An Examination of Personality Traits as a Predictor of the Use of Self-Regulated Learning Strategies

Each learner brings a unique mix of personality traits, preferences, and talents to the educational setting. These factors can influence the extent to which learners are able to effectively deploy skills and strategies to achieve their academic goals. Gaining a deeper awareness of how specific personality traits play a role in the choice and deployment of SRL strategies provides opportunities to anticipate which learners might be ineffective self-regulators. Doing so would enable instructional designers, educators, or higher education administrators to better plan and deliver effective educational experiences for a wide range of learners. The purpose of this study was to investigate the extent to which the use of SRL strategies was impacted by learner differences in Big Five personality traits. This mixed methods study examined the potential of utilizing the Big Five Inventory classification as a predictor of self-regulated strategy use. Specifically, the study investigated the relationship between the existence of openness, conscientiousness, extraversion, agreeableness, and neuroticism traits as possible predictors of learner use of SRL strategies. From a pool of approximately 4,200 graduate students, nearly 360 surveys were completed. Survey participants were asked to respond to five demographic items, 44 Big Five Inventory items, and 24 OSLQ items. The study indicated that personality trait classification does have an impact on the overall use of SRL strategies, as well as on the deployment of specific subscales within the OSLQ. Conscientiousness was the strongest predictor of overall OSLQ score, and agreeableness was shown as a significant predictor of each of the six OSLQ subscales. Contrary to the researcher’s initial hypothesis, exhibiting high neuroticism was not shown to have a significant negative impact on overall OSLQ scores. Results also indicated slight differences in overall OSLQ score based on personality trait and number of online courses taken. Finally, comments received during follow-up interviews lent support to statistical findings related to SRL strategy use across personality trait categories

Engaging Learners in Synchronous Online Training Using Facial Expression Analysis Technology

The rapid growth of digitalization and the rise in the number of remote work environments have amplified the importance of remote training using online learning platforms. The effectiveness of these online trainings heavily relies on various factors such as training content, methods and duration, trainer skills, and the technology platforms used in trainings. In addition to these internal, and generally, controllable factors, various uncontrollable factors also have a significant impact on the overall learning experience and outcomes. Consideration of cultural, generational, linguistic factors in addition to gender and race-related factors is essential in increasing the effectiveness of online training efforts. The purpose of this study is to investigate how facial recognition technology can aid in creating an engaging learning experience for diverse participants in online synchronous training. In particular, the study explores factors affecting the learning experience through an empirical analysis. Incorporating learners’ feedback, practical design methods are delineated to form a highly inclusive and engaging learning model using facial expressions analysis

Adaptive intelligent personalised learning (AIPL) environment

As individuals the ideal learning scenario would be a learning environment tailored just for how we like to learn, personalised to our requirements. This has previously been almost inconceivable given the complexities of learning, the constraints within the environments in which we teach, and the need for global repositories of knowledge to facilitate this process. Whilst it is still not necessarily achievable in its full sense this research project represents a path towards this ideal.In this thesis, findings from research into the development of a model (the Adaptive Intelligent Personalised Learning (AIPL)), the creation of a prototype implementation of a system designed around this model (the AIPL environment) and the construction of a suite of intelligent algorithms (Personalised Adaptive Filtering System (PAFS)) for personalised learning are presented and evaluated. A mixed methods approach is used in the evaluation of the AIPL environment. The AIPL model is built on the premise of an ideal system being one which does not just consider the individual but also considers groupings of likeminded individuals and their power to influence learner choice. The results show that: (1) There is a positive correlation for using group-learning-paradigms. (2) Using personalisation as a learning aid can help to facilitate individual learning and encourage learning on-line. (3) Using learning styles as a way of identifying and categorising the individuals can improve their on-line learning experience. (4) Using Adaptive Information Retrieval techniques linked to group-learning-paradigms can reduce and improve the problem of mis-matching. A number of approaches for further work to extend and expand upon the work presented are highlighted at the end of the Thesis

Chinese learners and computer assisted language learning: a study of learning styles, learner attitudes and the effectiveness of CALL in Chinese higher education

E-leaming has become a staple diet in many learners’ academic lives in higher education institutions all around the world. This study did not follow the techno- centric standpoint and the comparative research design tradition in this field; instead, it focused on how learners’ learning styles and attitudes interact with the effectiveness of E-leaming implementation in the field of foreign language learning. The research was set in the author’s home institution—a comprehensive university in mainland China, where the first- and second-year undergraduate students who were studying a compulsory English course were surveyed from 2003 to 2004. For this course, the College of Foreign Languages developed an online computer-assisted language learning (CALL) environment—NCE Online which was the basis of this investigation. The author’s former colleagues helped organise the distribution and collection of 4 questionnaires and 9 groups of student interviews over one academic year. A total of 5258 participants completed the first questionnaire in 2003 while the numbers of participants who completed the other questions varied from around 200 to 700. To understand data from the learners in more depth, the language teachers and NCE Online developers were also surveyed with a questionnaire and individual interviews. The results showed that the learners had very positive attitudes towards the use of computer technologies in their study, and that there was an evident tendency to expect an increasing proportion of CALL elements as the students progressed in their English study. Despite these positive attitudes, what was equally clear was that there were still more students who preferred to have traditional classroom learning as their main learning mode, and they did not think of the E- leaming materials available as more effective than the traditional ones. Meanwhile, their teachers’ attitudes and the University’s policies also played an important role in influencing learners’ attitudes and actual behaviour toward the CALL system. In addition, the research revealed that Chinese learners have learning styles distinct from their peers in the west, which suggests that a CALL environment for Chinese learners should not follow blindly the much-advocated constructivist design model in the west. Reconsideration of both the ideals of foreign language teaching methodologies and E-leaming pedagogies, which originated mainly in Europe and Northern America, needs to take place before the design of a CALL system for Chinese learners. The implications of this research were therefore discussed to begin just such a rethinking of CALL implementations in Chinese higher education

