Browsing by Author "Susnjak, Teo"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
- ItemAccelerating classifier training using AdaBoost within cascades of boosted ensembles : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Sciences at Massey University, Auckland, New Zealand(Massey University, 2009) Susnjak, TeoThis thesis seeks to address current problems encountered when training classifiers within the framework of cascades of boosted ensembles (CoBE). At present, a signifi- cant challenge facing this framework are inordinate classifier training runtimes. In some cases, it can take days or weeks (Viola and Jones, 2004; Verschae et al., 2008) to train a classifier. The protracted training runtimes are an obstacle to the wider use of this framework (Brubaker et al., 2006). They also hinder the process of producing effective object detection applications and make the testing of new theories and algorithms, as well as verifications of others research, a considerable challenge (McCane and Novins, 2003). An additional shortcoming of the CoBE framework is its limited ability to train clas- sifiers incrementally. Presently, the most reliable method of integrating new dataset in- formation into an existing classifier, is to re-train a classifier from beginning using the combined new and old datasets. This process is inefficient. It lacks scalability and dis- cards valuable information learned in previous training. To deal with these challenges, this thesis extends on the research by Barczak et al. (2008), and presents alternative CoBE frameworks for training classifiers. The alterna- tive frameworks reduce training runtimes by an order of magnitude over common CoBE frameworks and introduce additional tractability to the process. They achieve this, while preserving the generalization ability of their classifiers. This research also introduces a new framework for incrementally training CoBE clas- sifiers and shows how this can be done without re-training classifiers from beginning. However, the incremental framework for CoBEs has some limitations. Although it is able to improve the positive detection rates of existing classifiers, currently it is unable to lower their false detection rates.
- ItemLearning analytics : on effectiveness of dashboarding for enhancing student learning : a thesis with publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Technology, School of Mathematical & Computational Sciences, Massey University, Auckland, New Zealand(Massey University, 2022) Ramaswami, GomathyOngoing advancements in learning analytics have provided institutions with immense opportunities to identify and discern student learning patterns across different course offerings. These patterns can help identify those students who may be at some risk of course failure (or of course completion) as soon as possible, which further allows institutions in timely offering them guidance and support for overcoming their learning difficulties. Learning Analytics Dashboards (LAD) are currently used to deliver graphical representations of data-driven insights timeously to support management teams, instructors, and students. LADs provide a comprehensive overview on current learning environments with much use of visualizations for displaying learning patterns that can capture various aspects of the student learning experience. Hence, LADs are increasingly being used as a pedagogical approach for motivating students and supporting them in meeting their learning goals. This research study has developed a student-facing LAD that shows a snapshot of students' online learning behaviors by implementing descriptive analytics components and also incorporates machine learning in a way that enables both predictive and prescriptive analytics. The study is divided into two parts. First, a generic predictive model has been developed to identify the at-risk students across a wide variety of courses. After generating the predictive model, model explainability using anchors has demonstrated the reasoning behind predictive models to enable transparency of the predictive models and increase students’ trust as they interact with the LAD. Machine learning models used in this study have implemented prescriptive components by prioritizing which changes in learning behaviors and which learning strategies adopted by a student will most likely translate into favorable results. Second, a LAD is developed. The dashboard provides visualizations that incorporates graphical and statistical information of online behavioral student patterns as they engage with the coursework. An online student survey that gauged LAD effectiveness for its usefulness and the motivational impact of its prescriptive output to better engage students with the coursework has shown promising results. The LAD design, as far as we know, is the first in the learning analytics domain that has combined all three analytics, namely descriptive, predictive, and prescriptive. This thesis has investigated an active area of research and has paved the way for more meaningful LAD design and implementation, thereby contributing to both theory and practice.
- ItemPrediction of students' performance through data mining : a thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science, Massey University, Auckland, New Zealand(Massey University, 2020) Umer Baloch, RahilaGovernment funding to higher education providers is based upon graduate completions rather than on student enrollments. Therefore, unfinished degrees or delayed degree completions are major concerns for higher education providers since these problems impact their long-term financial security and overall cost-effectiveness. Therefore, providers need to develop strategies for improving the quality of their education to ensure increased enrollment and retention rates. This study uses predictive modeling techniques for assisting providers with real-time identification of struggling students in order to improve their course retention rates. Predictive models utilizing student demographic and other behavioral data gathered from an institutional learning platform have been developed to predict whether a student should be classed as at-risk of failing a course or not. Identification of at-risk students will help instructors take proactive measures, such as offering students extra help and other timely supports. The outcomes of this study will, therefore, provide a safety net for students as well as education providers in improving student engagement and retention rates. The computational approaches adopted in this study include machine learning techniques in combination with educational process mining methods. Results show that multi-purpose predictive models that were designed to operate across a variety of different courses could not be generalized due to the complexity and diversity of the courses. Instead, a meta-learning approach for recommending the best classification algorithms for predicting students’ performance is demonstrated. The study reveals how process-unaware learning platforms that do not accurately reflect ongoing learner interactions can enable the discovery of student learning practices. It holds value in reconsidering predictive modeling techniques by supplementing the analysis with contextually-relevant process models that can be extracted from stand-alone activities of process-unaware learning platforms. This provides a prescriptive approach for conducting empirical research on predictive modeling with educational data sets. The study contributes to the fields of learning analytics and education process mining by providing a distinctive use of predictive modeling techniques that can be effectively applied to real-world data sets.