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Personalized Approaches to Supporting the Learning Needs of Lifelong Professional Learners



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Advanced learning technology research has begun to take on a complex challenge: supporting lifelong learning. Professional learning is an essential subset of lifelong learning that is more tractable than the full lifelong learning challenge. Professionals do not always have access to professional teachers to provide input to the problems they encounter, so they rely on their peers in an online learning community (OLC) to help meet their learning needs. Supporting professional learners within an OLC is a difficult problem as the learning needs of each learner continuously evolve, often in different ways from other learners. Hence, there is a need to provide personalized support to learners adapted to their individual learning needs. This thesis explores personalized approaches for detecting the unperceived learning needs and meeting the expressed learning needs of learners in an OLC. The experimental test bed for this research is Stack Overflow (SO), an OLC used by software professionals. To date, seven experiments have been carried out mining SO peer-peer interaction data. Knowing that question-answerers play a huge role in meeting the learning needs of the question-askers, the first experiment aimed to detect the learning needs of the answerers. Results from experiment 1 show that reputable answerers themselves demonstrate unperceived learning needs as revealed by a decline in quality answers in SO. Of course, a decline in quality answers could impact the help-seeking experience of question-askers; hence experiment 2 sought to understand the effects of the help-seeking experience of question-askers on their enthusiasm to continuously participate within the OLC. As expected, negative help-seeking experiences of question-askers had a large impact on their propensity to seek further help within the OLC. To improve the help-seeking experience of question-askers, it is important to proactively detect the learning needs of the question-answerers before they provide poor quality answers. Thus, in experiment 3 the goal was to predict whether a question-answerer would give a poor answer to a question based on their past peer-peer interactions. Under various assumptions, accuracies ranging from 84.57% to 94.54% were achieved. Next, experiment 4 attempted to detect the unperceived learning needs of question-askers even before they are aware of such needs. Using information about a learner’s interactions over a 5-month period, a prediction was made as to what they would be asking about during the next month, achieving recall and precision values of 0.93 and 0.81. Knowing the learning needs of question-askers early creates an opportunity to predict prospective answerers who could provide timely and quality answers to their question. The goal of experiment 5 was thus to predict the actual answerers for questions based only on information known at the time the question was asked. The iv success rate was at best 63.15%, which would only be marginally useful to inform a real-life peer recommender system. Thus, experiment 6 explored new measures in predicting the answerers, boosting the success rate to 89.64%. Of course, a peer recommender system would be deemed to be especially useful if it can provide prompt interventions, especially to get answers to questions that would otherwise not be answered quickly. To this end, experiment 7 attempted to predict the question-askers whose questions would be answered late or even remain unanswered, and a success rate of 68.4% was achieved. Results from these experiments suggest that modelling the activities of learners in an OLC is key in providing support to them to meet their learning needs. Perhaps, the most important lesson learned in this research is that lightweight approaches can be developed to help meet the evolving learning needs of professionals, even as knowledge changes within a profession. Metrics based on the experiments above are exactly such lightweight methodologies and could be the basis for useful tools to support professional learners.



Lifelong learning, education data mining, professional learning, personalization, user modelling, online learning community



Doctor of Philosophy (Ph.D.)


Computer Science


Computer Science



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