A Framework for Students Profile Detection

Some of the biggest problems tackling Higher Education Institutions are students’ drop-out and academic disengagement. Physical or psychological disabilities, social-economic or academic marginalization, and emotional and affective problems, are some of the factors that can lead to it. This problematic is worsened by the shortage of educational resources, that can bridge the communication gap between the faculty staff and the affective needs of these students. This dissertation focus in the development of a framework, capable of collecting analytic data, from an array of emotions, affects and behaviours, acquired either by human observations, like a teacher in a classroom or a psychologist, or by electronic sensors and automatic analysis software, such as eye tracking devices, emotion detection through facial expression recognition software, automatic gait and posture detection, and others. The framework establishes the guidance to compile the gathered data in an ontology, to enable the extraction of patterns outliers via machine learning, which assist the profiling of students in critical situations, like disengagement, attention deficit, drop-out, and other sociological issues. Consequently, it is possible to set real-time alerts when these profiles conditions are detected, so that appropriate experts could verify the situation and employ effective procedures. The goal is that, by providing insightful real-time cognitive data and facilitating the profiling of the students’ problems, a faster personalized response to help the student is enabled, allowing academic performance improvements

The guiding process in discovery hypertext learning environments for the Internet

Hypertext is the dominant method to navigate the Internet, providing user freedom and control over navigational behaviour. There has been an increase in converting existing educational material into Internet web pages but weaknesses have been identified in current WWW learning systems. There is a lack of conceptual support for learning from hypertext, navigational disorientation and cognitive overload. This implies the need for an established pedagogical approach to developing the web as a teaching and learning medium. Guided Discovery Learning is proposed as an educational pedagogy suitable for supporting WWW learning. The hypothesis is that a guided discovery environment will produce greater gains in learning and satisfaction, than a non-adaptive hypertext environment. A second hypothesis is that combining concept maps with this specific educational paradigm will provide cognitive support. The third hypothesis is that student learning styles will not influence learning outcome or user satisfaction. Thus, providing evidence that the guided discovery learning paradigm can be used for many types of learning styles. This was investigated by the building of a guided discovery system and a framework devised for assessing teaching styles. The system provided varying discovery steps, guided advice, individualistic system instruction and navigational control. An 84 subject experiment compared a Guided discovery condition, a Map-only condition and an Unguided condition. Subjects were subdivided according to learning styles, with measures for learning outcome and user satisfaction. The results indicate that providing guidance will result in a significant increase in level of learning. Guided discovery condition subjects, regardless of learning styles, experienced levels of satisfaction comparable to those in the other conditions. The concept mapping tool did not appear to affect learning outcome or user satisfaction. The conclusion was that using a particular approach to guidance would result in a more supportive environment for learning. This research contributes to the need for a better understanding of the pedagogic design that should be incorporated into WWW learning environments, with a recommendation for a guided discovery approach to alleviate major hypertext and WWW issues for distance learning

Explainable AI (XAI): Improving At-Risk Student Prediction with Theory-Guided Data Science, K-means Classification, and Genetic Programming

This research explores the use of eXplainable Artificial Intelligence (XAI) in Educational Data Mining (EDM) to improve the performance and explainability of artificial intelligence (AI) and machine learning (ML) models predicting at-risk students. Explainable predictions provide students and educators with more insight into at-risk indicators and causes, which facilitates instructional intervention guidance. Historically, low student retention has been prevalent across the globe as nations have implemented a wide range of interventions (e.g., policies, funding, and academic strategies) with only minimal improvements in recent years. In the US, recent attrition rates indicate two out of five first-time freshman students will not graduate from the same four-year institution within six years. In response, emerging AI research leveraging recent advancements in Deep Learning has demonstrated high predictive accuracy for identifying at-risk students, which is useful for planning instructional interventions. However, research suggested a general trade-off between performance and explainability of predictive models. Those that outperform, such as deep neural networks (DNN), are highly complex and considered black boxes (i.e., systems that are difficult to explain, interpret, and understand). The lack of model transparency/explainability results in shallow predictions with limited feedback prohibiting useful intervention guidance. Furthermore, concerns for trust and ethical use are raised for decision-making applications that involve humans, such as health, safety, and education. To address low student retention and the lack of interpretable models, this research explored the use of eXplainable Artificial Intelligence (XAI) in Educational Data Mining (EDM) to improve instruction and learning. More specifically, XAI has the potential to enhance the performance and explainability of AI/ML models predicting at-risk students. The scope of this study includes a hybrid research design comprising: (1) a systematic literature review of XAI and EDM applications in education; (2) the development of a theory-guided feature selection (TGFS) conceptual learning model; and (3) an EDM study exploring the efficacy of a TGFS XAI model. The EDM study implemented K-Means Classification for explorative (unsupervised) and predictive (supervised) analysis in addition to assessing Genetic Programming (GP), a type of XAI model, predictive performance, and explainability against common AI/ML models. Online student activity and performance data were collected from a learning management system (LMS) from a four-year higher education institution. Student data was anonymized and protected to ensure data privacy and security. Data was aggregated at weekly intervals to compute and assess the predictive performance (sensitivity, recall, and f-1 score) over time. Mean differences and effect sizes are reported at the .05 significance level. Reliability and validity are improved by implementing research best practices

VIP: A UNIFYING FRAMEWORK FOR COMPUTATIONAL EYE-GAZE RESEARCH

Ph.DDOCTOR OF PHILOSOPH