Research interests
- Artificial Intelligence in Education
- Web-based Education, elearning
- Intelligent/Adaptive Teaching systens
- Educational Data Mining (check out the new community web site here)
- Skill acquisition, cognitive theories of learning
- User modeling and personalisation
- Computer Supported Collaborative Learning
- Computer Science Education
Current projects
- Support for Collaborative
Learning and Collaborative Work: The main objective is to
develop methods for supporting online learning communities because of
the potential learning communities have for preparing for highly
self-organised and life-long forms of learning and working, for
creating new knowledge, and for creating social capital. Smart group
support tools, including methods for user modelling and data mining,
are used in order to optimise knowledge building and exchange.
Empirical studies and experiments are conducted in order to identify
successful cooperation patterns, including the selection of external
representations and communication media.
This research addresses three objectives:
(1) to identify, using various computational
approaches, patterns of cooperation and media usage that distinguish
successful from less-successful groups
(2) to create visual representations of such patterns
in a manner that can be used as feedback for groups, and to analyse the
effects of such feedback on group performance,
(3) to use the patterns identified in (1) to derive
group- and person-specific guidance, and to test the effectiveness of
this approach.
- ARC-funded work with Judy Kay (School of IT), and Peter
Reimann
& Peter Goodyear (Faculty of Education).
- Educational Data Mining:
Web-based educational systems collect tremendous amount of electronic
data, ranging from simple histories of students' interactions with the
system to detailed traces about their reasoning. However, less
attention has been given to handling the large quantities of data
collected from the students' interactions and extracting pedagogically
useful information from it. Such systems give teachers and learning
researchers access to an extensive source of electronic data about
students' learning, data which is currently under-exploited. Data
mining techniques have the potential to remedy this situation. Data
mining is a multidisciplinary sub-field of computer science combining
work from several areas such as artificial intelligence, databases,
statistics and information visualization. It encompasses a range of
techniques and algorithms for discovering interesting patterns hidden
in large data sets such as association analysis, classification,
cluster analysis as well as statistical analysis and database query. In
this project, the goals are:
- To identify, adapt or create new data mining methods that
are suited for turning learners'
performance data into information of relevance to teachers,
instructional designers, and learning researchers.
- To define how to prepare and analyse the student data so that
we can
extract interesting patterns
- To automate/facilitate some of the algorithm selection and
pre-processing features
- To find suitable ways to present the results to users
- To exploit the patterns found to improve adaptation of teaching
systems.
We have several threads of projects
within this area. I work in collaboration with:
- Student Modelling, tools for
reflection: collaboration with Judy Kay on personalisation, we
built viewable individual models and comparative models, with work by
David Vadas on the Logic
Tutor user model viewer.
Past projects
- Logic
Tutor (with Agathe Merceron) and the Logic-ITA (see my publications)
- Tada-Ed
- Search Tutor
- JITT: Just-in-time training system using workflows and scrutable
personalisation
Funding since 2001
- ARC Discovery Project grant
2009-2011: "Comprehensive support for collaborative writing:
Visualising argument, text and process structures”. Reimann, Calvo,
Yacef (270K).
- ARC Discovery Project grant:
2005-2007. “Analyzing and supporting collaboration management
in online learning communities”. Reimann, Goodyear, Kay, Yacef
($200,000).
- USYD Bridging Support grant 2007: "Mining user models of learner
activity in the Reflect system", Yacef, Kay, Koprinska, $15,000).
- USYD Bridging Support grant 2006:
"Data mining of learner models”. Yacef, Kay, Koprinska, Brusilovsky and
Zaiane ($36,300)
- SIT CRC project grant 2005-2007: “Bridging the Gap: Smart Support
for the Intergenerational Distributed Family”. Kummerfeld, Kay, Yacef,
Koprinska, Poon. ($270,000).
- School of IT 2004-2005:
“Extracting patterns from student data in web-based intelligent
tutoring systems”, Yacef ($15,000).
- School of IT 2004-2005:
“Evaluating the educational usability of data mining and visualization
of students' results in web-based learning systems”, Yacef, Reimann
($10,000).
- SESQUI R&D grant: 2004. “User controlled personalisation in a
system for tutoring logic”, Kay, Yacef, Kummerfeld ($20,000).
- SITCRC project grant: 2002-2003.
“Workflow based just-in-time workplace Training”, Kay, Davis, Yacef
($190,000).
- Sesqui Early Career grant:
2001, “Providing an Intelligent Tutoring Aid to assist undergraduate
teaching”, Yacef ($19,000).
Useful
links