Collaborating Authors


Multi-Task Learning based Online Dialogic Instruction Detection with Pre-trained Language Models Artificial Intelligence

In this work, we study computational approaches to detect online dialogic instructions, which are widely used to help students understand learning materials, and build effective study habits. This task is rather challenging due to the widely-varying quality and pedagogical styles of dialogic instructions. To address these challenges, we utilize pre-trained language models, and propose a multi-task paradigm which enhances the ability to distinguish instances of different classes by enlarging the margin between categories via contrastive loss. Furthermore, we design a strategy to fully exploit the misclassified examples during the training stage. Extensive experiments on a real-world online educational data set demonstrate that our approach achieves superior performance compared to representative baselines.

The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.

Constructing a personalized learning path using genetic algorithms approach Artificial Intelligence

A substantial disadvantage of traditional learning is that all students follow the same learning sequence, but not all of them have the same background of knowledge, the same preferences, the same learning goals, and the same needs. Traditional teaching resources, such as textbooks, in most cases pursue students to follow fixed sequences during the learning process, thus impairing their performance. Learning sequencing is an important research issue as part of the learning process because no fixed learning paths will be appropriate for all learners. For this reason, many research papers are focused on the development of mechanisms to offer personalization on learning paths, considering the learner needs, interests, behaviors, and abilities. In most cases, these researchers are totally focused on the student's preferences, ignoring the level of difficulty and the relation degree that exists between various concepts in a course. This research paper presents the possibility of constructing personalized learning paths using genetic algorithm-based model, encountering the level of difficulty and relation degree of the constituent concepts of a course. The experimental results shows that the genetic algorithm is suitable to generate optimal learning paths based on learning object difficulty level, duration, rating, and relation degree between each learning object as elementary parts of the sequence of the learning path. From these results compared to the quality of the traditional learning path, we observed that even the quality of the weakest learning path generated by our GA approach is in a favor compared to quality of the traditional learning path, with a difference of 3.59\%, while the highest solution generated in the end resulted 8.34\% in favor of our proposal compared to the traditional learning paths.

How is AI Contributing to the Education Sector?


Artificial intelligence has entered every industry, and the educational sector is no exception. The administrative staff, management, teachers, and students are all using AI in different ways to achieve similar goals. During the last few years, AI has spread its roots much wider and deeper in this sector. Markets and Markets has predicted that the global market share of AI in education is estimated to reach $3.68 billion by 2023 at a CAGR (Compound Annual Growth Rate) of 47%. Another platform, Market Search Engine, has predicted that the share will reach $5.80 billion by 2025.

100 Best Coursera Courses & Specializations 2021


Are you looking for Best Free Coursera Courses 2021? You can earn a Coursera Certificate with Coursera free courses by applying for Coursera scholarship and by doing Coursera paid courses. You are going to get a 7-day free trial on Coursera when you join and start your very first subscription to do a Coursera Specializations for free. If you do not cancel your free trial you will be automatically transferred to paid subscription on the 8th Day. You can continue your Coursera Classes either by using Coursera App on mobile or on any other devices. This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Learn and launch your career in Data Science with these best Coursera courses. A nine-course introduction to data science developed and taught by leading instructors. Develop programs to gather, clean, analyze, and visualize data. You will get new insights into your data. Learn to apply data science methods and techniques, and acquire analytical skills. This program is designed to take beginner learners to job readiness in about eight months. Design, develop and manage cloud solutions to drive business objectives. Learn to solve real business problems. Master Excel to add a highly valuable asset to your employability portfolio.

Jointly Modeling Heterogeneous Student Behaviors and Interactions Among Multiple Prediction Tasks Artificial Intelligence

Prediction tasks about students have practical significance for both student and college. Making multiple predictions about students is an important part of a smart campus. For instance, predicting whether a student will fail to graduate can alert the student affairs office to take predictive measures to help the student improve his/her academic performance. With the development of information technology in colleges, we can collect digital footprints which encode heterogeneous behaviors continuously. In this paper, we focus on modeling heterogeneous behaviors and making multiple predictions together, since some prediction tasks are related and learning the model for a specific task may have the data sparsity problem. To this end, we propose a variant of LSTM and a soft-attention mechanism. The proposed LSTM is able to learn the student profile-aware representation from heterogeneous behavior sequences. The proposed soft-attention mechanism can dynamically learn different importance degrees of different days for every student. In this way, heterogeneous behaviors can be well modeled. In order to model interactions among multiple prediction tasks, we propose a co-attention mechanism based unit. With the help of the stacked units, we can explicitly control the knowledge transfer among multiple tasks. We design three motivating behavior prediction tasks based on a real-world dataset collected from a college. Qualitative and quantitative experiments on the three prediction tasks have demonstrated the effectiveness of our model.

Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data Machine Learning

Datasets play a critical role in shaping the perception of performance and progress in machine learning (ML)--the way we collect, process, and analyze data affects the way we benchmark success and form new research agendas (Paullada et al., 2020; Dotan & Milli, 2020). A growing appreciation of this determinative role of datasets has sparked a concomitant concern that standard datasets used for training and evaluating ML models lack diversity along significant dimensions, for example, geography, gender, and skin type (Shankar et al., 2017; Buolamwini & Gebru, 2018). Lack of diversity in evaluation data can obfuscate disparate performance when evaluating based on aggregate accuracy (Buolamwini & Gebru, 2018). Lack of diversity in training data can limit the extent to which learned models can adequately apply to all portions of a population, a concern highlighted in recent work in the medical domain (Habib et al., 2019; Hofmanninger et al., 2020). Our work aims to develop a general unifying perspective on the way that dataset composition affects outcomes of machine learning systems.

Where to find free college courses online


On the arts side of things, you can get a free four-week look (or listen!) to German and/or Italian opera. There's also the ability to study the classic literature of the 19th century. The Georgia Institute of Technology, or Georgia Tech, is one of the United States' leading research universities, offering a technologically based education to over 25,000 students. You can join those thousands of scholars online through edX's GTx portal. GTx offers a large selection of free technology-based classes and courses that range from five-week courses you can study part-time at home, as well as paid-for options, on topics such as human-computer interaction and an introduction to Python programming, that can lead to a professional qualification, or even a master's degree.

Personalized Education in the AI Era: What to Expect Next? Artificial Intelligence

The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to ultimately meet her desired goal. This concept emerged several years ago and is being adopted by a rapidly-growing number of educational institutions around the globe. In recent years, the boost of artificial intelligence (AI) and machine learning (ML), together with the advances in big data analysis, has unfolded novel perspectives to enhance personalized education in numerous dimensions. By taking advantage of AI/ML methods, the educational platform precisely acquires the student's characteristics. This is done, in part, by observing the past experiences as well as analyzing the available big data through exploring the learners' features and similarities. It can, for example, recommend the most appropriate content among numerous accessible ones, advise a well-designed long-term curriculum, connect appropriate learners by suggestion, accurate performance evaluation, and the like. Still, several aspects of AI-based personalized education remain unexplored. These include, among others, compensating for the adverse effects of the absence of peers, creating and maintaining motivations for learning, increasing diversity, removing the biases induced by the data and algorithms, and the like. In this paper, while providing a brief review of state-of-the-art research, we investigate the challenges of AI/ML-based personalized education and discuss potential solutions.

Educational Content Linking for Enhancing Learning Need Remediation in MOOCs Artificial Intelligence

Since its introduction in 2011, there have been over 4000 MOOCs on various subjects on the Web, serving over 35 million learners. MOOCs have shown the ability to democratize knowledge dissemination and bring the best education in the world to every learner. However, the disparate distances between participants, the size of the learner population, and the heterogeneity of the learners' backgrounds make it extremely difficult for instructors to interact with the learners in a timely manner, which adversely affects learning experience. To address the challenges, in this thesis, we propose a framework: educational content linking. By linking and organizing pieces of learning content scattered in various course materials into an easily accessible structure, we hypothesize that this framework can provide learners guidance and improve content navigation. Since most instruction and knowledge acquisition in MOOCs takes place when learners are surveying course materials, better content navigation may help learners find supporting information to resolve their confusion and thus improve learning outcome and experience. To support our conjecture, we present end-to-end studies to investigate our framework around two research questions: 1) can manually generated linking improve learning? 2) can learning content be generated with machine learning methods? For studying the first question, we built an interface that present learning materials and visualize the linking among them simultaneously. We found the interface enables users to search for desired course materials more efficiently, and retain more concepts more readily. For the second question, we propose an automatic content linking algorithm based on conditional random fields. We demonstrate that automatically generated linking can still lead to better learning, although the magnitude of the improvement over the unlinked interface is smaller.