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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 theoretical and data-driven 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.
After successful completion of this course, students will be able tounderstand key ideas in the area of Artificial Intelligence in Educationunderstand the basics of psychology of learning critically assess approaches to student modelling in ITSsdevelop constraints and production rules for use in ITSsdevelop small-scale constraint-based tutorsunderstand current research topics in the area of Artificial Intelligence in Education
Subject to approval of the Head of Department.
Students must attend one activity from each section.
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. In order to pass a course you must meet two requirements:a) The university has adopted a common scale for converting marks to grades. According to this scale, an average mark of 50% is sufficient to pass the course (i.e. to achieve a C-), with an average mark of 55% a C grade is achieved and so forth. We apply this conversion scale to the average marks students achieve over all assessment items.b) You must achieve an average mark of at least 45% on invigilated assessment items.Marks are sometimes scaled to achieve consistency between courses from year to year.Updated Semester One 2020 assessment deadlines and details will be available once finalised.
Course notes will be handed out, as well as some papers.
Library portalCourse Information on Learn
Course Outline 2020
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.
Week / Lecture topic1 Psychology of learning, Computers in education2 Student Modeling3 Model/Knowledge tracing4 Constraint-based Modeling, Think-aloud protocol5 SQL-Tutor6 EER-Tutor, Comparing model-tracing tutors to constraint-based tutors7 Evaluation of ITSs8 Authoring tools, ASPIRE9 Pedagogical module, Third generation tutors (metacognition and affect)10 CSCL, OLM, Eye Tracking11 Affective student modelling, AR + ITS12 Presentations and Course review
Notices about this course will be posted to the course forum in the Learn system (learn.canterbury.ac.nz). CSSE students will also be made members of a class called "CSSE Notices", where general notices will be posted that apply to all classes (such as information about building access or job opportunities).There are several important documents available online about departmental regulations, policies and guidelines at the following site. We expect all students to be familiar with these. Policies
Domestic fee $1,022.00
International Postgraduate fees
* 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.