Learning styles and CASE tools in Software Engineering

Concrete Converger + Assimilator = 52% Accomodator + Diverger = 48% Active Reflective Accomodator + Converger = 44% Diverger + Assimilator = 56% Total number of students = 23 Index of Learning Styles Soloman and Felder The comparison of student and staff results from the Index of Learning Style survey are shown in Table 2 and Table 3. Table 2: Student Index of Learning Style Survey Results Processing Perception Input Understanding Active 57% Sensory 70% Visual 83% Sequential 61% Reflective 43% Intuitive 30% Verbal 17% Global 39% Total number of students = 23 Table 3: Staff Index of Learning Style Survey Results Processing Perception Input Understanding Active 12.5% Sensory 37.5% Visual 75% Sequential 62.5% Reflective 87.5% Intuitive 62.5% Verbal 25% Global 37.5% Total number of staff = 8 The results from Table 2 show that: 57% of the students learn best actively, yet our teaching is often passive; 70% of the students are sensors, yet we often teach intuitively; 83% of the students are visual, yet a lot a our material is presented to them verbally or in written form; 39 % of the students are global learners, yet our teaching is often sequential and does not focus on TL Forum 2000: Fowler et al learning styles and CASE tools in Softw... http://lsn.curtin.edu.au/tlf/tlf2000/fowler.html 7 of 10 9/06/2009 3:34 PM the big picture; Our results for students are similar to those of Mackenzie, [1998] who surveyed 75 Mechanical Engineering students. Our students are showing a greater tendency to sensory (70% compared with 56% from Mackenzie's studies). Soloman [1999] has surveyed large volumes of students via her online site and her results show that: 80% of all students are active learners 55% are sensors and 60% for engineers 75% are visual learners 60% are sequential learners Our students showed a more even split between active and reflective learners and a greater tendency to sensory learning. We are aware of the small sample size in this initial study. However, the surveying of students is continuing and an increase in data will enhance this research. Most engineering academics are intuitors, but the majority of engineering students are sensors [Felder 1988]; our results indicate a similar outcome, Table 3. Consequently, if staff teach according to their learning styles then a mismatch will exist with the average student. Application to CASE tools Application to our current teaching practice Currently, we introduce Rational Rose by means of an online tutorial. A nine step exercise, taking approximately two hours to complete, uses all stages of the package to finally generate skeleton code for a small program. Our results show that a large percentage of our students are active learners and hence using practical hands on sessions to teach the package directly supports their learning style. This hands on approach also supports sensory learners. However, 39% of our students are global learners and we are directing them to work sequentially. We aim to provide a 30 minute quiz which will necessitate students searching through the package and therefore obtaining a global view of the software's capability. This approach should also appeal to the active learner who is keen to "see how it works". The package is particularly visual with its adherence to the UML modelling language and associated graphical icons. Application of Felder's idea stopping several times in a teaching session to pause for thought and pose a short question for discussion would provide support for reflective learners [Felder 1988]. This technique, in the context of the online exercise, would assist in breaking up the long two hour session, making the experience more stimulating and rewarding. Another suggestion [Felder 1993] is to talk to students about their learning styles and the strengths and weaknesses associated with each style. We propose to incorporate a topic into our first year Foundation Unit to survey and discuss student learning styles.