Abstract:

The landscape of higher education didactics has already changed towards a student centred, activating and thus, an inductive one. Although we are well on track, learning obstacles still exist.

To our knowledge, there is no clear definition about a learning obstacle. Therefore, before extracting the currently existing learning obstacles, we derive a definition for a learning obstacle in a scientific manner. In order to get a more holistic view we include influences of learning theories as well. For our definition, we refer to the cognitive load theory [1], [2] and a doctoral thesis [3] dealing with the effect on an instruction designed by cognitive load theory principles and learning obstacles.

Up to now three learning obstacles, which are based on the cognitive load theory, seem to be the most important ones: didactical obstacles, epistemological obstacles and psycho-genetic obstacles [3]. Nevertheless, we are continuously looking for further ones [2].
Within our project EVELIN we are interested in the experimental improvement of software engineering education. In order to enhance our courses, our approach is to gather existing problems, difficulties, obstacles and barriers to learning in software engineering (SE).
For our data collection we use eye tracking, virtual (VR) and augmented reality (AR) and several questionnaire methods. VR/AR have a double function in this case and act as technology to overcome the collected obstacles in a further step, too. In this paper, we present our first empirical data obtained by five studies. Our approach includes three different perspectives: three of our studies can be counted to evaluation, self- assessments and freshmen conceptions in three questionnaires, one observation by eye tracking and one assessment of students’ answers.

The first study was conducted in a classical module consisting of a lecture and a practice lesson. The second one was conducted during a five-day block course, whereby the participants answered a questionnaire each day. In an eye tracking study [4], we examined the most common errors in C and extracted difficulties of students during source code reading. Analyses of the online learning journal, that is an accompanying part to the lecture in SE, show a perspective of written students’ answers.

In our future research, we use our first results to support individual learning by the development of new teaching and learning arrangements. Thereby we try to implement adaptable learning scenarios using virtual and augmented reality. A further approach is the continuation of the online learning journal that fit the student’s needs. This paper contributes with a definition for learning obstacles, our first results in identifying learning obstacles in SE and first assumptions to overcome these.

References:
[1] J. Sweller, “Element Interactivity and Intrinsic, Extraneous, and Germane Cognitive Load,” Educ Psychol Rev, vol. 22, pp. 123–138, 2010.
[2] R. Moreno, “Cognitive load theory: more food for thought,” Instructional Science, vol. 38, no. 2, pp. 135–141, 2010.
[3] A. Takir, The effect of an instruction designed by cognitive load theory principles on 7th grade students’ achievement in algebra topics and cognitive load. PhD thesis, 2011.
[4] M. Nivala, F. Hauser, J. Mottok, and H. Gruber, “Developing visual expertise in software engineering: An eye tracking study,” IEEE Global Engineering Education Conference, EDUCON, no. April, pp. 613–620, 2016.

Keywords:

Learning Obstacles, Barriers to Learning, Cognitive Load Theory, Evaluation of Student Learning, Software Engineering.