8 research outputs found
Recommended from our members
INTELLIGENT TUTORING SYSTEMS, PEDAGOGICAL AGENT DESIGN, AND HISPANIC ENGLISH LANGUAGE LEARNERS
According to the most recent data from the National Center of Education Statistics (NCES) there were approximately 5 million English Language Learners (ELLs) in the U.S. public schools in the Fall of 2016, representing about 10% of the student population (2019). Spanish is the primary language for most ELL students, by a large margin. As a group, ELLs have faced a deeply rooted and persistent math achievement gap (U.S. Department of Education, 2015). Despite research indicating that intelligent tutors and animated pedagogical agents enhance learning, many tutors are not designed with ELLs in mind. As a result, Hispanic ELL students may experience difficulty accessing the relevant content when using a tutor. This mixed-method research investigates how a tutor can reach Hispanic ELL students, based on the social and cultural Identity framework of the Figured Worlds Theory by Holland et al., (1998). Students will socially and culturally engage with their animated pedagogical agents constructing figured worlds of learning and connection that have the power to shape the students’ senses of themselves as learners of math. This study investigates how Hispanic ELL students perceive the utility of and relate to a learning companion (LC) design. Data was examined from 76 middle school students interacting with a math tutor, MathSpring. The findings indicate that ELL students find the MathSpring LC more useful and helpful than do non-ELL students and the ELL students designed LCs that looked more like themselves than did the non-ELL students. The findings also indicate that students formed ‘She/Me Connection’ and ‘She is Like Me’ figured worlds
FACE READERS: The Frontier of Computer Vision and Math Learning
The future of AI-assisted individualized learning includes computer vision to inform intelligent tutors and teachers about student affect, motivation and performance. Facial expression recognition is essential in recognizing subtle differences when students ask for hints or fail to solve problems. Facial features and classification labels enable intelligent tutors to predict students’ performance and recommend activities. Videos can capture students’ faces and model their effort and progress; machine learning classifiers can support intelligent tutors to provide interventions. One goal of this research is to support deep dives by teachers to identify students’ individual needs through facial expression and to provide immediate feedback. Another goal is to develop data-directed education to gauge students’ pre-existing knowledge and analyze real-time data that will engage both teachers and students in more individualized and precision teaching and learning. This paper identifies three phases in the process of recognizing and predicting student progress based on analyzing facial features: Phase I: Collecting datasets and identifying salient labels for facial features and student attention/engagement; Phase II: Building and training deep learning models of facial features; and Phase III: Predicting student problem-solving outcome. © 2023 Copyright for this paper by its authors
Leveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System
In the context of building an intelligent tutoring system (ITS), which
improves student learning outcomes by intervention, we set out to improve
prediction of student problem outcome. In essence, we want to predict the
outcome of a student answering a problem in an ITS from a video feed by
analyzing their face and gestures. For this, we present a novel transfer
learning facial affect representation and a user-personalized training scheme
that unlocks the potential of this representation. We model the temporal
structure of video sequences of students solving math problems using a
recurrent neural network architecture. Additionally, we extend the largest
dataset of student interactions with an intelligent online math tutor by a
factor of two. Our final model, coined ATL-BP (Affect Transfer Learning for
Behavior Prediction) achieves an increase in mean F-score over state-of-the-art
of 45% on this new dataset in the general case and 50% in a more challenging
leave-users-out experimental setting when we use a user-personalized training
scheme
ATL-BP: a student engagement dataset and model for affect transfer learning for behavior prediction
IIS-1551572 - National Science Foundationhttp://10.0.4.85/TBIOM.2022.3210479Published versio
Parsing Pointer Movements in a Target Unaware Environment
Analysis of the movements of the Mouse pointer could lead to valuable insights into a user’s mental status in digital environments. Previous research has yielded data showing a significant link between user mental status and pointer movements[1]. However, there is currently no standardized system to detect and parse out individual targeted movements of a mouse pointer by a user in an active environment. Active analysis of mouse movements could be useful in situations where the emotional state of the user is being measured. Data was collected through the Mathspring Project including results of problems solved, the facial expressions and self-reported emotions of students, and the movements of the mouse pointer, which is the focus of this work [3]. Although a connection has been shown in previous research [1], the ability to track this in a live system is held back by the manual process for splitting the motions of the pointer. The focus of this project is the development of a generalizable system to parse these movements automatically without needing much processing power or an immense amount of training data for each time. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG
Affect-driven learning outcomes prediction in intelligent tutoring systems
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\u27 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\u27s 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
Prediction of student engagement
A major challenge for online learning is the inability of
systems to support student emotion and to maintain student
engagement. In response to this challenge, computer vision
has become an embedded feature in some instructional
applications. In this paper, we propose a video dataset
of college students solving math problems on the educational
platform MathSpring.org with a front facing camera
collecting visual feedback of student gestures. The video
dataset is annotated to indicate whether students’ attention
at specific frames is engaged or wandering. In addition,
we train baselines for a computer vision module that determines
the extent of student engagement during remote
learning. Baselines include state-of-the-art deep learning
image classifiers and traditional conditional and logistic regression
for head pose estimation. We then incorporate a
gaze baseline into the MathSpring learning platform, and
we are evaluating its performance with the currently implemented
approach.Published versio
Blinded by science?: Exploring affective meaning in students’ own words
This work addresses students’ open responses on causal attributions of their self-reported affective states. We use qualitative thematic data analysis techniques to develop a coding scheme by identifying common themes in students’ self-reported attributions. We then applied this scheme to a larger set of student reports. Analysis shows that students’ reasons for reporting a certain affect do not always align with researchers’ expectations. In particular, we discovered that a sizable group of students externalize their affect, attributing perceived difficulty of the problem and their own negativity as lying outside of themselves