This paper explores the potential use cases of WhatsApp Communities (WACs) in the realm of education. We examine the unique features of WACs, such as the capacity to manage up to 50 groups and the Announcement Channel functionality, which enables sending tailored messages to all community members. Our discussion highlights how these capabilities can enhance communication and collaboration among students, teachers, and administrators, offering valuable opportunities for virtual education. Furthermore, we emphasize the potential of WACs in promoting learner literacy and citizenship, ultimately contributing to the ongoing evolution of our education systems for the future.

A key to personalized online learning is presenting content at an appropriate difficulty level; content that is too difficult can cause frustration and content that is too easy may result in boredom. Appropriate content can improve students' engagement and learning outcome. In this research, we propose a computer vision enhanced problem selector (COVES), a deep learning model to select a personalized difficulty level for each student. A combination of visual information and traditional log data is used to predict student-problem interactions, which are then used to guide problem difficulty selection in real time. COVES was trained on a dataset of fifty-one sixth-grade students interacting with the online math tutor MathSpring. Once COVES was integrated into the tutor, its effectiveness was tested with twenty-two seventh-grade students in controlled experiments. Students who received problems at an appropriate difficulty level, based on real-time predictions of their performance, demonstrated improved engagement with the math tutor. Results indicate that COVES leads to higher mastery of math concepts, better timing, and higher scores, thus providing a positive learning experience for the participants.

This experience report details pilot deployments of the Platform for Ethics and Responsible Computing Education (PEaRCE) in undergraduate computer science (CS) courses. PEaRCE is an online learning environment designed to immerse undergraduate CS students in realistic interactive work scenarios. The simulated work scenarios illustrate how technical CS course material can be readily exploited towards the creation of appealing systems, which nevertheless can be instruments of harm to others. Students take the role of an employee within a project and are given details about the project. Students then decide whether to continue with the project immediately, or to first talk to various project stakeholders who could provide additional information about the project and its impacts. At the end of the simulation, students are provided information about the project scenario, including the potential impacts of the project and feedback on stakeholder conversation choices. We report on preliminary findings from pilot deployments of PEaRCE in undergraduate CS courses, including student feedback about PEaRCE and the simulation scenarios and discuss future directions for the design of the system and its use in future CS courses.

In this work, we propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS). By analyzing a student's face and gestures, our method predicts the outcome of a student answering a problem in an ITS from a video feed. Our work is motivated by the reasoning that the ability to predict such outcomes enables tutoring systems to adjust interventions, such as hints and encouragement, and to ultimately yield improved student learning. We collected a large labeled dataset of student interactions with an intelligent online math tutor consisting of 68 sessions, where 54 individual students solved 2,749 problems. We will release this dataset publicly upon publication of this paper. It will be available at https://www.cs.bu.edu/faculty/betke/research/learning/. Working with this dataset, our transfer-learning challenge was to design a representation in the source domain of pictures obtained “in the wild” for the task of facial expression analysis, and transferring this learned representation to the task of human behavior prediction in the domain of webcam videos of students in a classroom environment. We developed a novel facial affect representation and a user-personalized training scheme that unlocks the potential of this representation. We designed several variants of a recurrent neural network that models the temporal structure of video sequences of students solving math problems. Our final model, named ATL-BP for Affect Transfer Learning for Behavior Prediction, achieves a relative increase in mean F -score of 50 % over the state-of-the-art method on this new dataset.

Equipping an Intelligent Tutoring System (ITS) with the ability to interpret affective signals from students could potentially improve the learning experience of students by enabling the tutor to monitor the students' progress and provide timely interventions as well as present appropriate affective reactions via a virtual tutor. Most ITSs equipped with affect modeling capabilities attempt to predict the emotional state of users. However, the focus in this work is instead on trying to directly predict the learning outcomes of students from a stream of video capturing the students faces as they work on a set of math problems. Using facial features extracted from a video stream, we train classifiers to directly predict the success or failure of a student's attempt to answer a question while the student has just begun to work on the problem. In this work, we first introduce a novel dataset of student interactions with MathSpring, a popular ITS. We provide an exploratory analysis of the different problem outcome classes using typical facial action unit activations. We develop baseline models to predict the problem outcome labels of students solving math problems and discuss how early problem outcome labels can be forecasted and utilized to provide possible interventions.

