Session Information
16 SES 13 B JS, Online Teaching and Learning in Higher Education
Joint Paper Session NW 16 and NW 22
Contribution
As is well known, engineering has been struggling with high dropout rates for a long time. Recent studies focusing on Germany show that 35% of engineering students finish their studies without a degree (relative to the number of first year students 2012/2013) (Heublein & Schmelzer, 2018). In civil engineering studies this number is even at 42%(ibid.).This is not only an issue in Germany, but rather a common problem in other European countries as well (i.a. De Laet et al., 2017).
Explorations of the reasons leading to the dropout are complex. Contrary to the assumption that dropping out can be reduced to one simple reason, it is rather a complex and multi-dimensional drop out process that ultimately leads to the students decision to disenroll (Heublein et al., 2017). The drop out process can be divided into several phases, which are influenced by different factors (ibid.). Performance problems are a decisive factor, which mainly occur in basic subjects like engineering mechanics [EM]or engineering mathematics (Heublein, Hutzsch, Schreiber, Sommer & Besuch, 2009). These performance problems are often the source for failed exams (Heublein et al., 2017) and thus ultimately a trigger for disenrollment in the first semesters of the studies (Henn & Polaczek, 2007). As a possible cause of the performance problems, a decrease of special technical as well as mathematical knowledge among first-year students can be mentioned (Henn & Polaczek, 2007; Heublein & In der Smitten, 2013).
The reference model for quality assurance at faculties of engineering sciences according to Heublein & In der Smitten (2013) deals with this problem. The reference model states that the use of preventive support measures at different times in the course of the studies may lead to a weakening of the aforementioned problem.These are on the one hand the preliminary phase (self-assessments and prep courses) and on the other hand the introductory phase (additional learning offers). Such support measures are already available in various kinds at many universities, but in most cases only subject-unspecific topics are dealt with. For example, engineering application contexts - like those of EM - remain untreated. Also, there are still no empirical findings regarding the effectiveness of such support measures (Heublein et al., 2017).
For this reason, a collaborative research project was initiated, which has developed a digital support concept, with the objective of supporting the individual learning processes in civil engineering studies by the use of digital higher education. On the basis of the already mentioned reference model, support measures were designed for the preliminary and introductory phase. Thus an online self-assessment and an online prep course have been developed for the preliminary phase, which already focusses on the EM.
However, the paper presentation will only address the introductory phase. Studies of the introductory phase have already shown that first-year students have difficulties in understanding the key core concepts of EM(Prusty et al., 2011). One possible reason for this could be that the students do not succeed in linking the lecture content with the corresponding key core concepts. Research into solving this problem related to the EM content, are not yet available. Remedy could be created at this point by the so-called interactive online modules [IOM].
The IOM consists of learning videos and exercises both specially developed for the EM1 and EM2 courses (first two semesters). The learning videos can be divided into experimental videos (to illustrate the core concepts) and in animated slideshows (as teaching and learning support for calculating exercises). The online exercises are implemented using the server-based system JACK (Striewe, Zurmaar & Goedicke, 2015), which enables computer-aided testing with automated feedback generation.
Method
One of the research questions within the framework of the collaborative research project is to investigate the effectiveness of the IOM respective the individual learning success of students in the field of EM in civil engineering studies. This research question will be answered longitudinally in a classic experimental and control group design. For this purpose, Paper & Pencil surveys are executed at three measuring points [MP]: beginning of the first semester (MP1), end of the first semester (MP2) and end of the second semester (MP3). The control group consists of students who attend the EM1 and EM2 courses in the conventional concept. In contrast, the experimental group differs by the inclusion of the developed IOM. The data collection started in the winter term 2018/2019 and will be completed by the end of the summer term 2019. The effect of the IOM is determined on the basis of the results in the applied knowledge tests, the exam grades and the achieved credit points. The knowledge tests are based on the EM items by Dammann & Lang (2018) – EM, calculating and mathematical modeling ability. The use of the IOM is recorded based on the logged access data of the individual elements (learning videos and JACK exercises). The first of the three MPs includes the gathering of demographic variables such as gender, the university-entrance diploma grade, the last school grades (mathematics, physics, chemistry and technology), the school type and more information on the educational background. Also, background variables such as the highest educational attainment of the parents, the country of birth for the students and their parents, as well as the place of their graduation are collected. Due to the structure of the study as a collaborative research project, conclusions can also be drawn on the generalizability of the results with regard to the effectiveness of IOM.
