This mixed method study examined how integration of synchronous Web-based desktop conferencing affects undergraduate distance learning students' educational experience. Specifically, it compared students in one distance learning section of an introductory technology course delivered through a synchronous-enhanced mode (employing both synchronous and asynchronous tools) with students in a second section of the same distance learning course provided in the asynchronous only mode to examine variations in cognitive presence, social presence, and teaching presence through the lens of the Community of Inquiry model (Garrison, Anderson, and Archer, 2000). Additionally, this study measured perceived learning, learner-instructor interaction, and learner-learner interaction to determine whether observable differences between the two groups occurred. Benefits of the synchronous-enhanced distance learning environment predicted in this study included a statistically significantly stronger sense of cognitive presence, social presence, and teaching presence. It was also hypothesized that the synchronous-enhanced distance learning environment would yield statistically significantly higher reported perceived learning and interaction in contrast to the asynchronous condition. This study qualitatively explored how students perceived their experiences in synchronous-enhanced and asynchronous distance learning environments through focus group interviews and deductive analysis. Results of the analysis revealed no statistically significant differences between the synchronous-enhanced and asynchronous groups in measuring cognitive presence, social presence, teaching presence, perceived learning, learner-instructor interaction, and learner-learner interaction. Qualitative results evidenced higher frequencies in the synchronous-enhanced group with regard to clarification, collaboration, direct instruction, and interaction.

<div>With the advent of Machine Learning and Deep Learning models, many avenues of development have opened. Today, these technologies are being leveraged to perform a wide variety of tasks that were otherwise not possible with traditional systems. The power of Machine Learning and Artificial Intelligence makes it possible to compute very complicated tasks at near real-time speeds. To provide an example, Machine Learning models are used extensively in the retail industry to predict and analyze critical parameters such as sales, promotions, customer behavior, recommendations, and offers.</div><div><br></div><div><br></div><div>Today, it is increasingly common to observe AI being used across many of the biggest domains such as Health, Environment, Military, and Business. Artificial Intelligence being used in educational settings has thus been a growing field of focus and study. For example, conversational AI being deployed to act as virtual tutors to answer student questions and concerns. Additionally, there is a fill-the-hole type of AIs that will help students learn tasks such as coding by either showing them how to do it or by predicting where the student might go wrong and suggesting preemptive corrective steps. </div><div><br></div><div>As described, a great deal of literature exists about the use of Deep Learning and Machine Learning models in education. However, the existing tools and models act as external appendages that add to the course structure, thereby altering it. This proposed study introduces a Bayesian Knowledge Transfer model based on the Long Short Term Memory structure (BKT-LSTM) utilized in a live STEM (Science, Technology, Engineering, and Mathematics) classroom. The model discovers individual student learning profiles based on past quiz performance and customizes future quizzes based on the learned patterns. The BKT-LSTM model works in tandem with the existing course curriculum and only tests those knowledge items that have already been covered in the classroom. The model does not change the course structure but rather aims to improve the student’s learning experience by focusing on areas of the student's knowledge that require more practice in learning. </div><div><br></div><div><br></div><div>Within a live STEM classroom, the BKT-LSTM model acts as a herald of change in the way students interact with the curriculum, even though no major changes are observed in the course structure. Students interacting with the model are subjected to quizzes with questions that target the individual student’s lack of learning in particular knowledge areas. Thus, students can be expected to perceive the change as unwelcoming due to the increasing difficulty in subsequent quizzes. Regardless, the study focuses on measuring the learning performance of the students. Do the students learn more in the new system? Another focus of the study is the student’s perception of engagement while interacting with the BKT-LSTM model. The effectiveness of the new educational process is determined not only by increased student learning performance, but also by the student’s perception of engagement while interacting with the model. Are the students enjoying the new experience? Do the students feel like they are learning something? Another important factor was also studied, that is learning performance of students interacting with the BKT-LSTM. </div><div><br></div>

This dissertation presents an empirical investigation of learning from online courses. The current dissertation examined student participation, using Arbaugh et al.'s Community of Inquiry (CoI) survey instrument and Biggs et al.'s revised version of the Study Process Questionnaire (R-SPQ-2F) to determine CoI influences on learning from the students' perspective. This study is in response to Rourke and Kanuka's call to provide further empirical evidence about CoI conceptual framework connections to deep and meaningful learning. The purpose of this study was to examine the impact of the elements of CoI, cognitive, social, and teaching presences and students' learning approaches to students' perceived learning. Students enrolled in traditional, online, and, blended courses during the 2016 spring semester at a southwestern university participated in a web-based survey. Structural equation modeling was used to test the indirect effects between the elements of CoI, learning approaches, and perceived learning. Student's deep approach to learning was found to have an indirect effect between cognitive presence and perceived learning. However, this study's findings, when the CoI framework was viewed in its entirety, failed to provide evidence to simulate deep and meaningful learning.

This thesis explores the extent to which the pedagogical intentions of a social science subject introductory workshop at an International Baccalaureate (IB) Diploma school align with the participating students’ experienced learning. The purpose is to gain an in-depth understanding of how well the IB faculty were able to implement the IB’s Approaches to Learning (ATL), a comprehensive pedagogical initiative. The research is guided by the question of the extent to which the workshop’s intended learning outcomes, constructed by the social science faculty at the particular IB school in focus, correspond with the perceived learning experienced by the students attending the workshop. The study initially introduces a theoretical framework including philosophy of the IB and the ATL initiative. It also describes the school setting and the teacher and student participants. Secondly, previous research is presented in order to situate the study in an academic context. Furthermore, the thesis discusses and evaluates the methods used. The results of the study indicate that the participating students’ understanding of the social science subjects’ interdisciplinary nature aligns well with the teachers’ intentions, and that the students are able to grasp and critically engage with the workshop’s focus on global issues in a local context. However, student lack understanding of the relationship between social science theories and the workshop’s case study which indicate a lack of implementation of the IB:s core pedagogical approach.

Within the last decade there has been a significant increase in the range of Social Media tools that have become available. This has led to a significant increase in the use and popularity of Social Media within many aspects of everyday life, particularly within the UK and US. One of the areas in which there has been a rise in the use of Social Media is within Higher Education (HE). Within HE there have been reports that Social Media has been successfully utilized for teaching and learning, particularly in the case of blogs. Despite reportedly successful usage there has to date been relatively few empirical studies which have explored whether the use of blogs within teaching and learning leads to an increase in perceived learning by students. This research study therefore provides an empirical study of perceived learning by students when using blogs within teaching and learning in UK and US HE. This research study adopts a post positivist research approach and a quantitative research design method. Questionnaires have been utilised in order to explore student views of perceived learning when using blogs as a tool for HE teaching and learning within the UK and US. This study provides a framework for student use of blogs within HE teaching and learning and explores whether the use of blogs in this way leads to greater levels of perceived learning amongst students. The results of this research are analysed using PLS-SEM and have shown that the successful use of blogs for teaching and learning is complex. The results have demonstrated that students do report higher degrees of learning from using blogs within teaching and learning, however, this is influenced by the perceptions students hold relating to digital technology, teaching and learning, previous experience and expectations of blogging. The results of this study have implications for both HE teachers and HE students and provides a framework which can be used to help ensure the successful use of blogs when utilised for HE teaching and learning within the UK and US in the future.

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