Affect and Cognition

Learning objectives can be classified into three domains: cognitive, affective, and psychomotor (Bloom Taxonomy). Affective domain refers to learning outcomes in areas such as emotions, moods, attitudes, and feelings, which has been shown to strongly influence cognitive outcomes. As an example, the production of adrenaline and dopamine during emotional experiences affects the transfer of information from short term to long term memory, as well as the level of motivation and cognitive engagement.

Computer Science education is heavy on the cognitive domain but not so for the affective or psychomotor domains. If according to Bloom that holistic education should include all three domains, then how can this be incorporated in CS education? Other disciplines of study have used fieldwork to promote development in these domains. Students go on fieldtrips and work in groups in their learning, as well as developing friendships during the off hours social functions and activities. Engineering students have their share of pranks and parties, and for computer science students, one of the most popular forms of team activity is online video gaming. But these may not be enough to attract the students to these programs or even promote these program to the extent we like to see especially for female students. Perhaps we need to revisit the "art" of computer programming as Knuth has proposed. There is the construction of a beautiful program which seems to be missing in our current CS education, where programming can give our students both intellectual and emotional satisfaction (Ershov, 1972).

Reference:

Ershov, A. P. Aesthetics and the human factor in programming. Comm. ACM 15 (July 1972), 501-505.

Student Perceptions of Their Grades

In a recent study of 278 students from an introductory biology course at the University of Minnesota where students were asked to predict their final course grades after each of the four exams given throughout the term, the following are some of the findings:

• On the first day of class, more than 90% of the students believed they would earn an A or B in the course.
• Students who earned A's and B's in the course at the end predicted they would earn lower grades than they actually earned. Students who earned C's, D's, and E's predicted they would earn higher grades than they actually earned.
• Students who earned high grades were more likely to attend class, submit extra assignments for credit, and attend help-sessions than students who earned low grades.
• On average, it is unlikely that students will significantly improve their grades deep into a semester.

Students are poor predictors of their grades and thus often fail to regulate their learning. First year students are usually not used to the expectations and demands of college level courses, which may explain the reason for their high expectations at the beginning of the course. The students need to be made aware of this and appropriate support put in place to encourage them to participate in order for them to achieve their goals. Perhaps the extra assignments / help-sessions / etc. should be made mandatory to assist first year students to transition into college life.

Reference:

Jensen, P., Moore, R. (2008). Students' Behaviors, Grades & Perceptions in an Introductory Biology Course. The American Biology Teacher. 70(8), pp483-487.

Self-Regulated Learner

A self regulated learner (SRL) is one who knows what she wants to learn and be able to monitor and adjusting her learning in achieving her goals. This involves metacognition, motivation, and strategic actions. The diagram on the right is a metacognition model by Winne and Hadwin. "Metacognition is the awareness learners have about their general academic strengths and weaknesses, cognitive resources they can apply to meet the demands of particular tasks, and their knowledge about how to regulate engagement in tasks to optimize learning processes and outcomes."

The diagram looks too simple and cryptic. Starting with the 4 phases in the bottom right, it shows that the learner goes through these 4 phases in self regulated learning. These phases are not linear, that is, the learner may switch from phase 3 back to phase 1 and then to phase 4, etc. In any case, here is a simplistic explanation of the diagram. After realizing what task is to be learned (phase 1), the learner checks with the external and internal conditions (external conditions are conditions external to the learner, like the time / resource, etc. available, and internal conditions are the motivational factors, beliefs of the learners, etc.), the learner engages in some operations to learn what needs to be learned. In the process of learning, she compares the expected Standards she have constructed (e.g. hitting the golf ball straight, or solving a differential equation), and the Products (or results) she is experiencing (e.g. the golf ball went to the left, or the answer to the solution of the differential equation is different from the answer in the textbook), and this results in the Cognitive Evaluations. The learner then may need to revise the goals and plans (Phase 2), repeat the process, or study and find other tactics (Phase 3), and through further adaptions of these learning processes, continue to evaluate the Standards (which may also be revised), and further compare with the Products of her learning.

In computing, the task that students usually encounter is in the from of creating a computer solution for a problem. The usual tactics we provide the students in their learning include: lecture / lab materials, textbook, previously solved problems, google, etc., and students may explore all these in their learning. Learning goals are useful especially if learning goals are constructed in the form of a semantic map so students can refer back the supporting learning goals so they can reassess whether they have learned these to continue. This is also part of self-regulated learning. In any case, students often find themselves "stuck" in their assignments. What other ways can we help them get "unstuck" so they can continue in the process of self-regulated learning?

Reference:

Winne, P., Perry, N. (2000). Handbook of Self-Regulation. Edited by M. Boekaerts, P.R. Pintrich, M. Zeidner. Academic Press.