ethanporcaro/eric-fulltext-meta huggingface dataset:
Sequence Matters, but How Exactly? A Method for Evaluating Activity Sequences from Data
Author: Doroudi, Shayan
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Intelligent Tutoring Systems,Sequential Approach,Problem Solving,Learning Processes,Learning Activities,Fractions,Mathematics Instruction,Elementary School Students,Performance Based Assessment,Grade 4,Grade 5

How should a wide variety of educational activities be sequenced to maximize student learning? Although some experimental studies have addressed this question, educational data mining methods may be able to evaluate a wider range of possibilities and better handle many simultaneous sequencing constraints. We introduce Sequencing Constraint Violation Analysis (SCOVA): a general method for evaluating alternative activity sequences using existing data. SCOVA can be used to explore many complex sequencing constraints, such as prerequisite relationships, blocking, interleaving, and spiraling. We demonstrate SCOVA on data collected from a fractions intelligent tutoring system (ITS). Some of our findings challenge our initial hypotheses regarding sequencing, illustrating the utility and versatility of the method. The method can also be applied to other learning environments, as long as the available data has substantial variability in students' activity sequences. [For the full proceedings, see ED592609.]
Riding an Emotional Roller-Coaster: A Multimodal Study of Young Child&apos;s Math Problem Solving Activities
Author: Chen, Lujie
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Young Children,Mathematics Education,Problem Solving,Psychological Patterns,Emotional Response,Learner Engagement,Intelligent Tutoring Systems,Instructional Design,Interpersonal Communication,Parent Child Relationship,Mothers,Parents as Teachers,Teacher Student Relationship,Video Technology,Correlation,Predictor Variables,Affective Behavior,Identification

Solving challenging math problems often invites a child to ride an &quot;emotional roller-coaster&quot; and experience a complex mixture of emotions including confusion, frustration, joy, and surprise. Early exposure to this type of &quot;hard fun&quot; may stimulate child's interest and curiosity of mathematics and nurture life long skills such as resilience and perseverance. However, without optimal support, it may also turn off child prematurely due to unresolved frustration. An ideal teacher is able to pick up child's subtle emotional signals in real time and respond optimally to offer cognitive and emotional support. In order to design an intelligent tutor specifically designed for this purpose, it is necessary to understand at fine-grained level the child's emotion experience and its interplay with the inter-personal communication dynamics between child and his/her teacher. In this study, we made such an attempt by analyzing a series of video recordings of problem solving sessions by a young student and his mom, the ideal teacher. We demonstrate a multimodal analysis framework to characterize several aspects of the child-mom interaction patterns within the emotional context at a granular level. We then build machine learning models to predict teacher's response using extracted multimodal features. In addition, we validate the performance of automatic detector of affect, intent-to-connect behavior, and voice activity, using annotated data, which provides evidence of the potential utility of the presented tools in scaling up analysis of this type to large number of subjects and in implementing tools to guide teachers towards optimal interactions in real time. [For the full proceedings, see ED592609.]
Student Emotion, Co-Occurrence, and Dropout in a MOOC Context
Author: Dillon, John
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Psychological Patterns,Emotional Response,Dropouts,Online Courses,Learning Processes,Anxiety,Learner Engagement,College Students

This paper discusses self-reported emotions experienced by students in a Massive Open Online Course (MOOC) learning context. Emotions have been previously shown to be related to learning in classrooms and laboratory studies and have even been leveraged to improve learning. In this study, frequently occurring discrete emotions as well as frequently, co-occurring pairs of emotions were analyzed during learning with a MOOC. Both discrete and co-occurring emotions were related to students dropping out of the course, illustrating the importance of student emotion in a MOOC context. [For the full proceedings, see ED592609.]
Seeking Programming-Related Information from Large Scaled Discussion Forums, Help or Harm?
Author: Lu, Yihan
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Programming,Advanced Students,Reading Processes,Computer Mediated Communication,Problem Solving,Information Seeking,Student Attitudes,Novices,Computer Science Education,Online Searching,Markov Processes,Web Browsers,College Students,Discussion,Student Behavior

Online programming discussion forums have grown increasingly and have formed sizable repositories of problem solving-solutions. In this paper, we investigate programming learners' information seeking behaviors from online discussion forums. We design engines to collect students' information seeking processes, including query formulation, refinement, results examination, and reading processes. We model these behaviors and conduct sequence pattern mining. The results show that programming learners indeed seek for programming related information from discussion forums by actively searching on the site and reading posts progressively according to course schedule topics. Advanced students consistently perform query refinements, examine search results and commit to read, however, novices do not. In addition, advanced students commit to read posts, but novices only skim. [For the full proceedings, see ED592609.]
Semantic Features of Math Problems: Relationships to Student Learning and Engagement
Author: Slater, Stefan
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Semantics,Mathematical Applications,Teacher Developed Materials,Correlation,Learning,Learner Engagement,Intelligent Tutoring Systems,Metadata,Middle School Students,Middle School Teachers,Mathematics Instruction

