This project has also been featured on the 4TU research innovation website.
Background and justification of the project
Modular curriculum structures are increasingly employed in an attempt to cater to the needs of a more diverse student population. Moreover, modular courses are widely seen as a useful tool to allow for students’ individual learning paths with greater flexibility and choice in managing their studies, and to select, or “mix and match”, for their own learning trajectory within an ever-increasing number of courses in engineering education. At the same time, it is also widely suggested that modular structures may be beneficial to universities in that they potentially allow institutions to expand student markets, develop more efficient uses of resources and increase opportunities for curricula breadth. However, it is also argued that modularization creates the possibility of fragmentation and incoherence of the educational experience, potentially weakens learning outcomes and comes with epistemological, structural and pedagogical challenges (French 2015). The curriculum and STEM education literature (which is often ignored in the development and implementation of engineering education), indicate principles that could help to ensure effective learning from modular courses. Particularly salient are the needs to ensure the “connectivity” between modules, and the integration of modules if effective problem solving is a desired objective (Fung 2017). Moreover, it is often stressed that there is a need for the teaching of ‘learning-to-learn’ strategies and “learning how to build individual learning path” courses (Pepin & Kock 2018), and the importance of assessment to integrate knowledge gained from modules (Cornford 1997).
At TU/e, modularization of the curriculum is part of the strategy 2030 that is expected to enable students “to pursue their individual interests and ambitions, whilst providing rigorous academic engineering education” (TU/e strategy 2030, p. 5). However, students often do not know on which criteria to base their choice, and how to connect the knowledge from previous modules to present module knowledge, and further on how to make connection to future modules. It is known that whilst a modular system can result in significant benefits for students, it is important to recognize that a vital condition for the realization of these gains is the ability of students to act autonomously, when “making a whole of different parts” (Bell & Wade 1993). If there are no modifications in the organization, structure, or teaching practices within and across modules concomitant with modularization, then students might not benefit from modularization: the curriculum has to be (partly) re-designed, so (not only to teach them a particular knowledge package but) to help student to select the for them best modules, and best organization/orchestration of modules, and to support them to connect these and develop their own learning (and studying) trajectories. Greater understanding of student actual learning paths (Pepin & Kock 2018), and helping them how to best “make connections” between and amongst modules (and knowledge packages they have learnt) would allow us to teach/offer modules that more effectively enhance learning. This is likely to happen when we become explicit of (and make explicit connections to) the learning outcomes of previous modules/courses, and expected “incomes” for potential future modules/courses. Concerning mathematics in/for engineering education, there are at least two issues related to modularization: From the student perspective, students often do not recognize the mathematics they have learnt in the mathematics course in the engineering course; they do not know how to connect the mathematics they have previously learnt to the engineering contexts (Gradwohl & Eichler 2018). So, from the mathematics education perspective, one of the main issues is, in order to build their individual learning paths, to make explicit and clear connections between the mathematics and engineering knowledge. This is not only related to the relevance of the mathematics (in the engineering context/s), but also to the language used in both contexts, for example (Schueler-Meyer 2019). From the curriculum perspective, in modularized courses (e.g. mathematics course/s for particular engineering course/s) it is not always clear how the (mathematics and engineering) modules connect to each other, i.e. which exact pre-knowledge students need, with which knowledge they come out, and where this knowledge connects to (Gueudet & Quere 2018).
This proposal links to the educational innovation project CMODE-Constructing knowledge by modular on-demand digital education, led by prof. Lopez Arteaga, where a framework for the design of challenge-based modular education is being developed.
Objectives and expected outcomes of the project
Hence, in this project we aim to investigate how to enhance connections between the mathematics and engineering knowledge; how to make connections between modules; and how to support students to develop suitable individual learning paths. The project will be conducted in collaboration between the Department of Mechanical Engineering (Prof. Ines Lopez-Arteaga), the Department of Mathematics and Computer Science (Dr. Hans Sterk) and Eindhoven School of Education (Prof. Birgit Pepin, Dr Alexander Schueler- Meyer).
