Moving Students from Rule Based to Creative Problem Solving Skills

Lubben et al. wrote an article on the change of students' perception of "preciseness" under different contexts. In laboratory / pharmacy settings, measurements are expected to be more precise than in a kitchen setting. Deviations are not acceptable in the laboratory / pharmacy settings, but ok in a kitchen setting. The interesting thing is that students based their judgement on the perceived effects of the result mostly rather than on the instructions given or the process to be used. That is, whether deviations are ok or not, (or what precision really means), depends largely on whether there will be any effects on the results. In the kitchen setting, deviations are ok because the measurements are perceived not to have significant impact on the results, whereas such is not the case in laboratory and pharmacy settings. From this, the authors conclude that context makes a difference in the students' choice of a point-paradigm (drawing conclusions from individual data points) in the laboratory / pharmacy settings as opposed to the set-paradigm (drawing conclusions from the ensemble of all data) used in the kitchen. One of the goal of teaching is to move students from a point-paradigm to a set-paradigm.

In computing, context does not play such a significant role in student's perception of preciseness. Whether the students are writing a program for data analysis in a laboratory or for a game program, preciseness and accuracy are needed. But a similar transformation of students' perception of what is essential in programming needs to take place for computer science students. Novice programmers stick to a "formulaic" strategy in solving problems. To them, there is one solution they need to come up with in solving a problem. Whereas seasoned programmers are free to explore different ways of thinking about the problems, modeling, and solving them. Students eventually learn that the result is what really matters and they realize they can be free to be creative, and innovate and construct their programs.

I started programming with BASIC, and was it fun to create programs with GOTO's! I could create the most convoluted programs and few people would have understood them, but it was fun. Those programs would probably fail under many conditions and any half decent test plan, but it was fun. I wonder whether our computer science education may be prescribing too many rules in programming and rob the students from experiencing the fun and creativity in computer science.

Reference:

Lubben, F., Campbell, B., Buffler, A., Allie, S. (2004). The Influence of Context on Judgements of the Quality of Experimental Measurements. Proceedings of the 12th Annual Conference of the Southern African Association for Research in Mathematics, Science and Technology Education. Pages 569 - 577.

STROBE

STROBE is a classroom observation tool used by trained observers on learners without interfering with their activities. It yields quantitative data from brief observations of individual learners from around the classroom. The observation occur over 5-min "STROBE cycle" that is typically repeated from 8 to 10 times depending on the length of the class session. Each STROBE cycle proceeds as follows:

First, the observer writes down the following:

• the start time of the cycle,
• the subjects to be observed, whether it be "entire class", "subgroups", or any specific group,
• the major activity, which can be "instructional", "procedural", or other,
• the estimated portion of learners on task, which can be "all", "almost all", "half or less", etc.,

Next, the observer selects a learner from the class and observes the selected learner for 10 to 20 seconds, marking the type of engagement the learner exhibits, such as "talking", "listening", "reading", "writing", etc., and the object at whom the learner's engagement is directed ("other learners", "instructor", "self", etc.). This is repeated 4 times.

The observer also observes the instructor and marks the instructor's type and object of engagement. Finally, the observer also notes the number of questions students ask in the cycle.

What STROBE provides is a simple and effective way of gauging the level of engagement of students in the classroom, not necessary learning. It can also be skewed by untrained observers like me who did it for the first time in one of the CS classes recently. As a newbie to this, I found myself picking on the students who were not "norms" to be my targets, those who were working on their computers, those who were talking to other people, etc. I had to keep reminding to randomly pick students and not just the ones that catch my attention!

Reference:

Kelly, P. , Haidet, P., Schneider, V., Searle, N., Seidel, C., Richards, B. (2005). A Comparison of In-Class Learner Engagement Across Lecture, Problem-Based Learning, and Team Learning Using the STROBE Classroom Observation Tool, Teaching and Learning in Medicine, Volume 17, Issue 2 April 2005 , pages 112 - 118

Outliers

Having heard of the latest book by Malcolm Gladwell, Outliers, and read some of the raving reviews about the book, I was naturally drawn to it while my family roamed the malls during the Christmas holidays. Little did I anticipate that as soon as I started the first page of the book, it was not until three chapters later when I finally left the store with a copy in hand. The condensed message behind the book is simple: outliers are not born, they are made. They are shaped by culture, tradition, communities, and they do have breaks that they seize and take advantage of .. but most of all, they work hard. This reminds me of one of my professors in my undergraduate years who told me that if one wants to pursue a PhD, all one needs is patience, persistence, and money! According to Malcolm, there is this magic number of 10,000 hours of practice and hard work which outliers usually spend to get to where they are at. In a culture where many believe that success comes only to the selected few with special genetic makeup, or by pure luck, the book contains a number of evidences to dispel these perceptions. Also, Malcolm suggests that our culture and tradition may either make or break us. He traces the cause of a number of plane crashes to the cultural influence on the pilots, and the difference in aptitude towards mathematics between Asian and Western children also to their different cultural upbringing.

What does this have to do with computer science education? I have heard so many students who claim that they “are just not made to program”, or they “just don’t have the aptitude” for computer programming. What Malcolm has shown, even though mostly via anecdotal accounts, that success depends largely on repetitive practice and hard work. In computing, it has also been demonstrated that highly intensive training programs have been successful in converting students with no programming background to proficient software developers. The problem that face every computer science educator is how to make this repetitive practice and seemingly hard work that require long hours of engagement to be perceived as challenging, rewarding, and, at the same time, providing the students with a sense of autonomy in their learning – the three essential ingredients, according to Malcolm, that make any work satisfying.