The creation of crowd-sourced content in learning systems is a powerful method for adapting learning systems to the needs of a range of teachers in a range of domains, but the quality of this content can vary. This study explores linguistic differences in teacher-created problem content in ASSISTments using a combination of discovery with models and correlation mining. Specifically, we find correlations between semantic features of mathematics problems and indicators of learning and engagement, suggesting promising areas for future work on problem design. We also discuss limitations of semantic tagging tools within mathematics domains and ways of addressing these limitations. [For the full proceedings, see ED592609. For the grantee submission see ED571512.]
Semi-Markov Model for Simulating MOOC Students
Author: Faucon, Louis
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Markov Processes,Monte Carlo Methods,Bayesian Statistics,Online Courses,Student Behavior,Expectation,Maximum Likelihood Statistics,Accuracy

Large-scale experiments are often expensive and time consuming. Although Massive Online Open Courses (MOOCs) provide a solid and consistent framework for learning analytics, MOOC practitioners are still reluctant to risk resources in experiments. In this study, we suggest a methodology for simulating MOOC students, which allow estimation of distributions, before implementing a large-scale experiment. To this end, we employ generative models to draw independent samples of artificial students in Monte Carlo simulations. We use SemiMarkov Chains for modeling student's activities and ExpectationMaximization algorithm for fitting the model. From the fitted model, we generate simulated students whose processes of weekly activities are similar to these of the real students. [For the full proceedings, see ED592609.]
Topic-Wise Classification of MOOC Discussions: A Visual Analytics Approach
Author: Atapattu, Thushari
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,Large Group Instruction,Educational Technology,Technology Uses in Education,Visualization,Graphs,Visual Stimuli,Data Collection,Data Analysis,Discussion (Teaching Technique)

With a goal of better understanding the online discourse within the Massive Open Online Course (MOOC) context, this paper presents an open source visualisation dashboard developed to identify and classify emergent discussion topics (or themes). As an extension to the authors' previous work in identifying key topics from MOOC discussion contents, this work visualises lecture-related discussions as a graph of relationships between topics and threads. We demonstrate the visualisation using three popular MOOCs offered during 2013. This work facilitates the course staff to locate and navigate the most influential topic clusters as well as the discussions that require intervention by connecting the topics with the corresponding weekly lectures. Further, we demonstrate how our interactive visualisation can be used to explore correlation between discussion topics and other variables such as views, posts, votes, and instructor intervention. [For the full proceedings, see ED592609.]
The Eyes Have It: Gaze-Based Detection of Mind Wandering during Learning with an Intelligent Tutoring System
Author: Hutt, Stephen
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Attention,Intelligent Tutoring Systems,Eye Movements,Biology,Science Instruction,Undergraduate Students,Educational Technology,Technology Uses in Education

Mind wandering (MW) is a ubiquitous phenomenon characterized by an unintentional shift in attention from task-related to task-unrelated thoughts. MW is frequent during learning and negatively correlates with learning outcomes. Therefore, the next generation of intelligent learning technologies should benefit from mechanisms that detect and combat MW. As an initial step in this direction, we used eye-gaze and contextual information (e.g., time into session) to build an automated MW detector as students interact with GuruTutor -- an intelligent tutoring system (ITS) for biology. Students self-reported MW by responding to pseudorandom thought-probes during the tutoring session while a consumer-grade eye tracker monitored their eye movements. We used supervised machine learning techniques to discriminate between positive and negative responses to the probes in a student-independent fashion. Our best results for detecting MW (F[subscript 1] of 0.49) were obtained with an evolutionary approach to develop topologies for neural network classifiers. These outperformed standard classifiers (F[subscript 1] of 0.43 with a Bayes net) and a chance baseline (F[subscript 1] of 0.19). We discuss our results in the context of integrating MW detection into an attention-aware version of GuruTutor. [For the full proceedings, see ED592609.]
Human Communicative Dialogic Practices in Content and Language-Based Educational Interactions with Technology
Author: Garcia Laborda, Jesus
Date: 2016-00-00
Type: Journal Articles
Subjects: Second Language Learning,Interpersonal Communication,Interaction,English (Second Language),Educational Technology,Technology Uses in Education,Teacher Student Relationship,Group Discussion,Foreign Countries,College Students,Preservice Teachers,Teaching Methods,Student Attitudes,Peer Evaluation

