Online Learning Outcomes Equivalent to Traditional Methods: But what about the drops?

This is a great result, if I can believe it.  They took 605 students, some in a traditional course and some in a “hybrid” course, and did pre/post tests.  They found no difference in outcomes.

Here’s what I’m not sure about: What happened to those students who failed or who withdrew?  Other studies have suggested that online courses have higher withdraw/failure rates.  Is that the case here? There is only one footnote (page 18) that mentions withdraw/failure: “(27) Note that the pass rate in Figure 1 and Appendix Table A3 cannot be used to calculate the percentage of students who failed the course because the non-passing group includes students who never enrolled or withdrew from the course without receiving a grade.”  But that’s it.  If you lose more students in one format, and the students you lose are the weaker students (not an unreasonable assumption), then having the same learning gains doesn’t mean for all students.  It means that you’ve biased your sample.

The researchers asked the students to complete a number of tests and questionnaires before beginning the course and again after completing it, and they analyzed and compared the results between the two groups of students. The results revealed no statistical difference in educational outcomes between the two groups of students. In fact, the students in the hybrid course performed slightly better, but not enough to be statistically significant.

via Online Learning Outcomes Equivalent to Traditional Methods, Study Finds — Campus Technology.

First Workshop on AI-Supported Education for Computer Science

Shared by Leigh Ann Sudol-DeLyser (Visiting Scholar, New York University) with the SIGCSE list.

Dear SIGCSE-ers!

I would like to announce the First Workshop on AI-Supported Education for Computer Science to be held at the Artificial Intelligence in Education conference this summer in Memphis and invite the submission of papers from the SIGCSE community. Please see the website at: https://sites.google.com/site/aiedcs2013/ Submissions are due by April 12, 2013.

Workshop Description:

Designing and deploying AI techniques within computer science learning environments presents numerous important challenges. First, computer science focuses largely on problem solving skills in a domain with an infinitely large problem space. Modeling the possible problem solving strategies of experts and novices requires techniques that represent a large and complex solution space and address many types of unique but correct solutions to problems. Additionally, with current approaches to intelligent learning environments for computer science, problems that are provided by AI-supported educational tools are often difficult to generalize to new contexts. The need is great for advances that address these challenging research problems. Finally, there is growing need to support affective and motivational aspects of computer science learning, to address widespread attrition of students from the discipline. Addressing these problems as a research community, AIED researchers are poised to make great strides in building intelligent, highly effective AI-supported learning environments and educational tools for computer science and information technology.

Topics of Interest:

• Student modeling for computer science learning
• Adaptation and personalization within computer science learning environments
• AI-supported tools that support teachers or instructors of computer science
• Intelligent support for pair programming or collaborative computer science problem solving
• Automatic question generation or programming problem generation techniques
• Affective and motivational concerns related to computer science learning
• Automatic computational artifact analysis or goal/plan recognition to support adaptive feedback or automated assessment
• Discourse and dialogue research related to classroom, online, collaborative, or one-on-one learning of computer science
• Online or distributed learning environments for computer science