ethanporcaro/eric-fulltext-meta huggingface dataset:
Translating Head Motion into Attention - Towards Processing of Student's Body-Language
Author: Raca, Mirko
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Nonverbal Communication,Attention,Motion,Video Technology,Students,Measurement Techniques,Classification,Computers

Evidence has shown that student's attention is a crucial factor for engagement and learning gain. Although it can be accurately assessed ad-hoc by an experienced teacher, continuous contact with all students in a large class is difficult to maintain and requires training for novice practitioners. We continue our previous work on investigating unobtrusive measures of body-language in order to predict student's attention during the class, and provide teachers with a support system to help them to &quot;scale-up&quot; to a large class. Our work here is focused on head-motion, by which we aim to mimic large-scale gaze tracking. By using new computer vision techniques we are able to extract head poses of all students in the video-stream from the class. After defining several measures about head motion, we checked their significance and attempted to demonstrate their value by fitting a mixture model and training support vector machines (SVM) classifiers. We show that drops in attention are reflected in a decreased intensity of head movement. We were also able to reach 61.86% correct classifications of student attention on a 3-point scale. [For complete proceedings, see ED560503.]
Data-Driven Proficiency Profiling
Author: Mostafavi, Behrooz
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Intelligent Tutoring Systems,Logical Thinking,Mathematical Logic,Mastery Learning,Mathematics Achievement,Comparative Analysis,Computation,Scores,Accuracy

Deep Thought is a logic tutor where students practice constructing deductive logic proofs. Within Deep Thought is a data-driven mastery learning system (DDML), which calculates student proficiency based on rule scores weighted by expert-decided weights in order to assign problem sets of appropriate difficulty. In this study, we designed and tested a data-driven proficiency profiler (DDPP) method in order to calculate student proficiency without expert involvement. The DDPP determines student proficiency by comparing relevant student rule scores to previous students who behaved similarly in the tutor and successfully completed it. This method was compared to the original DDML method, proficiency based on average rule scores, and proficiency based on minimum rule scores. Our testing has shown that while the DDPP has the potential to accurately calculate student proficiency, more data is required to improve it. [For complete proceedings, see ED560503.]
Who Do You Think I Am? Modeling Individual Differences for More Adaptive and Effective Instruction
Author: Allen, Laura K.
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Intelligent Tutoring Systems,Individual Differences,Natural Language Processing,Student Evaluation,Cognitive Processes,Feedback (Response)

The purpose of intelligent tutoring systems is to provide students with personalized instruction and feedback. The focus of these systems typically rests in the adaptability of the feedback provided to students, which relies on automated assessments of performance in the system. A large focus of my previous work has been to determine how natural language processing (NLP) techniques can be used to model individual differences based on students' natural language input. My proposed research will build on this work by using NLP techniques to develop stealth assessments of students' individual differences and to provide more fine-grained information about the cognitive processes in which these students are engaged throughout the learning task. Ultimately, my aim will be to combine this linguistic data with on-line system data in order to develop more robust student models within ITSs for ill-defined domains. [For complete proceedings, see ED560503.]
You Are Your Words: Modeling Students' Vocabulary Knowledge with Natural Language Processing Tools
Author: Allen, Laura K.
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Vocabulary,Knowledge Level,Models,Natural Language Processing,Essays,Prediction,College Students,High School Students,Student Evaluation,Statistical Analysis

The current study investigates the degree to which the lexical properties of students' essays can inform stealth assessments of their vocabulary knowledge. In particular, we used indices calculated with the natural language processing tool, TAALES, to predict students' performance on a measure of vocabulary knowledge. To this end, two corpora were collected which contained essays from early college and high school students, respectively. The lexical properties of these essays were then calculated using TAALES. The results of this study indicated that two of the linguistic indices were able to account for 44% of the variance in the college students' vocabulary knowledge scores. Additionally, the significant indices from this first corpus analysis were able to account for a significant portion of the variance in the high school students' vocabulary scores. Overall, these results suggest that natural language processing techniques can inform stealth assessments and help to improve student models within computer-based learning environments. [For complete proceedings, see ED560503.]
Exploring Dynamical Assessments of Affect, Behavior, and Cognition and Math State Test Achievement
Author: San Pedro, Maria Ofelia Z.
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Mathematics Tests,Achievement Tests,Middle School Students,Intelligent Tutoring Systems,Student Behavior,Knowledge Level,Psychological Patterns,Change,Scores,Prediction

