COSC420-17S1 (C) Semester One 2017

Intelligent Tutoring Systems

0.1250 EFTS
20 Feb 2017 - 25 Jun 2017


This course addresses the use of artificial intelligence to create computer-based intelligent tutoring systems.

This course addresses the use of Artificial Intelligence to create computer-based Intelligent Tutoring Systems (ITSs). Students will learn data-driven and theoretical methods for creating ITSs. ITSs have been demonstrated to dramatically enhance student learning in many domains, including, to name a few, mathematics, computer science, medicine, biology and engineering. In addition to discussion and readings about methods and models of problem solving, learning, and tutor design, the course will have a "learning by doing" component.

Learning Outcomes

After completing this course, students will be able to
- understand key ideas in the area of Artificial Intelligence in Education
- understand the basics of psychology of learning
- explain the functionality of ITSs
- critically assess approaches to student modelling in ITSs
- design constraints and production rules for use in ITSs
- design and develop small-scale constraint-based tutors
- evaluate ITSs
- understand and assess current research topics in the area of Artificial Intelligence in Education


Subject to approval of the Head of Department.

Timetable 2017

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Friday 13:00 - 15:00 Erskine 121 20 Feb - 9 Apr
1 May - 4 Jun

Course Coordinator / Lecturer

Tanja Mitrovic


Assessment Due Date Percentage 
Assignment One 5%
Assignment Two 15%
Assignment Three 25%
Review of Papers and Participation 10%
Final Exam 45%

Review of selected papers and participation (10%): You will be assigned some papers to read, and will need to write a short review (one paragraph) and email it to the lecturer at least one hour before the lecture. Your review may contain questions about the paper, or a description of the most important point of the paper. You will also participate in class discussion on these papers.

No assignments will be accepted after the drop dead date (i.e. a week after the assignment is due). The penalty for the late submission of an assignment will be an absolute deduction of 15% of the maximum possible mark.


Course notes will be handed out, as well as some papers.

Additional Course Outline Information

Grade moderation

The Computer Science department's grading policy states that in order to pass a course you must meet two requirements:
1. You must achieve an average grade of at least 50% over all assessment items.
2. You must achieve an average mark of at least 45% on invigilated assessment items.
If you satisfy both these criteria, your grade will be determined by the following University- wide scale for converting marks to grades: an average mark of 50% is sufficient for a C- grade, an average mark of 55% earns a C grade, 60% earns a B- grade and so forth. However if you do not satisfy both the passing criteria you will be given either a D or E grade depending on marks. Marks are sometimes scaled to achieve consistency between courses from year to year.

Students may apply for special consideration if their performance in an assessment is affected by extenuating circumstances beyond their control.

Applications for special consideration should be submitted via the Examinations Office website within five days of the assessment.

Where an extension may be granted for an assessment, this will be decided by direct application to the Department and an application to the Examinations Office may not be required.

Special consideration is not available for items worth less than 10% of the course.

Students prevented by extenuating circumstances from completing the course after the final date for withdrawing, may apply for special consideration for late discontinuation of the course. Applications must be submitted to the Examinations Office within five days of the end of the main examination period for the semester.

Tentative lecture schedule

Week / Lecture topic
1   Psychology of learning, IT and education
2   Student Modeling
3   Model/Knowledge tracing
4   Constraint-based Modeling, SQL-Tutor
5   Think-aloud protocol, EER-Tutor, Comparing model-tracing tutors to constraint-based tutors
6   Easter - No Lecture
7   Evaluation of ITSs
8   Authoring tools / ASPIRE
9   Pedagogical module, Third generation tutors (metacognition and affect)
10  CSCL, OLM, Eye Tracking
11  Affective student modelling, AR + ITS
12  Course review

Indicative Fees

Domestic fee $963.00

International fee $3,950.00

* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.

For further information see Computer Science and Software Engineering.

All COSC420 Occurrences

  • COSC420-17S1 (C) Semester One 2017