Affective Learning with Online Software Tutors for Programming

We conducted a study to see if there was any difference in the affective learning of students who used software tutors to learn programming concepts. We used two software tutors – on arithmetic expression evaluation and tracing selection statements for this study. Data was collected over five semesters with the arithmetic tutor and over four semesters with the selection tutor, yielding a sample size in the thousands. The data was analysed using ANOVA, with sex (male versus female), representation (Caucasians and Asians versus other races), and discipline (Computer Science versus other disciplines) as fixed factors. We found no difference in the affective learning of any demographic group all the groups felt in equal measure that they had learned using the software tutors. This is a positive result since affective learning complements cognitive learning. But, we did find statistically significant difference between the sexes and between the representation groups on prior-preparedness on arithmetic tutor, but not selection tutor. This may be because arithmetic tutor covered concepts that students had been exposed to in high school, but the concepts covered by selection tutor were unique to programming and unlikely to have been seen by students who had had no prior programming experience. Finally, non-Computer Science majors learned significantly more concepts than Computer Science majors on both the tutors, even though no significant difference was found between the two disciplinary groups on prior-preparedness. This may allude to difference in perceived self-efficacy of the two groups towards learning from software tutors. 1. Motivation Essentialism permeates the discussion of participation of women in computing. Essentialism posits that people have properties essential to their composition, and that all members of a demographic group share common characteristics (Frieze & Quesenberry, 2013). Gender essentialism has been supported by studies that suggest that girls are concerned about the passivity of their interactions with the computer as a (learning) tool, and that they dislike narrowly and technically focused programming classes ((AAUW, 2000). Whereas men view computers as machines, women view them as tools for use within a societal and/or interdisciplinary context (Margolis & Fisher, 2002). In response, National Center for Women & Information Technology (NCWIT) recommends that one strategy for retaining undergraduates (women) in computing is to align assignments and coursework with student interests and career goals (Barker & Cohoon, 2009). All along, there have also been arguments against using essentialism to explain the differences in the attitudes of men and women towards computing, and therefore, the difference in their participation levels in the discipline. Klawe warned about the widespread misconceptions (Klawe, 2002) that “girls don’t like using computers” and that “girls are people people and computer people are not people people”. Explaining the differences based on culture rather than essentialism has been recently proposed as a more effective way of increasing the participation of women in computing (Frieze & Quesenberry, 2013).