Dr Hassan Khosravi

Snr Lecturer, Learning Analytics

Institute for Teaching and Learning Innovation
h.khosravi@uq.edu.au
+61 7 334 60774

Overview

Research achievements and breakthroughs are critical to the growth and reputation of universities, however, the very core of any post-secondary institute is its students and facilitating their learning. Dr Hassan Khosravi strongly believes in the importance of excellent teaching and has dedicated much of his time during the last decade towards this goal.

As a computer scientist by training, Hassan is passionate about applying innovative analytic approaches from fields such as machine learning, data mining, and visualisation to rich and complex digital data available on learners with the goal of personalising education and improving outcomes for all students. He is passionate about exploiting techniques with pedagogical benefits that promote active learning and developing new technologies that intervene in real-time to support students in decision-making.

Hassan's personal experience in teaching, especially large classes, has prepared him well for research in learning analytics and higher education. His teaching career includes instructing roughly 20 different offerings, with class sizes ranging from 50 to 350, of 9 distinct courses to a total of roughly 2500 students. He has the experience and enthusiasm to teach first-year introductory programming courses to majors and non-majors, second-year data structures and software engineering courses, and upper-level courses in algorithms, artificial intelligence, database management, and data science. Hassan has strived to incorporate effective techniques and innovations that promote active learning such as the use of classroom response systems (clickers), flipped classroom methodologies, peer learning systems (e.g., PeerWise), collaborative learning (e.g., two-stage exams), online discussion board (e.g., Piazza), problem-based learning, and massive open online courses into his teaching. In 2015, he was honoured with a UBC Computer Science Department Teaching Award, which is presented to a few of the more than fifty faculty members in the Department for recognition of outstanding teaching. He has also received numerous “congratulations on excellent teaching” letters from the Dean of Faculty of Science, which are presented to only a handful of faculty members each year.

Research Interests

  • Learning Analytics, Higher Education, Machine Learning, CS Education

Qualifications

  • Doctor of Philosophy, Simon Fraser University

Publications

View all Publications

Available Projects

  • Are you interested in applying novel and innovative analytic approaches from fields such as machine learning, data mining, and visualisation to address highly challenging and world-changing tasks such as enhancing and personalising education? If yes, then please send me an email along with your CV and a paragraph or two describing potential research topics that are of interest to you.

    Full PhD scholarships equivalent to the APA rate, $26,288 per annum, (2016 rate, indexed annually) are available. The scholarship will be for three (3) years with the possibility of a six (6)-month extension and is subject to the UQ Research Scholarship General Conditions. You may apply through http://jobs.uq.edu.au/caw/en/job/499994/research-scholar.

    Applicants who successfully meet entry requirements for PhD and are awarded an Australian Postgraduate Award (APA) will be eligible for annual top-up scholarships.

    Also accepting Honours and part-time PhD students, as well as undergraduate students wanting voluntary research roles over the semester.

  • Due to the extensive number of eLearning objects (e.g., videos, articles, review questions), available to learners, it becomes more challenging for them to identify those that best suit their needs in effectively mastering the material. The primary goal of the project is to employ exemplary techniques from the fields of learning analytics and Recommender Systems for Technology Enhanced Learning (RecSysTEL) to harness the rich available data on students to provide accurate, personalised recommendations on learning objects for individual students that reflect both their knowledge gaps and preferences. For more information please see https://arxiv.org/abs/1704.00556

  • Educators continue to face significant challenges in providing high quality, post-secondary instruction in large classes including motivating and engaging diverse populations (e.g., academic ability and backgrounds, generational expectations), and providing helpful feedback and guidance. The primary goal of this project is to apply learning analytics techniques to explore the data collected from large introductory classes to (1) identify groups of students with similar patterns of performance and engagement, and (2) provide them with more meaningful appraisals that are tailored to help them effectively master the learning objectives. Fore more information please see http://www.cs.ubc.ca/~hkhosrav/pub/SIGCSE2017.pdf

View all Available Projects

Publications

Journal Article

Conference Publication

  • Khosravi, Hassan and Cooper, Kendra (2017). Using learning analytics to investigate patterns of performance and engagement in large classes. In: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education. SIGCSE '17, Seattle, WA, United States, (309-314). 8-11 March 2017. doi:10.1145/3017680.3017711

  • Khosravi, Hassan (2013). Fast parameter learning for Markov logic networks using Bayes nets. In: Inductive Logic Programming - 22nd International Conference, ILP 2012, Revised Selected Papers. 22nd International Conference on Inductive Logic Programming, ILP 2012, Dubrovnik, Croatia, (102-115). 17-19 September 2012. doi:10.1007/978-3-642-38812-5_8

  • Khosravi, Hassan, Bozorgkhan, Ali and Schulte, Oliver (2013). Transaction-based link strength prediction in a social network. In: Proceedings of the 2013 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013. 2013 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, Singapore, (191-198). 16-19 April 2013. doi:10.1109/CIDM.2013.6597236

  • Khosravi, Hassan and Bina, Bahareh (2010). A survey on statistical relational learning. In: Advances in Artificial Intelligence - 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Proceedings. 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, ON, Canada, (256-268). 31 May - 2 June 2010. doi:10.1007/978-3-642-13059-5_25

  • Khosravi, Hassan, Schulte, Oliver, Man, Tong, Xu, Xiaoyuan and Bina, Bahareh (2010). Structure learning for Markov Logic Networks with many descriptive attributes. In: AAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference. 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10, Atlanta, GA, United States, (487-493). 11-15 July 2010.

