ethanporcaro/eric-fulltext-meta huggingface dataset:
Predictors of Student Satisfaction: A Large-Scale Study of Human-Human Online Tutorial Dialogues
Author: Chen, Guanliang
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Student Attitudes,Intelligent Tutoring Systems,Computer Software,Dialogs (Language),Computational Linguistics,Models,Correlation,Teacher Student Relationship,Tutors,Teaching Methods,Prediction

For the development of successful human-agent dialogue-based tutoring systems, it is essential to understand what makes a human-human tutorial dialogue successful. While there has been much research on dialogue-based intelligent tutoring systems, there have been comparatively fewer studies on analyzing large-scale datasets of human-human online tutoring dialogues. A critical indicator of success of a tutoring dialogue can be student satisfaction, which is the focus of the study reported in the paper. Specifically, we used a large-scale dataset, which consisted of over 15,000 tutorial dialogues generated by human tutors and students in a mobile app-based tutoring service. An extensive analysis of the dataset was performed to identify factors relevant to student satisfaction in online tutoring systems. The study also engineered a set of 325 features as input to a Gradient Tree Boosting model to predict tutoring success. Experimental results revealed that (i) in a tutorial dialogue, factors such as efforts spent by both tutors and students, utterance informativeness and tutor responsiveness were positively correlated with student satisfaction; and (ii) Gradient Tree Boosting model could effectively predict tutoring success, especially with utterances from the later period of a dialogue, but more research effort is needed to improve the prediction performance. [For the full proceedings, see ED599096.]
Optimizing Assignment of Students to Courses Based on Learning Activity Analytics
Author: Shimada, Atsushi
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Student Placement,Learning Activities,Learning Analytics,Cognitive Style,Simulation,Student Improvement,Course Descriptions,Lecture Method,Electronic Publishing,Books,Student Satisfaction,Scores,Student Behavior,Behavior Patterns,Cluster Grouping,Electronic Learning,Foreign Countries,College Students

In this paper, we focus on optimizing the assignment of students to courses. The target courses are conducted by different teachers using the same syllabus, course design, and lecture materials. More than 1,300 students are mechanically assigned to one of ten courses taught by different teachers. Therefore, mismatches often occur between students' learning behavior patterns and teachers' approach to teaching. As a result, students may be less satisfied, have a lower level of understanding of the material, and achieve less. To solve these problems, we propose a strategy to optimize the assignment of students to courses based on learning activity analytics. The contributions of this study are 1) clarifying the relationship between learning behavior pattern and teaching based on learning activity analytics using large-scale educational data, 2) optimizing the assignment of students to courses based on learning behavior pattern analytics, and 3) demonstrating the effectiveness of assignment optimization via simulation experiments. [For the full proceedings, see ED599096.]
Toward Near Zero-Parameter Prediction Using a Computational Model of Student Learning
Author: Weitekamp, Daniel, III.
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Computation,Models,Learning,Prediction,Educational Technology,Prior Learning,Accuracy,Intelligent Tutoring Systems,Students,Fractions

Computational models of learning can be powerful tools to test educational technologies, automate the authoring of instructional software, and advance theories of learning. These mechanistic models of learning, which instantiate computational theories of the learning process, are capable of making predictions about learners' performance in instructional technologies given only the technology itself without fitting any parameters to existing learners' data. While these so call &quot;zero-parameter&quot; models have been successful in modeling student learning in intelligent tutoring systems they still show systematic deviation from human learning performance. One deviation stems from the computational models' lack of prior knowledge--all models start off as a blank slate--leading to substantial differences in performance at the first practice opportunity. In this paper, we explore three different strategies for accounting for prior knowledge within computational models of learning and the effect of these strategies on the predictive accuracy of these models. [For the full proceedings, see ED599096.]
Modelling End-of-Session Actions in Educational Systems
Author: Hansen, Christian
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Integrated Learning Systems,Probability,Foreign Countries,Student Behavior,Mathematical Models,Mathematics,Interaction

In this paper we consider the problem of modelling when students end their session in an online mathematics educational system. Being able to model this accurately will help us optimize the way content is presented and consumed. This is done by modelling the probability of an action being the last in a session, which we denote as the End-of-Session probability. We use log data from a system where students can learn mathematics through various kinds of learning materials, as well as multiple types of exercises, such that a student session can consist of many different activities. We model the End-of-Session probability by a deep recurrent neural network in order to utilize the long term temporal aspect, which we experimentally show is central for this task. Using a large scale dataset of more than 70 million student actions, we obtain an AUC of 0.81 on an unseen collection of students. Through a detailed error analysis, we observe that our model is robust across different session structures and across varying session lengths. [For the full proceedings, see ED599096.]
How Should Online Teachers of English as a Foreign Language (EFL) Write Feedback to Students?
Author: Aguerrebere, Cecilia
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: English (Second Language),Second Language Learning,Second Language Instruction,Language Teachers,Online Courses,Feedback (Response),High School Students,Language Proficiency,Teacher Student Relationship,Time Management,Teaching Methods,Foreign Countries,Student Motivation,Computer Mediated Communication,Correlation,Student Participation