There is increasing evidence that fine-grained aspects of student performance and interaction within educational software are predictive of long-term learning. Machine learning models have been used to provide assessments of affect, behavior, and cognition based on analyses of system log data, estimating the probability of a student's particular affective state, behavior, and knowledge (cognition). These measures have (in aggregate) successfully predicted outcomes such as performance on standardized exams. In this paper, we employ a different approach of relating interaction patterns to learning outcomes, using dynamical methods that assess patterns of fine-grained measures of affect, behavior, and knowledge as they occur across time. We use Hurst exponents and Entropy scores computed from assessments of affect, behavior, performance, and knowledge acquired from 1,376 middle school students who used a math tutoring system (ASSISTments), and analyze the relations of these dynamical measures to the students' end-of-year state test (MCAS) performance. Our results show that fine-grained changes in affect, behavior, and knowledge are significantly related to and predictive of their eventual MCAS performance, providing a new lens on the dynamic and nuanced nature of student interaction within online learning platforms and how it affects achievement. [For complete proceedings, see ED560503.]
Interaction Network Estimation: Predicting Problem-Solving Diversity in Interactive Environments
Author: Eagle, Michael
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Problem Solving,Prediction,Intelligent Tutoring Systems,Computer Assisted Instruction,Computation,Accuracy,Data Analysis,Interaction,Networks

Intelligent tutoring systems and computer aided learning environments aimed at developing problem solving produce large amounts of transactional data which make it a challenge for both researchers and educators to understand how students work within the environment. Researchers have modeled student-tutor interactions using complex networks in order to automatically derive next step hints. However, there are no clear thresholds for the amount of student data required before the hints can be produced. We introduce a novel method of estimating the size of the unobserved interaction network from a sample by leveraging Good-Turing frequency estimation. We use this estimation to predict size, growth, and overlap of interaction networks using a small sample of student data. Our estimate is accurate in as few as 10{30 students and is a good predictor for the growth of the observed state space for the full network, as well as the subset of the network which is usable for automatic hint generation. These methods provide researchers with metrics to evaluate different state representations, student populations, and general applicability of interaction networks on new datasets. [For complete proceedings, see ED560503.]
Data Analysis Tools and Methods for Improving the Interaction Design in E-Learning
Author: Popescu, Paul Stefan
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Data Analysis,Electronic Learning,Interaction,Design,Man Machine Systems,Research Methodology

In this digital era, learning from data gathered from different software systems may have a great impact on the quality of the interaction experience. There are two main directions that come to enhance this emerging research domain, Intelligent Data Analysis (IDA) and Human Computer Interaction (HCI). HCI specific research methodologies can be used to present the user what IDA brings after learning and analyzing user's behavior. This research plan aims to investigate how techniques and mechanisms available in both research areas can be used in order to improve learners' experiences and overall effectiveness of the e-Learning environment. The foreseen contributions relate to three levels. First is the design and implementation of new algorithms for IDA. The next level is related to design and implementation of a generic leaning analytic engine that can accommodate educational data in attempt to model data (i.e., users, assets, etc.) and provide input for the presentation layer. Last and top level is represented by the presentation layer where the output of the underlying levels adapts the user interface for students and professors. [For complete proceedings, see ED560503.]
Topic Transition in Educational Videos Using Visually Salient Words
Author: Gandhi, Ankit
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Video Technology,Technology Uses in Education,Educational Technology,Vocabulary,Online Courses,Language Usage,Scoring,Teaching Methods,Visual Stimuli,Classification,Probability,Scores,Navigation (Information Systems),Educational Media

In this paper, we propose a visual saliency algorithm for automatically finding the topic transition points in an educational video. First, we propose a method for assigning a saliency score to each word extracted from an educational video. We design several mid-level features that are indicative of visual saliency. The optimal feature combination strategy is learnt from a Rank-SVM to obtain an overall visual saliency score for all the words. Second, we use these words and their saliency scores to find the probability of a slide being a topic transition slide. On a test set of 10 instructional videos (12 hours), the F-score of the proposed algorithm in retrieving topic-transition slides is 0.17 higher than that of Latent Dirichlet Allocation (LDA)-based methods. The proposed algorithm enables demarcation of an instructional video along the lines of &quot;table of content&quot;/&quot;sections&quot; for a written document and has applications in efficient video navigation. User studies also demonstrate statistically significant improvement in across-topic navigation using the proposed algorithm. [For complete proceedings, see ED560503.]
A Comparison of Video-Based and Interaction-Based Affect Detectors in Physics Playground
Author: Kai, Shiming
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Video Technology,Interaction,Physics,Affective Behavior,Educational Technology,Technology Uses in Education,Science Instruction,Nonverbal Communication,Student Behavior,Games,Measurement Techniques,Prediction,Grade 8,Grade 9,Pretests Posttests,Observation,Coding,Scientific Principles,Concept Formation,Scientific Concepts,Statistical Analysis

