties?

1b) How can the heterogeneous data be integrated in order to reliably and validly identify productive and unproductive learning processes with regard to competence development?

2a) Which learning pathways lead to competence development (i.e. are productive) or do not lead to competence development (i.e. are unproductive)? (Hypotheses: Productive learning processes are characterized by continuous successful processing of activities and thus knowledge integration. Unproductive learning processes are characterized by the unsuccessful completion of an activity and, by consequence, subsequent failure.)

2b) Which learning difficulties can be identified as the cause of unproductive learning processes? (Hypothesis: In addition to a lack of – albeit necessary – prior knowledge, prevailing ideas about everyday life can be identified as learning difficulties.)

3) To what extent can learning processes that have been identified as being unproductive be transformed into productive learning processes through targeted instruction? (Hypotheses: Unproductive learning processes, which are caused by a lack of prior knowledge, can be transformed into productive learning processes by direct instruction following the failure to accomplish. Unproductive learning processes, which are caused by ideas of everyday life, can be transformed into productive learning processes by guided instruction towards the successful processing of the activity.)

Project objectives

Learning Analytics shall serve to identify non-invasively (i.e. without explicit assessment) learning processes in digital learning in school lessons which do or do not contribute to competence development, in order to gain insights into supporting the competence development of (preferably) all students.

Cooperations

Prof. Dr. Knut Neumann, IPN - Leibniz Institute for Science and Mathematics Education

Prof. Dr. Nikol Rummel, Faculty of Philosophy and Educational Research, Ruhr-Universität Bochum

Project Management

Project Team

• Dr. Daniele Di Mitri
• Onur Karademir

Project Details