Interactive online learning environments, represented by Massive AI-empowered Courses (MAIC), leverage LLM-driven multi-agent systems to transform passive MOOCs into dynamic, text-based platforms, enhancing interactivity through LLMs. This paper conducts an empirical study on a specific MAIC course to explore three research questions about dropouts in these interactive online courses: (1) What factors might lead to dropouts? (2) Can we predict dropouts? (3) Can we reduce dropouts? We analyze interaction logs to define dropouts and identify contributing factors. Our findings reveal strong links between dropout behaviors and textual interaction patterns. We then propose a course-progress-adaptive dropout prediction framework (CPADP) to predict dropouts with at most 95.4% accuracy. Based on this, we design a personalized email recall agent to re-engage at-risk students. Applied in the deployed MAIC system with over 3,000 students, the feasibility and effectiveness of our approach have been validated on students with diverse backgrounds.

Angel Cortez was ready for a change. Cortez, 43, is a Mexican immigrant who has worked in agriculture, landscaping and restaurants since he arrived in California more than 25 years ago. But he said a workplace injury nearly a decade ago has made physical labor -- jobs requiring him to stand or walk for long periods -- exceedingly painful. He has been looking to transition into jobs he could do primarily while seated. But his options felt limited: He has a high school education from Mexico, but doesn't speak English fluently and wasn't comfortable using a computer.

Creating and managing your own website may not be a huge problem for large companies. However, it is for people who are just starting a business, freelancers or smaller companies. Without in-depth know-how or the right tools, the process is quite time-consuming and complicated. Squarespace offers a special all-in-one solution for this. The user-friendly platform not only helps you to create an appealing web design, but also assists you with administrative issues, such as the creation of customer invoices – all from a single source.

As online courses become the norm in the higher-education landscape, investigations into student performance between students who take online vs on-campus versions of classes become necessary. While attention has been given to looking at differences in learning outcomes through comparisons of students' end performance, less attention has been given in comparing students' engagement patterns between different modalities. In this study, we analyze a heterogeneous knowledge graph consisting of students, course videos, formative assessments and their interactions to predict student performance via a Graph Convolutional Network (GCN). Using students' performance on the assessments, we attempt to determine a useful model for identifying at-risk students. We then compare the models generated between 5 on-campus and 2 fully-online MOOC-style instances of the same course. The model developed achieved a 70-90\% accuracy of predicting whether a student would pass a particular problem set based on content consumed, course instance, and modality.

The aim of this paper is to categorize and describe different types of learners in massive open online courses (MOOCs) by means of a subgroup discovery approach based on MapReduce. The final objective is to discover IF-THEN rules that appear in different MOOCs. The proposed subgroup discovery approach, which is an extension of the well-known FP-Growth algorithm, considers emerging parallel methodologies like MapReduce to be able to cope with extremely large datasets. As an additional feature, the proposal includes a threshold value to denote the number of courses that each discovered rule should satisfy. A post-processing step is also included so redundant subgroups can be removed. The experimental stage is carried out by considering de-identified data from the first year of 16 MITx and HarvardX courses on the edX platform. Experimental results demonstrate that the proposed MapReduce approach outperforms traditional sequential subgroup discovery approaches, achieving a runtime that is almost constant for different courses. Additionally, thanks to the final post-processing step, only interesting and not-redundant rules are discovered, hence reducing the number of subgroups in one or two orders of magnitude. Finally, the discovered subgroups are easily used by courses' instructors not only for descriptive purposes but also for additional tasks such as recommendation or personalization.

Online learning and MOOCs have become increasingly popular in recent years, and the trend will continue, given the technology boom. There is a dire need to observe learners' behavior in these online courses, similar to what instructors do in a face-to-face classroom. Learners' strategies and activities become crucial to understanding their behavior. One major challenge in online courses is predicting and preventing dropout behavior. While several studies have tried to perform such analysis, there is still a shortage of studies that employ different data streams to understand and predict the drop rates. Moreover, studies rarely use a fully online team-based collaborative environment as their context. Thus, the current study employs an online longitudinal problem-based learning (PBL) collaborative robotics competition as the testbed. Through methodological triangulation, the study aims to predict dropout behavior via the contributions of Discourse discussion forum 'activities' of participating teams, along with a self-reported Online Learning Strategies Questionnaire (OSLQ). The study also uses Qualitative interviews to enhance the ground truth and results. The OSLQ data is collected from more than 4000 participants. Furthermore, the study seeks to establish the reliability of OSLQ to advance research within online environments. Various Machine Learning algorithms are applied to analyze the data. The findings demonstrate the reliability of OSLQ with our substantial sample size and reveal promising results for predicting the dropout rate in online competition.

Big data and machine learning tools have jointly empowered humans in making data-driven decisions. However, many of them capture empirical associations that might be spurious due to confounding factors and subgroup heterogeneity. The famous Simpson's paradox is such a phenomenon where aggregated and subgroup-level associations contradict with each other, causing cognitive confusions and difficulty in making adequate interpretations and decisions. Existing tools provide little insights for humans to locate, reason about, and prevent pitfalls of spurious association in practice. We propose VISPUR, a visual analytic system that provides a causal analysis framework and a human-centric workflow for tackling spurious associations. These include a CONFOUNDER DASHBOARD, which can automatically identify possible confounding factors, and a SUBGROUP VIEWER, which allows for the visualization and comparison of diverse subgroup patterns that likely or potentially result in a misinterpretation of causality. Additionally, we propose a REASONING STORYBOARD, which uses a flow-based approach to illustrate paradoxical phenomena, as well as an interactive DECISION DIAGNOSIS panel that helps ensure accountable decision-making. Through an expert interview and a controlled user experiment, our qualitative and quantitative results demonstrate that the proposed "de-paradox" workflow and the designed visual analytic system are effective in helping human users to identify and understand spurious associations, as well as to make accountable causal decisions.

A new kind of AI job is emerging--and it pays six-figure salaries and doesn't require a degree in computer engineering, or even advanced coding skills. With the rise in generative artificial intelligence, a host of companies are now looking to hire "prompt engineers" who are tasked with training the emerging crop of AI tools to deliver more accurate and relevant responses to the questions real people are likely to pose. Some of these jobs can even pay up to $335,000 a year. Anna Bernstein, a 29-year-old prompt engineer at generative AI firm Copy.ai in New York, is one of the few people already working in this new field. Her role involves writing text-based prompts that she feeds into the back end of AI tools so they can do things such as generate a blog post or sales email with the proper tone and accurate information.

The current demand for pursuing a career in the field of AI and computer vision is at an all-time high. As with various other aspects of the digital realm, a comprehensive understanding of these areas can be attained through online resources. It is often presumed that the quality of online courses could be better than traditional methods, such as college-level programs, practical experience in the field, and offline studies. However, online learning has advanced beyond this misconception. Paid and free online courses can teach fundamental computer vision principles and specific elements of the discipline.

Machine learning and deep learning have become essential skills for researchers in many fields, from computer science to biology to finance. With the explosion of data and the increasing demand for data-driven insights, the ability to understand and apply machine learning and deep learning techniques has become a critical advantage for researchers. However, learning these skills can be challenging, especially for those who are new to the field. In this article, I will share some of the top resources that can help researchers learn machine learning and deep learning effectively. One of the best ways to learn machine learning and deep learning is through online courses. There are many excellent courses available, including those from top universities like Stanford, MIT, and Carnegie Mellon.