  • Schulte, Oliver, Frigo, Gustavo, Greiner, Russell and Khosravi, Hassan (2010). The IMAP hybrid method for learning Gaussian bayes nets. In: Advances in Artificial Intelligence - 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Proceedings. 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, ON, Canada, (123-134). 31 May - 2 June 2010. doi:10.1007/978-3-642-13059-5_14

  • Schulte, Oliver, Frigo, Gustavo, Greiner, Russell, Luo, Wei and Khosravi, Hassan (2009). A new hybrid method for bayesian network learning with dependency constraints. In: Symposium on Computational Intelligence and Data Mining, 2009 CIDM '09. IEEE Symposium on. 2009 IEEE Symposium on Computational Intelligence and Data Mining (Cidm), Nashville, TN, (53-60). 30 March - 2 April 2009. doi:10.1109/CIDM.2009.4938629

  • Khosravi, Hassan and Colak, Recep (2009). Exploratory Analysis of Co-Change Graphs for Code Refactoring. In: Advances in Artificial Intelligence, Proceedings. 22nd Canadian Conference on Artificial Intelligence, Kelowna Canada, (219-223). May 25-27, 2009.

  • Bina, Bahareh, Schulte, Oliver and Khosravi, Hassan (2009). LNBC: A link-based naive bayes classifier. In: ICDM Workshops 2009 - IEEE International Conference on Data Mining. 2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009, Miami, FL, (489-494). December 6, 2009-December 6, 2009. doi:10.1109/ICDMW.2009.116

  • Khosravi H., Shiri M.E., Khosravi H., Iranmanesh E. and Davoodi A. (2008). TACtic- a multi behavioral agent for trading agent competition. In: Advances in Computer Science and Engineering - 13th International CSI Computer Conference, CSICC 2008, Revised Selected Papers. 13th International Computer Society of Iran Computer Conference on Advances in Computer Science and Engineering, CSICC 2008, Kish Island, (811-815). March 9, 2008-March 11, 2008. doi:10.1007/978-3-540-89985-3_109

Other Outputs

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Are you interested in applying novel and innovative analytic approaches from fields such as machine learning, data mining, and visualisation to address highly challenging and world-changing tasks such as enhancing and personalising education? If yes, then please send me an email along with your CV and a paragraph or two describing potential research topics that are of interest to you.

    Full PhD scholarships equivalent to the APA rate, $26,288 per annum, (2016 rate, indexed annually) are available. The scholarship will be for three (3) years with the possibility of a six (6)-month extension and is subject to the UQ Research Scholarship General Conditions. You may apply through http://jobs.uq.edu.au/caw/en/job/499994/research-scholar.

    Applicants who successfully meet entry requirements for PhD and are awarded an Australian Postgraduate Award (APA) will be eligible for annual top-up scholarships.

    Also accepting Honours and part-time PhD students, as well as undergraduate students wanting voluntary research roles over the semester.

  • Due to the extensive number of eLearning objects (e.g., videos, articles, review questions), available to learners, it becomes more challenging for them to identify those that best suit their needs in effectively mastering the material. The primary goal of the project is to employ exemplary techniques from the fields of learning analytics and Recommender Systems for Technology Enhanced Learning (RecSysTEL) to harness the rich available data on students to provide accurate, personalised recommendations on learning objects for individual students that reflect both their knowledge gaps and preferences. For more information please see https://arxiv.org/abs/1704.00556

  • Educators continue to face significant challenges in providing high quality, post-secondary instruction in large classes including motivating and engaging diverse populations (e.g., academic ability and backgrounds, generational expectations), and providing helpful feedback and guidance. The primary goal of this project is to apply learning analytics techniques to explore the data collected from large introductory classes to (1) identify groups of students with similar patterns of performance and engagement, and (2) provide them with more meaningful appraisals that are tailored to help them effectively master the learning objectives. Fore more information please see http://www.cs.ubc.ca/~hkhosrav/pub/SIGCSE2017.pdf

  • UQx creates Massive Open Online Courses (MOOCs) for the EDx platform (https://www.edx.org/school/uqx) and collects student interactions with courseware and interactive activities. UQx is building open source tools to process learner clickstream data and present visualisations to course stakeholders.

    In this project, we aim to (1) design and develop new visualisations for the UQx learning analytics dashboard, and (2) develop machine learning algorithms to better understand learner’s behaviours taking online courses.

    The project deliverables include extensions to the open source UQx Dashboard project (https://github.com/UQ-UQx/dashboard_js).