Increased attention to the relationships between affect and learning has led to the development of machine-learned models that are able to identify students' affective states in computerized learning environments. Data for these affect detectors have been collected from multiple modalities including physical sensors, dialogue logs, and logs of students' interactions with the learning environment. While researchers have successfully developed detectors based on each of these sources, little work has been done to compare the performance of these detectors. In this paper, we address this issue by comparing interaction-based and video-based affect detectors for a physics game called Physics Playground. Specifically, we report on the development and detection accuracy of two suites of affect and behavioral detectors. The first suite of detectors applies facial expression recognition to video data collected with webcams, while the second focuses on students' interactions with the game as recorded in log-files. Ground-truth affect and behavior annotations for both face- and interaction-based detectors were obtained via live field observations during game-play. We first compare the performance of these detectors in predicting students' affective states and off-task behaviors, and then proceed to outline the strengths and weakness of each approach. [For complete proceedings, see ED560503.]
Sensor-Free or Sensor-Full: A Comparison of Data Modalities in Multi-Channel Affect Detection
Author: Paquette, Luc
Date: 2016-06-00
Type: Speeches/Meeting Papers
Subjects: Models,Affective Behavior,Intelligent Tutoring Systems,Games,Medical Services,Comparative Analysis,Computer Software,Diaries,Interaction,Human Posture,Observation,Measurement Equipment,Training,Military Personnel,Technology Uses in Education,Physiology,Coding,Statistical Analysis,Regression (Statistics)

Computational models that automatically detect learners' affective states are powerful tools for investigating the interplay of affect and learning. Over the past decade, affect detectors--which recognize learners' affective states at run-time using behavior logs and sensor data--have advanced substantially across a range of K-12 and postsecondary education settings. Machine learning-based affect detectors can be developed to utilize several types of data, including software logs, video/audio recordings, tutorial dialogues, and physical sensors. However, there has been limited research on how different data modalities combine and complement one another, particularly across different contexts, domains, and populations. In this paper, we describe work using the Generalized Intelligent Framework for Tutoring (GIFT) to build multi-channel affect detection models for a serious game on tactical combat casualty care. We compare the creation and predictive performance of models developed for two different data modalities: 1) software logs of learner interactions with the serious game, and 2) posture data from a Microsoft Kinect sensor. We find that interaction-based detectors outperform posture-based detectors for our population, but show high variability in predictive performance across different affect. Notably, our posture-based detectors largely utilize predictor features drawn from the research literature, but do not replicate prior findings that these features lead to accurate detectors of learner affect. [For complete proceedings, see ED560503.]
Dynamic User Modeling within a Game-Based ITS
Author: Snow, Erica L.
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Intelligent Tutoring Systems,Models,Individualized Instruction,Needs Assessment,Computer System Design,Feedback (Response),Statistical Data,Data Processing,Data Analysis,High School Students,Reading Comprehension,Reading Improvement,Help Seeking,Educational Games,Game Theory

Intelligent tutoring systems are adaptive learning environments designed to support individualized instruction. The adaptation embedded within these systems is often guided by user models that represent one or more aspects of students' domain knowledge, actions, or performance. The proposed project focuses on the development and testing of user models within the iSTART-2 intelligent tutoring system, which will be informed by dynamic methodologies and data mining techniques. My previous work has used post hoc dynamic methodologies to quantify optimal and in-optimal learning behaviors within the game-based system, iSTART-2. I plan to build upon this work by conducting dynamical analyses in real-time to inform the user models embedded within iSTART-2. I am seeking advice and feedback on the statistical methods and feature selection that should included within the new dynamic user model. The implications of this approach for both iSTART-2 and the EDM field are discussed. [For complete proceedings, see ED560503.]
Use of Time Information in Models behind Adaptive System for Building Fluency in Mathematics
Author: Rihák, Jirí
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Mathematics Achievement,Mathematics Skills,Models,Reaction Time,Systems Building,Information Utilization,Task Analysis,Computer System Design,Academic Records,Inquiry,Data Collection,Data Processing,Data Analysis,Problem Solving