A theory of cognition called embodied cognition believes that the development of thinking skills is distributed among mind, senses and the environment. Research into this field has resulted into the development of applications in different areas including Mathematics. This paper reports one part of a larger series of studies on the design and implementation of embodied cognition of Mathematics educational systems. We describe the migration and evaluation of a game called "Estimate It", a wearables-based game for teaching measurement estimation and geometry. Experts were invited to evaluate the game, for which a generally positive rating was acquired. The game's collaborative nature, its hands-on way of teaching estimation, and the incorporation of technology were seen as promising points. Infrastructure readiness, classroom control and adjustment to the new technology were areas of concern.

Students self-reported not only their emotional state, but also the causal attributions of their emotions. After coding emotions with internal references to self, and external references to the environment or domain, we examined how sub-groups of students based on internal/external attributions and above or below median performance differ in terms of their emotional state, perceptions of item difficulty, and gender.

We report on the reactions of males and female students to the presence of animated pedagogical agents that provided emotional and motivational support. One hundred high school students used agents embedded in an Intelligent Tutoring System for Mathematics and randomized controlled evaluations compared students with and without learning companions. The results indicate that affective pedagogical agents improve affective outcomes of students in general and particularly so for female students, who reported being more frustrated and less confident while solving math problems prior to using the tutoring system. We discuss issues of incorporating gender into user models and of generating responses tailored to gender.

Many children's games have a universal, cross-cultural appeal and have been played for hundreds of years, suggesting a developmental need for them. This paper presents a framework for leveraging this appeal and longevity by developing cognitive games based on popular playground games. This framework employs technology to maintain the physicality and embodied components of the game. The Prête-à-apprendre (PAP+) toolkit is used to develop an e-Textiles game for teaching concepts of estimations and number sense in Mathematics. The game consists of "hoodies" and "barrels" that communicate with each other using a low-power wireless network (Zigbee). Each "hoodie" contains an LED display to convey constraints to children while each barrel contains and Radio Frequency Identifier (RFID) reader to identify children. The game can be deployed in structured or unstructured environments. An evaluation of the pedagogical design of the game based on various intrinsic and extrinsic motivation criteria is also presented.

This qualitative study presented high school students' help-seeking behavior and how they interacted with hints while they solved math problems on an intelligent tutoring system for math.

This paper describes the use of sensors in intelligent tutors to detect students' affective states and to embed emotional support. Using four sensors in two classroom experiments the tutor dynamically collected data streams of physiological activity and students' self-reports of emotions. Evidence indicates that state-based fluctuating student emotions are related to larger, longer-term affective variables such as self-concept in mathematics. Students produced self-reports of emotions and models were created to automatically infer these emotions from physiological data from the sensors. Summaries of student physiological activity, in particular data streams from facial detection software, helped to predict more than 60% of the variance of students emotional states, which is much better than predicting emotions from other contextual variables from the tutor, when these sensors are absent. This research also provides evidence that by modifying the “context” of the tutoring system we may well be able to optimize students' emotion reports and in turn improve math attitudes.

We researched the impact of gendered pedagogical agents on student attitudes for math, motivation and achievement in math, within the context of an adaptive tutoring software for high school mathematics. Learning companions emphasize perseverance by valuing effort in challenging tasks. They are also empathetic, as they reflect students' emotional states. The results suggest that, across two studies, it was the male learning companion that produced the most positive impact on female students' state-based emotions, attitudes and learning. It is possible that girls transfer their stereotypes to the computer software.