Anthropology has paid close attention to linguistic interaction and foreign language learning. This study approaches the use of dialogic learning that has strong roots in anthropology, sociology and applied linguistics. Following earlier research on teaching content in English in different subjects of teacher training through dialogic interaction, this paper focuses on the specific use of digital technology as a support for analysis through teacher-instructor interaction, as a means of social interaction, and as the creation of self-designed materials for content language integrated learning. Data is obtained from group discussions using digital content. Results indicate that technology represents a shifting pedagogical paradigm to enhance interaction in the Zone of Proximal Development and promote learning content in English through self and peer reflection. The findings of this study may serve as a monitor for content and language integrated learning educators aiming to enhance principled communicative insightful teaching with technology.
The Affective Impact of Tutor Questions: Predicting Frustration and Engagement
Author: Vail, Alexandria K.
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Affective Behavior,Questioning Techniques,Emotional Response,Nonverbal Communication,College Students,Student Behavior,Tutoring,Computer Mediated Communication,Educational Technology,Technology Uses in Education

Tutorial dialogue is a highly effective way to support student learning. It is widely recognized that tutor dialogue moves can significantly influence learning outcomes, but the ways in which tutor moves, student affective response, and outcomes are related remains an open question. This paper presents an analysis of student affective response, as evidenced by multimodal data streams, immediately following tutor questions. The findings suggest that students' affect immediately following tutor questions is highly predictive of end-of-session self-reported engagement and frustration. Notably, facial action units which have been associated with emotional states such as embarrassment, disgust, and happiness appear to play important roles in students' expressions of frustration and engagement during learning. This line of investigation will aid in the development of a deeper understanding of the relationships between tutorial dialogue and student affect during learning. [For the full proceedings, see ED592609.]
The Apprentice Learner Architecture: Closing the Loop between Learning Theory and Educational Data
Author: MacLellan, Christopher J.
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Educational Research,Data Collection,Learning Theories,Recall (Psychology),Prior Learning,Memory,Models,Prediction,Fractions,Arithmetic,Addition,Simulation,Intelligent Tutoring Systems,Bayesian Statistics

While Educational Data Mining research has traditionally emphasized the practical aspects of learner modeling, such as predictive modeling, estimating students knowledge, and informing adaptive instruction, in the current study, we argue that Educational Data Mining can also be used to test and improve our fundamental theories of human learning. Using the Apprentice Learner architecture, a computational theory of learning capable of simulating human behavior in interactive learning environments, we generate two models that embody alternative theories of human learning: (1) that humans perfectly recall previous training during learning and (2) that humans only recall a limited window of experience. We evaluate which of these models is better supported by data from two fractions tutoring systems. In general, we find that the model with a complete memory better fits the data than a model recalling only the previous training experience (data-drive theory development). Additionally, we demonstrate that both models are able to predict student performances, as well as, reproduce the main effects of an experimental paradigm without being trained on student data (theory-driven prediction). These results demonstrate how the Apprentice Learner architecture can be used to close the loop between learning theory and educational data. [For the full proceedings, see ED592609.]
Transactivity as a Predictor of Future Collaborative Knowledge Integration in Team-Based Learning in Online Courses
Author: Wen, Miaomiao
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Educational Research,Data Collection,Cooperative Learning,Predictor Variables,Teamwork,Online Courses,Large Group Instruction,Assignments,Learning Processes,Group Dynamics

To create a satisfying social learning experience, an emerging challenge in educational data mining is to automatically assign students into effective learning teams. In this paper, we utilize discourse data mining as the foundation for an online team-formation procedure. The procedure features a deliberation process prior to team assignment, where participants hold discussions both to prepare for the collaboration task and provide indicators that are then used during automated team assignment. We automatically assign teams in a way that maximizes average observed pairwise transactivity exchange within teams, whereas in a control condition, teams are assigned randomly. We validate our team-formation procedure in a crowdsourced online environment that enables effective isolation of variables, namely Amazon's Mechanical Turk. We compare group knowledge integration outcomes between the two team assignment conditions. Our results demonstrate that transactivity-based team assignment is associated with significantly greater knowledge integration (p &lt; 0.05, effect size 3 standard deviations). [For the full proceedings, see ED592609.]
Student Usage Predicts Treatment Effect Heterogeneity in the Cognitive Tutor Algebra I Program
Author: Sales, Adam C.
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Algebra,Mathematics Instruction,Educational Technology,Technology Uses in Education,Intelligent Tutoring Systems,High School Students,Secondary School Mathematics,Program Effectiveness,Middle School Students

