Computing and Information Systems - Theses
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ItemUnsupervised all-words sense distribution learning( 2016)There has recently been significant interest in unsupervised methods for learning word sense distributions, or most frequent sense information, in particular for applications where sense distinctions are needed. In addition to their direct application to word sense disambiguation (WSD), particularly where domain adaptation is required, these methods have successfully been applied to diverse problems such as novel sense detection or lexical simplification. Furthermore, they could be used to supplement or replace existing sources of sense frequencies, such as SemCor, which have many significant flaws. However, a major gap in the past work on sense distribution learning is that it has never been optimised for large-scale application to the entire vocabularies of a languages, as would be required to replace sense frequency resources such as SemCor. In this thesis, we develop an unsupervised method for all-words sense distribution learning, which is suitable for language-wide application. We first optimise and extend HDP-WSI, an existing state-of-the-art sense distribution learning method based on HDP topic modelling. This is mostly achieved by replacing HDP with the more efficient HCA topic modelling algorithm in order to create HCA-WSI, which is over an order of magnitude faster than HDP-WSI and more robust. We then apply HCA-WSI across the vocabularies of several languages to create LexSemTm, which is a multilingual sense frequency resource of unprecedented size. Of note, LexSemTm contains sense frequencies for approximately 88% of polysemous lemmas in Princeton WordNet, compared to only 39% for SemCor, and the quality of data in each is shown to be roughly equivalent. Finally, we extend our sense distribution learning methodology to multiword expressions (MWEs), which to the best of our knowledge is a novel task (as is applying any kind of general-purpose WSD methods to MWEs). We demonstrate that sense distribution learning for MWEs is comparable to that for simplex lemmas in all important respects, and we expand LexSemTm with MWE sense frequency data.
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ItemMachine learning for feedback in massive open online courses( 2016)Massive Open Online Courses (MOOCs) have received widespread attention for their potential to scale higher education, with multiple platforms such as Coursera, edX and Udacity recently appearing. Online courses from elite universities around the world are offered for free, so that anyone with internet access can learn anywhere. Enormous enrolments and diversity of students have been widely observed in MOOCs. Despite their popularity, MOOCs are limited in reaching their full potential by a number of issues. One of the major problems is the notoriously low completion rates. A number of studies have focused on identifying the factors leading to this problem. One of the factors is the lack of interactivity and support. There is broad agreement in the literature that interaction and communication play an important role in improving student learning. It has been indicated that interaction in MOOCs helps students ease their feelings of isolation and frustration, develop their own knowledge, and improve learning experience. A natural way of improving interactivity is providing feedback to students on their progress and problems. MOOCs give rise to vast amounts of student engagement data, bringing opportunities to gain insights into student learning and provide feedback. This thesis focuses on applying and designing new machine learning algorithms to assist instructors in providing student feedback. In particular, we investigate three main themes: i) identifying at-risk students not completing courses as a step towards timely intervention; ii) exploring the suitability of using automatically discovered forum topics as instruments for modelling students' ability; iii) similarity search in heterogeneous information networks. The first theme can be helpful for assisting instructors to design interventions for at-risk students to improve retention. The second theme is inspired by recent research on measurement of student learning in education research communities. Educators explore the suitability of using latent complex patterns of engagement instead of traditional visible assessment tools (e.g. quizzes and assignments), to measure a hypothesised distinctive and complex learning skill of promoting learning in MOOCs. This process is often human-intensive and time-consuming. Inspired by this research, together with the importance of MOOC discussion forums for understanding student learning and providing feedback, we investigate whether students' participation across forum discussion topics can indicate their academic ability. The third theme is a generic study of utilising the rich semantic information in heterogeneous information networks to help find similar objects. MOOCs contain diverse and complex student engagement data, which is a typical example of heterogeneous information networks, and so could benefit from this study. We make the following contributions for solving the above problems. Firstly, we propose transfer learning algorithms based on regularised logistic regression, to identify students who are at risk of not completing courses weekly. Predicted probabilities with well-calibrated and smoothed properties can not only be used for the identification of at-risk students but also for subsequent interventions. We envision an intervention that presents probability of success/failure to borderline students with the hypothesis that they can be motivated by being classified as "nearly there". Secondly, we combine topic models with measurement models to discover topics from students' online forum postings. The topics are enforced to fit measurement models as statistical evidence of instruments for measuring student ability. In particular, we focus on two measurement models, the Guttman scale and the Rasch model. To the best our knowledge, this is the first study to explore the suitability of using discovered topics from MOOC forum content as instruments for measuring student ability, by combining topic models with psychometric measurement models in this way. Furthermore, these scaled topics imply a range of difficulty levels, which can be useful for monitoring the health of a course and refining curricula, student assessment, and providing personalised feedback based on student ability levels and topic difficulty levels. Thirdly, we extend an existing meta path-based similarity measure by incorporating transitive similarity and temporal dynamics in heterogeneous information networks, evaluated using the DBLP bibliographic network. The proposed similarity measure might apply to MOOC settings to find similar students or threads, or thread recommendation in MOOC forums, by modelling student interactions in MOOC forums as a heterogeneous information network.