Total: 15
Learning materials are increasingly available on the Web making them an excellent source of information for building e-Learning recommendation systems. However, learners often have difficulty finding the right materials to support their learning goals because they lack sufficient domain knowledge to craft effective queries that convey what they wish to learn. The unfamiliar vocabulary often used by domain experts creates a semantic gap between learners and experts, and also makes it difficult to map a learner's query to relevant learning materials. We build an e-Learning recommender system that uses background knowledge extracted from a collection of teaching materials and encyclopedia sources to support the refinement of learners' queries. Our approach allows us to bridge the gap between learners and teaching experts. We evaluate our method using a collection of realistic learner queries and a dataset of Machine Learning and Data Mining documents. Evaluation results show our method to outperform benchmark approaches and demonstrates its effectiveness in assisting learners to find the right materials.
We investigate the issues of undergraduate on-time graduation with respect to subject proficiencies through the lens of representation learning, training a student vector embeddings from a dataset of 8 years of course enrollments. We compare the per-semester student representations of a cohort of undergraduate Integrative Biology majors to those of graduated students in subject areas involved in their degree requirements. The result is an embedding rich in information about the relationships between majors and pathways taken by students which encoded enough information to improve prediction accuracy of on-time graduation to 95%, up from a baseline of 87.3%. Challenges to preparation of the data for student vectorization and sourcing of validation sets for optimization are discussed.
Data analysis education plays an important role in accelerating the efficient use of data analysis technologies in various domains. Not only the knowledge of statistics and machine learning, but also practical skills of deploying machine learning and data analysis techniques, are required for conducting data analysis projects in the real world. Data analysis competitions, such as Kaggle, have been considered as an efficient system for learning such skills by addressing real data analysis problems. However, current data analysis competitions are not designed for educational purposes and it is not well studied how data analysis competition platforms should be designed for enhancing educational effectiveness. To answer this research question, we built, and subsequently operated an educational data analysis competition platform called University of Big Data for several years. In this paper, we present our approaches for supporting and motivating learners and the results of our case studies. We found that providing a tutorial article is beneficial for encouraging active participation of learners, and a leaderboard system allowing an unlimited number of submissions can motivate the efforts of learners. We further discuss future directions of educational data analysis competitions.
The field of learning analytics needs to adopt a more rigorous approach for predictive model evaluation that matches the complex practice of model-building. In this work, we present a procedure to statistically test hypotheses about model performance which goes beyond the state-of-the-practice in the community to analyze both algorithms and feature extraction methods from raw data. We apply this method to a series of algorithms and feature sets derived from a large sample of Massive Open Online Courses (MOOCs). While a complete comparison of all potential modeling approaches is beyond the scope of this paper, we show that this approach reveals a large gap in dropout prediction performance between forum-, assignment-, and clickstream-based feature extraction methods, where the latter is significantly better than the former two, which are in turn indistinguishable from one another. This work has methodological implications for evaluating predictive or AI-based models of student success, and practical implications for the design and targeting of at-risk student models and interventions.
Learning analytics applies data analysis techniques to learning data in order to support students’ learning processes and to improve the quality of education. Despite the increasing attention to learning analytics for higher education, it has not been fully addressed in primary and preschool education. In this research, we apply learning analytics to preschool education to predict the continuation of learning of preschool children. Based on our hypothesis that temporal patterns in the assessment scores of development tests are effective features for prediction, we extract the temporal patterns using time-series clustering, and use them as the features of prediction models. The experimental results using a real preschool education dataset show that the use of the temporal patterns improves the predictive accuracy of future continuation of study.
Interactive training environments typically include feedback mechanisms designed to help trainees improve their performance through either guided or self-reflection. In this context, trainees are candidate teachers who need to hone their social skills as well as other pedagogical skills for their future classroom. We chose an avatar-mediated interactive virtual training system–TeachLivE–as the basic research environment to investigate the motions and embodiment of the trainees. Using tracking sensors, and customized improvements for existing gesture recognition utilities, we created a gesture database and employed it for the implementation of our real-time gesture recognition and feedback application. We also investigated multiple methods of feedback provision, including visual and haptics. The results from the conducted user studies and user evaluation surveys indicate the positive impact of the proposed feedback applications and informed body language. In this paper, we describe the context in which the utilities have been developed, the importance of recognizing nonverbal communication in the teaching context, the means of providing automated feedback associated with nonverbal messaging, and the preliminary studies developed to inform the research.
