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Note that the information presented here does not necessarily reflect the most up to date syllabus or course information. Rather this information is intended to provide a general overview of course content from previous offerings.
Gord McCalla (GM), Thorvaldson 281.4, 966-4902, <mccalla@cs.usask.ca>
Office hours: by appointment (to arrange a time send me an e-mail or chat to me after class)
Time and Place: Tuesday, 2:30 – 5:30, Spinks S372 (breakout room)
Jim Greer (JG), Julita Vassileva (JV), Ben Daniel (BD) will each take one week of the course; also, members of the ARIES laboratory will provide demonstrations of locally developed learning technologies
Beverly Park Woolf, Building Intelligent Interactive Tutors, Morgan Kaufmann, 2009, 467 p.
20%: paper presentations (select a paper, summarize it, and present it in class) – there will be at least 2 such presentations, maybe more, depending on class size and other constraints
15%: participation (in class and on-line)
10%: project proposal
5%: project proposal presentation
40%: final project
10%: final project presentation (including demonstration)
(note: the entire group will share equally in the group grades; but if there has not been equal sharing of effort among members of the group, differential marks may be awarded)
“Aspects of advanced learning technology are studied, including: learner modelling, instructional planning, domain knowledge representation, authoring tools, tutorial dialogue, evaluation, semantic web technology, and theories of learning. The course takes an applied perspective, with the goal of understanding current resarch issues involved in building intelligent systems for use by learners.”
The course will be a bit more focussed than implied by this description and a bit more adventurous, too. The main goal will be to come to an understanding of the research area known as artificial intelligence in education (AIED). AIED is the area of e-learning that tries to combine research on the frontier of computer science (the “AI”) with research on the frontier of social science (the “ED”). AIED as a research area is extremely applied, but draws on both theoretical and applied ideas and techniques across a wide range of computer science and social science. It has been said that AIED is “AI-complete” in the sense that any problem in AI is instantiated within AIED, often in a way that makes it more interesting and tractable than the general AI problem. Thus, AIED can often explore research issues that in their general form are “too tough” for AI itself to deal with at present.
| Date | Topic | Lect. | Comments |
|---|---|---|---|
| Sept. 7 | Overview of the field: motivation, goals, history, background, the basic AIED architecture, example systems | GM | |
| Sept. 14 | Local ITS projects: iHelp, Recollect, OATS, ProTutor, Conundrum, Scottlebot | ARIES | GM away all week |
| Sept. 21 | Domain knowledge representation, learner modelling, personalization | GM | |
| Sept. 28 | Papers (focus on knowledge), eg. cognitive tutor, Andes, SCENT, … | students | |
| Oct. 5 | Pedagogy, planning, interaction, communication | GM | |
| Oct. 12 | Papers (focus on interaction), eg. AutoTutor, TLTS, LISTEN, … | students | |
| Oct. 19 | Alternative pedagogical styles: collaborative environments, goal-based scenarios, situated cognition, virtual learning environments, distributed communities of practice | GM | |
| Oct. 26 | Papers (focus on alternative pedagogies), eg. Betty’s Brain reciprocal learning system, Lester’s interactive narratives, Fischer’s culturally and socially sensitive systems, Schank’s goal-based scenarios, Soloway’s science discovery environments, … | students | |
| Nov. 2 | Project proposal presentation: special time, later in the week | students | GM away early week |
| Nov. 9 | Evaluation (how to evaluate an advanced learning system): experimental design, statistical techniques, data mining | BD, GM | |
| Nov. 16 | Julita Vassileva’s advanced learning technology | JV | |
| Nov. 23 | Jim Greer’s advanced learning technology | JG | |
| Nov. 30 | Gord McCalla’s advanced learning technology: issues papers: fragmented learning, active learner modelling, ecological approach. Following up on these issues: learning companions (Chan, Aimeur), lifelong learning (Kay, Fischer), educational data mining (Baker, Beck) | GM | |
| Dec. 7 | Project presentation and demonstration: special time | students |