Expected Outcomes
The digital elements of the support concept have been completely developed. Also, the data collection at MP1 has been successfully completed. At the moment the data of the MP1 is evaluated, as well as the data at the further MP is gathered. In the data collection at MP1, a high number of participants was recorded (at one of the locations about n = 200). This can be due to a change in the methodological approach of the data collection. Originally intended, also done in pilot phase, the demographic questionnaire and knowledge tests were online tests. However, only a small number of test persons could be generated at all locations during the pilot phase - despite the test participants being awarded with a money compensation. This led to a change in the concept: online tests are replaced by paper & pencil tests, which in turn are conducted as part of the EM lectures. In the paper presentation, the IOM will be presented exemplary, also the conclusions from the pilot phase as well as the first results of the main study. This should serve as a foundation for a discussion in the plenum. The focus will be on the question of whether the students are actively using the IOM, if the IOM are delivering individual learning success and have the potential to become an integral part of the future digitization of the higher education landscape.
References
Dammann, E. & Lang, M. (2018). Mechanisch-mathematisches Modellieren als Prädiktor für Studienerfolg in der Eingangsphase des Bauingenieurstudiums. In G. Kammasch & J. Petzold (Ed.), Digitalisierung in der Techniklehre - Ihr Beitrag zum Profil Technischer Bildung - 12. Ingenieurpädagogischen Regionaltagung TU Ilmenau vom 11.-13. Mai 2017, 143–148, Ilmenau. De Laet, T., Broos, T., Van Staalduinen, J., Ebner, M., Langie, G., Van Soom, C. et al. (2017). Confidence in and beliefs about first-year engineering student success - Case study from KU Leuven, TU Delft and TU Graz. Proceedings of the 45th SEFI Annual Conference 2017, 894–902. Henn, G. & Polaczek, C. (2007). Studienerfolg in den Ingenieurwissenschaften. Das Hochschulwesen - Forum für Hochschulforschung, -praxis und -politik, 05.2007, 144–147. Heublein, U., Ebert, J., Isleib, S., Hutzsch, C., König, R., Richter, J. et al. (2017). Zwischen Studienerwartungen und Studienwirklichkeit - Ursachen des Studienabbruchs, beruflicher Verbleib der Studienabbrecherinnen und Studienabbrecher und Entwicklung der Studienabbruchquote an deutschen Hochschulen. No. 01.2017. Deutsches Zentrum für Hochschul- und Wissenschaftsforschung GmbH, Hannover. Heublein, U., Hutzsch, C., Schreiber, S., Sommer, D. & Besuch, G. (2009). Ursachen des Studienabbruchs in Bachelor- und in herkömmlichen Studiengängen - Ergebnisse einer bundesweiten Befragung von Exmatrikulierten des Studienjahres 2007/08. Deutsches Zentrum für Hochschul- und Wissenschaftsforschung GmbH, Hannover. Heublein, U. & In der Smitten, S. (2013). Referenzmodell zur Qualitätssicherung an Fachbereichen und Fakultäten des Maschinenbaus und der Elektrotechnik - Konzept für die Lehre. Maschinenhaus - die VDMA Initiative für Studienerfolg. No. 2/4. HIS-Institut für Hochschulforschung, Hannover. Heublein, U. & Schmelzer, R. (2018). Die Entwicklung der Studienabbruchquoten an den deutschen Hochschulen - Berechnungen auf Basis des Absolventenjahrgangs 2016. Deutsches Zentrum für Hochschul- und Wissenschaftsforschung (DZHW), Hannover. Prusty, G., Russell, C., Ford, R., Ben-Naim, D., Ho, S., Vrcelj, Z. et al. (2011). Adaptive tutorials to target threshold concepts in mechanics — a community of practice approach. Faculty of Engineering - Papers (Archive), 305–311. Striewe, M., Zurmaar, B. & Goedicke, M. (2015). Evolution of the E-Assessment Framework JACK. Gemeinsamer Tagungsband der Workshops der Tagung Software Engineering 2015, 118–120.
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