The case of the newly developed modularized course 4DB00 Dynamic and control of mechanical systems (first prototype of the modular education philosophy under development in CMODE) will be explored with respect to connections between modules, and how students develop their own learning paths through the course, in particular how they use previously learnt mathematical knowledge.
Phase 1: Exploratory study
Step 1: We use a tool/taxonomy developed at the Technical University Munich (Mittermeier, Ullmann, Strasser, Buchschmid & Mueller 2018, in the following TUM-tool) to establish appropriate descriptions of learning outcomes (of modules), so to increase the transparency in the curriculum, and to evaluate the module interfaces. This is likely to enhance inner-course and inner-departmental discussions concerning curriculum design and how to support students in achieving the individually aspired profile of competences within their individualized learning paths. Hence, we subsequently group-interview the teachers/instructors (e.g. lecturers; teaching assistants; tutors) of the course on the basis of the findings from the taxonomy and a document analysis of the course description/handbook (studiewijzer). We also take into consideration the work done by TU/e Innovation Fund funded projects (e.g. “Efficient and Reliable Online Homologation Recommendation” project; “Handel met Voorkennis” project), and the mathematics department’s alignment of their courses/modules with particular courses (see mathematics department modules and link to courses).
Step 2: We conduct observations during the above-mentioned modularized course, and we interview students about their perceived learning experiences on the course: how they perceive their learning trajectory through the course; how the course connects to previous courses/knowledge, in particular the mathematics courses; and how they perceive to (be able to) connect the course modules to future modules and courses. For this, we use the tools and findings from a present study on student learning gains (see “CDIO study”) and the study on students’ use of (curriculum) resources in the Calculus 1 and Linear Algebra 1 courses (see Pepin & Kock 2018).
Phase 2: Design & evaluation
Step 1: Together with Prof. Arteaga (and her team) and Dr. Sterk, we discuss the findings from the exploratory study, and identify ways of connecting the modules, and the mathematics in/for the modules. This will be articulated in particular design criteria for what we call “connected” modules. According to the literature (e.g. Wingate 2007), it is necessary to educate students to develop their individual learning paths, and hence we/the team will develop a module/series of short training sessions for this particular course, emphasizing how students can learn how to “feed back” (use the previously learnt), “feed up” (connect to present course learning objectives/gains), and “feed forward” (connect to future courses and learning objectives).
Step 2: The newly developed course will be presented and “walked through” with students who have done the course previously, in order to validate and/or amend particular aspects.
Step 3: The newly developed course modules (or aspects of the newly developed course) will be implemented and evaluated. The evaluation will be done through
• student satisfaction data (done as a matter of course for all courses) which can be compared with those of previous years
• student interviews of their perceived experiential learning, e.g. whether they saw benefits of (a) the “feedback-feed up-feed forward” intervention, and (b) the connections made by the instructors/teachers between modules and courses.
• teacher interviews on teaching experiences and their perceptions of student learning with the implementation of the new aspects.
Phase 3: Consolidation & upscaling
In this phase the findings of the study will be disseminated and, more importantly, discussed with teachers of other courses in terms of whether and how the findings (and the tools developed) might have implications for their practice and other courses. Questions such as the following will be investigated:
• In which ways can the TUM tool help teachers to establish how “connected” their modularized courses are?
• What might be the impact of the “learning to connect” series of sessions for other courses? Do the sessions need to be amended and validated for each course, or can there be a generic series that is likely to benefit all students?
• What can teachers learn from students’ perceptions of their learning paths and how to build them? What kinds of tools are necessary? What kinds of learning climates are beneficial?
Implications of the study for policy and practice
The study is original, as it combines (STEM) learning theory with curriculum development in terms of modularization. It is policy relevant for TU/e, as it directly links to the TU/e Strategy 2030 in terms of providing evidence about modularization (and best practice) in engineering education. It has implications for practice, as it provides tools and evidence of how curriculum change (in terms of modularization) can be supported.
Results and learnings
This project is currently still ongoing.
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