As research becomes more and more data intensive, managing this data becomes a major challenge in any organization. At university level there is seldom a unified data management system in place. The general approach to storing data in such environments is to deploy network storage. Each member can store their data organized to their own likings in their dedicated location on the network. Additionally, users tend to store data in distributed manner such as on private devices, portable storage, or public and private repositories. Adding to this complexity, it is common for university departments to have high fluctuation of staff, resulting in major loss of information and data on an employee’s departure. A common scenario then is that it is known that certain data has already been created via experiments or simulation. However, it can not be retrieved, resulting in a repetition of generation, which is costly and time-consuming. Additionally, as of recent years, publishers and funding agencies insist on storing, sharing, and reusing existing research data. We show how digital preservation can help group leaders and their employees cope with these issues, by introducing our own archival system OntoRAIS.
Concept prerequisite learning focuses on machine learning methods for measuring the prerequisite relation among concepts. With the importance of prerequisites for education, it has recently become a promising research direction. A major obstacle to extracting prerequisites at scale is the lack of large-scale labels which will enable effective data-driven solutions. We investigate the applicability of active learning to concept prerequisite learning.We propose a novel set of features tailored for prerequisite classification and compare the effectiveness of four widely used query strategies. Experimental results for domains including data mining, geometry, physics, and precalculus show that active learning can be used to reduce the amount of training data required. Given the proposed features, the query-by-committee strategy outperforms other compared query strategies.
A computer science faculty member and a philosophy faculty member collaborated in the development of a one-week introduction to ethics which was integrated into a traditional AI course. The goals were to: (1) encourage students to think about the moral complexities involved in developing accident algorithms for autonomous vehicles, (2) identify what issues need to be addressed in order to develop a satisfactory solution to the moral issues surrounding these algorithms, and (3) and to offer students an example of how computer scientists and ethicists must work together to solve a complex technical and moral problems. The course module introduced Utilitarianism and engaged students in considering the classic "Trolley Problem," which has gained contemporary relevance with the emergence of autonomous vehicles. Students used this introduction to ethics in thinking through the implications of their final projects. Results from the module indicate that students gained some fluency with Utilitarianism, including a strong understanding of the Trolley Problem. This short paper argues for the need of providing students with instruction in ethics in AI course. Given the strong alignment between AI's decision-theoretic approaches and Utilitarianism, we highlight the difficulty of encouraging AI students to challenge these assumptions.
Artificial Intelligence (AI) is becoming increasingly important. Thus, sound knowledge about the principles of AI will be a crucial factor for future careers of young people as well as for the development of novel, innovative products. Addressing this challenge, we present an ambitious 3-year project focusing on developing and implementing a professional, internationally accepted, standardized training and certification system for AI which will also be recognized by the industry and educational institutions. The approach is based on already implemented and evaluated pilot projects in the area of AI education. The project’s main goal is to train and certify teachers and mentors as well as students and young people in basic and advanced AI topics, fostering AI literacy among this target audience.
We present an interactive guided activity to introduce supervised learning by training a deep neural network (treated as a black box) to recognize "rock paper scissors" hand gestures from unconstrained images. The audience is actively involved in acquiring a varied and representative dataset, on which the rest of the activity is based. Covered concepts include the training/evaluation split, classifier evaluation, baseline accuracy, overfitting, generalization, data augmentation.
Artificial intelligence (AI) is an extremely large and complex field technically, while at the same time it captures our imagination and prompts us to explore major philosophical and ethical questions concerning humanity and human intelligence. Teaching a course that does justice to all these aspects of the field is a big challenge. However, due to the increase in computational capability with a commensurate decrease in cost, a wealth of products and materials are available that can be used to provide students with rich, meaningful, and memorable experiences within the context of a primarily technical course in AI. Toys, articles, and movies can all be used to foster student exploration of key questions in the technical, philosophical, and ethical issues of AI.
Thymio is a small, inexpensive, mass-produced mobile robot with widespread use in primary and secondary education. In order to make it more versatile and effectively use it in later educational stages, including university levels, we have expanded Thymio's capabilities by adding off-the-shelf hardware and open software components. The resulting robot, that we call Mighty Thymio, provides additional sensing functionalities, increased computing power, networking, and full ROS integration. We present the architecture of Mighty Thymio and show its application in advanced educational activities.
Computer vision, as a subfield in the general artificial intelligence (AI), is a technology can be visualized and easily found in a large number of state-of-art applications. In this project, undergraduate students performed research on a landmark recognition task using computer vision techniques. The project focused on analyzing, designing, configuring, and testing the two core components in landmark recognition: feature detection and description. The project modeled the landmark recognition system as a tour guide for visitors to the campus and evaluated the performance in the real world circumstances. By analyzing real-world data and solving problems, student’s cognitive skills and critical thinking skills were sharpened. Their knowledge and understanding in mathematical modeling and data processing were also enhanced.
The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning ex- perience, we here present abstracts of seven AI assign- ments from the 2018 session that are easily adoptable, playfully engaging, and flexible for a variety of instruc- tor needs.