The Cognitive Tutor Algebra I (CTAI) curriculum, which includes both textbook and online components, has been shown to boost student learning by about 0.2 standard deviations in a randomized effectiveness trial. Students who were assigned to the experimental condition varied substantially in how, and how much, the used the online component of CTAI, but original analyses of the experimental data focused on estimating average effects, and did not examine whether the CTAI treatment effect varied by the amount of style of usage. This study leverages log data from the experiment to present a more nuanced analysis. It uses the framework of Principal Stratification, which estimates the varying CTAI treatment effect as a function of &quot;potential&quot; usage--either how students used the program, or how they would have used it had they been assigned to the treatment condition. With experimental data, Principal Stratification does not require that we assume that all relevant variables have been measured. With this framework, we find that students who receive a medium amount of assistance from the software (in the form of hints and error feedback) experience the largest effects, with lower effects for students who receive a lot or a little; and evidence that students who do not follow the curriculum order experience smaller treatment effects. [For the full proceedings, see ED592609.]
Temporally Coherent Clustering of Student Data
Author: Klingler, Severin
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Student Behavior,Data Analysis,Behavior Patterns,Multivariate Analysis,Learning,Intelligent Tutoring Systems,Markov Processes,Elementary School Students,Spelling Instruction,Mathematics Instruction,Navigation (Information Systems),Help Seeking

The extraction of student behavior is an important task in educational data mining. A common approach to detect similar behavior patterns is to cluster sequential data. Standard approaches identify clusters at each time step separately and typically show low performance for data that inherently suffer from noise, resulting in temporally inconsistent clusters. We propose an evolutionary clustering pipeline that can be applied to learning data, aiming at improving cluster stability over multiple training sessions in the presence of noise. Our model selection is designed such that relevant cluster evolution effects can be captured. The pipeline can be used as a black box for any intelligent tutoring system (ITS). We show that our method outperforms previous work regarding clustering performance and stability on synthetic data. Using log data from two ITS, we demonstrate that the proposed pipeline is able to detect interesting student behavior and properties of learning environments. [For the full proceedings, see ED592609.]
Unnatural Feature Engineering: Evolving Augmented Graph Grammars for Argument Diagrams
Author: Xue, Linting
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Graphs,Persuasive Discourse,College Students,Expertise,Artificial Intelligence,Automation,Computation,Data Analysis,Standards

Graph data such as argument diagrams has become increasingly common in EDM. Augmented Graph Grammars are a robust rule formalism for graphs. Prior research has shown that hand-authored graph grammars can be used to automatically grade student-produced argument diagrams. But hand-authored rules can be time consuming and expensive to produce, and they may not generalize well to novel contexts. We applied Evolutionary Computation to automatically induce empirically-valid graph grammars for argument diagrams that can be used for automatic grading or provide the basis for hints. Our results show that our approach can generate more relevant rules than experts or other state of the art algorithms, and that these evolved rules outperform the alternatives. [For the full proceedings, see ED592609.]
A Year in Head Start: Findings from FACES 2014 on Children&apos;s Progress toward School Readiness during the 2014-2015 Program Year. Research Brief. OPRE Report 2018-80
Author: Klein, Ashley Kopack
Date: 2018-08-00
Type: Reports - Research
Subjects: Early Intervention,School Readiness,Family Characteristics,Child Development,Student Characteristics,Preschool Children,Cognitive Ability,Interpersonal Competence,Social Development,Physical Health,Program Effectiveness,Age Differences,Bilingual Students,Preschool Education,At Risk Students,Minority Group Students,English Language Learners

Using data from the Head Start Family and Child Experiences Survey (FACES 2014), this research brief describes the family background and developmental progress of Head Start children as they complete a program year (from Fall 2014 to Spring 2015). This brief highlights descriptive information from the FACES 2014-2015 Data Tables and Study Design report (Aikens et al. 2017a). Other FACES 2014 products describe characteristics of Head Start children at the beginning of the program year (Aikens et al. 2017c; Tarullo et al. 2017). This research brief addresses the following research questions: (1) What are the demographic characteristics of children and families who are completing a program year? (2) What are the cognitive and social skills of Head Start children as they complete a program year? How do these compare to their skills at the beginning of the program year? (3) What is the physical health status of children as they complete a program year? How does this compare to their status at the beginning of the program year? (4) Do children's cognitive and social skills and health status at the end of the program year vary by their program exposure and age? Does children's progress in their skills or health status vary across the program year? and (5) What are the cognitive and social skills and health status of dual language learners (DLLs) as they complete a program year? How do these compare to their skills or health status at the beginning of the program year?