In this work we introduce the system for adaptive practice of foundations of mathematics. Adaptivity of the system is primarily provided by selection of suitable tasks, which uses information from a domain model and a student model. The domain model does not use prerequisites but works with splitting skills to more concrete sub-skills. The student model builds on variation of Elo rating system which provide good accuracy and easy application in online system. The main feature of the student model is use of response times which can carry useful information about mastery. [For complete proceedings, see ED560503.]
Data-Driven Hint Generation from Peer Debugging Solutions
Author: Liu, Zhongxiu
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Programming,Educational Games,Puzzles,Problem Solving,Prompting,Science Activities,Research Methodology,Evaluation Methods,Doctoral Dissertations,Data,Research Projects

Data-driven methods have been a successful approach to generating hints for programming problems. However, the majority of previous studies are focused on procedural hints that aim at moving students to the next closest state to the solution. In this paper, I propose a data-driven method to generate remedy hints for BOTS, a game that teaches programming through a block-moving puzzle. Remedy hints aim to help students out of dead-end states, which are states in the problem from where no student has ever derived a solution. To address this, my proposed work includes designing debugging activities and generating remedy hints from students' solutions to debugging activities. [For complete proceedings, see ED560503.]
Enhancing Student Motivation and Learning within Adaptive Tutors
Author: Ostrow, Korinn S.
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Student Motivation,Educational Practices,Intelligent Tutoring Systems,Elementary Secondary Education,Research Projects,Doctoral Dissertations,Research Design,Technology Uses in Education

My research is rooted in improving K-12 educational practice using motivational facets made possible through adaptive tutoring systems. In an attempt to isolate best practices within the science of learning, I conduct randomized controlled trials within ASSISTments, an online adaptive tutoring system that provides assistance and assessment to students around the world. My work also incorporates big data analytics through the establishment of data driven learning models that promote the use of finite assessment to optimize student modeling and enhance user motivation. This paper highlights a turning point in my research as I transition into PhD candidacy. My contributions thus far and my research goals are discussed, with consult sought on how to best meld the realms of my work moving forward. An iteration of this work has also been published as a Doctoral Consortium at AIED 2015. [For complete proceedings, see ED560503.]
Developing Self-Regulated Learners through an Intelligent Tutoring System
Author: Kelly, Kim
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Intelligent Tutoring Systems,Active Learning,Self Management,Independent Study,Self Motivation,Beliefs,Help Seeking,Metacognition,Learning Strategies,Technology Uses in Education

Intelligent tutoring systems have been developed to help students learn independently. However, students who are poor self-regulated learners often struggle to use these systems because they lack the skills necessary to learn independently. The field of psychology has extensively studied self-regulated learning and can provide strategies to improve learning, however few of these include the use of technology. The present proposal reviews three elements of self-regulated learning (motivational beliefs, help-seeking behavior, and meta-cognitive self-monitoring) that are essential to intelligent tutoring systems. Future research is suggested, which address each element in order to develop self-regulated learning strategies in students while they are engaged in learning mathematics within an intelligent tutoring system. [For complete proceedings, see ED560503.]
Estimating the Local Size and Coverage of Interaction Network Regions
Author: Eagle, Michael
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Interaction,Teacher Student Relationship,Intelligent Tutoring Systems,Data,Educational Games,Problem Solving,Network Analysis,Social Networks,Incidence,Computation,Data Processing,Probability

Interactive problem solving environments, such as intelligent tutoring systems and educational video games, produce large amounts of transactional data which make it a challenge for both researchers and educators to understand how students work within the environment. Researchers have modeled the student-tutor interactions using complex network representations to automatically derive next-step hints, derive high-level approaches, and create visualizations of student behavior. However, students do not explore the complete problem space. The nonuniform exploration of the problem results in smaller networks and less next-step hints. In this work we explore the possibility of using frequency estimation to uncover locations in the network with differing amounts of student-saturation. Identification of these regions can be used to locate specific problem approaches and strategies that would be most improved by additional student-data. [For complete proceedings, see ED560503.]