<div>Today, machine learning models and Deep Neural Networks (DNNs) are prevalent in various areas. Also, educational Artificial Intelligence (AI) is drawing increasing attention</div><div>with the rapid development of online learning platforms. Researchers explore different types of educational AI to improve students’ learning performance and experience in online classes. Educational AIs can be categorized into “interactive” and “predictive.” Interactive AIs answer simple course questions for students, such as the due day of homework and the final project’s minimum page requirement. Predictive educational AIs play a role in predicting students’ learning states. Instructors can adjust the learning content based on the students’ learning states. However, most AIs are not evaluated in an actual class setting. Therefore, we want to evaluate the effects of a state-of-the-art educational AI model, BKT (Bayesian Knowledge Tracing)-LSTM(Long Short-Term Memory), on students’ learning performance in an actual class setting. Data came from the course CNIT 25501, a large introductory Java program?ming class at Purdue University. Participants were randomly separated into the control and experimental groups (AI-group). Weekly quizzes measured participants’ learning performance. Pre-quiz and base quizzes estimated participants’ prior knowledge levels. Using BKT-LSTM, participants in the experimental group had questions from the knowledge that they were most lacking. However, participants in the control group had questions from randomly picked knowledge. The results suggested that both the experimental and control groups had lower scores in review quizzes than in base quizzes. However, the score difference between base quizzes and review quizzes for the experimental group was more often significantly different (three quizzes) compared to the control group (two quizzes), demonstrating the predictive capability of BKT-LSTM to some extent. Initially, we expected that BKT-LSTM would enhance students’ learning performance. However, in post-quiz, participants in the control group had significantly higher scores than those in the experimental group. The result suggested that continuous complex questions may negatively affect students’ learning initiatives. On the contrary, relatively easy questions may improve their learning initiatives.</div>

<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>

abstract: Languages, specially gestural and sign languages, are best learned in immersive environments with rich feedback. Computer-Aided Language Learning (CALL) solu- tions for spoken languages have successfully incorporated some feedback mechanisms, but no such solution exists for signed languages. Computer Aided Sign Language Learning (CASLL) is a recent and promising field of research which is made feasible by advances in Computer Vision and Sign Language Recognition(SLR). Leveraging existing SLR systems for feedback based learning is not feasible because their decision processes are not human interpretable and do not facilitate conceptual feedback to learners. Thus, fundamental research is needed towards designing systems that are modular and explainable. The explanations from these systems can then be used to produce feedback to aid in the learning process. In this work, I present novel approaches for the recognition of location, movement and handshape that are components of American Sign Language (ASL) using both wrist-worn sensors as well as webcams. Finally, I present Learn2Sign(L2S), a chat- bot based AI tutor that can provide fine-grained conceptual feedback to learners of ASL using the modular recognition approaches. L2S is designed to provide feedback directly relating to the fundamental concepts of ASL using an explainable AI. I present the system performance results in terms of Precision, Recall and F-1 scores as well as validation results towards the learning outcomes of users. Both retention and execution tests for 26 participants for 14 different ASL words learned using learn2sign is presented. Finally, I also present the results of a post-usage usability survey for all the participants. In this work, I found that learners who received live feedback on their executions improved their execution as well as retention performances. The average increase in execution performance was 28% points and that for retention was 4% points. / Dissertation/Thesis / Doctoral Dissertation Engineering 2020