We analyze teachers' written feedback to students in an online learning environment, specifically a setting in which high school students in Uruguay are learning English as a foreign language. How complex should teachers' feedback be? Should it be adapted to each student's English proficiency level? How does teacher feedback affect the probability of engaging the student in a conversation? To explore these questions, we conducted both parametric (multilevel modeling) and non-parametric (bootstrapping) analyses of 27,627 messages exchanged between 35 teachers and 1074 students in 2017 and 2018. Our results suggest: (1) Teachers should adapt their feedback complexity to their students' English proficiency level. Students who receive feedback that is too complex or too basic for their level post 13-15% fewer comments than those who receive adapted feedback. (2) Feedback that includes a question is associated with higher odds-ratio (17.5-19) of engaging the student in conversation. (3) For students with low English proficiency, slow turnaround (feedback after 1 week) reduces this odds ratio by 0.7. These results have potential implications for online platforms offering foreign language learning services, in which it is crucial to give the best possible learning experience while judiciously allocating teachers' time. [For the full proceedings, see ED599096.]
Time-Series Insights into the Process of Passing or Failing Online University Courses Using Neural-Induced Interpretable Student States
Author: Jeon, Byungsoo
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,At Risk Students,Academic Achievement,Academic Failure,Data Analysis,Behavior Patterns,Intervention,Student Records,Universities,Models,Mentors,Computational Linguistics,Prediction,Learner Engagement,Notetaking,Student Behavior

This paper addresses a key challenge in Educational Data Mining, namely to model student behavioral trajectories in order to provide a means for identifying students most at risk, with the goal of providing supportive interventions. While many forms of data including clickstream data or data from sensors have been used extensively in time series models for such purposes, in this paper we explore the use of textual data, which is sometimes available in the records of students at large, online universities. We propose a time series model that constructs an evolving student state representation using both clickstream data and a signal extracted from the textual notes recorded by human mentors assigned to each student. We explore how the addition of this textual data improves both the predictive power of student states for the purpose of identifying students at risk for course failure as well as for providing interpretable insights about student course engagement processes. [For the full proceedings, see ED599096.]
Mining University Registrar Records to Predict First-Year Undergraduate Attrition
Author: Aulck, Lovenoor
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Student Records,Registrars (School),Predictor Variables,Undergraduate Students,Student Attrition,College Freshmen,Learning Analytics,Academic Persistence,Graduation,Enrollment,Student Characteristics,Demography,Time to Degree,Scores,Standardized Tests,Grades (Scholastic)

Each year, roughly 30% of first-year students at US baccalaureate institutions do not return for their second year and billions of dollars are spent educating these students. Yet, little quantitative research has analyzed the causes and possible remedies for student attrition. What's more, most of the previous attempts to model attrition at traditional campuses using machine learning have focused on small, homogeneous groups of students. In this work, we model student attrition using a dataset that is composed almost exclusively of information routinely collected for record-keeping at a large, public US university. By examining the entirety of the university's student body and not a subset thereof, we use one of the largest known datasets for examining attrition at a public US university (N = 66,060). Our results show that students' second year re-enrollment and eventual graduation can be accurately predicted based on a single year of data (AUROCs = 0.887 and 0.811, respectively). We find that demographic data (such as race, gender, etc.) and pre-admission data (such as high school academics, entrance exam scores, etc.) - upon which most admissions processes are predicated - are not nearly as useful as early college performance/transcript data for these predictions. These results highlight the potential for data mining to impact student retention and success at traditional campuses. [For the full proceedings, see ED599096.]
The Influence of School Demographics on the Relationship between Students&apos; Help-Seeking Behavior and Performance and Motivational Measures
Author: Karumbaiah, Shamya
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Help Seeking,Student Behavior,Performance,Institutional Characteristics,Demography,Elementary School Mathematics,Mathematics Achievement,Student Motivation,Social Influences,Self Concept,Mathematics Instruction,Grade 2,Grade 3,Grade 4,Grade 5,Elementary School Students,Urban Areas,Limited English Speaking,Special Education,Economically Disadvantaged

Demographic information often proves useful for finding subpopulations in educational data. Unfortunately, it is often not collected in the log files of online learning systems, which serve as one of the primary sources of data for the Educational Data Mining community. Recent work has sought to address this issue by investigating school-level differences in demographics, which can be used to discover trends in data where individual-level variation may be difficult or impossible to acquire. In this study, we use this approach to investigate the effect of demographic patterns on hint usage in an elementary level mathematics system, comparing this use to performance and motivational measures. In doing so, we expand upon the research into help-seeking behaviors, which typically takes a cognitive approach. Our results suggest the need to better understand what social factors are most likely to motivate help-seeking behaviors, particularly since research on their effectiveness has been mixed. [For the full proceedings, see ED599096.]
Using a Glicko-Based Algorithm to Measure In-Course Learning
Author: Reddick, Rachel
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Measurement Techniques,Mathematics,Assignments,Learning,Ability,Skill Development,Learning Analytics,Online Courses,Difficulty Level,Scoring,Test Items,Computer Assisted Testing,Scores,Competition

One significant challenge in the field of measuring ability is measuring the current ability of a learner while they are learning. Many forms of inference become computationally complex in the presence of time-dependent learner ability, and are not feasible to implement in an online context. In this paper, we demonstrate an approach which can estimate learner skill over time even in the presence of large data sets. We use a rating system derived from the Elo rating system and its relatives, which are commonly used in chess and sports tournaments. A learner's submission of a course assignment is interpreted as a single match. We apply this approach to Coursera's online learning platform, which includes millions of learners who have submitted assignments tens of millions of times in over 3000 courses. We demonstrate that this provides reliable estimates of item difficulty and learner ability. Finally, we address how this scoring framework may be used as a basis for various applications that account for a learner's ability, such as adaptive diagnostic tests and personalized recommendations. [For the full proceedings, see ED599096.]
Utterance-Level Modeling of Indicators of Engaging Classroom Discourse
Author: Stone, Cathlyn
Date: 2019-07-00
Type: Reports - Research
Subjects: Classification,Classroom Communication,Audio Equipment,Feedback (Response),Discourse Analysis,Educational Quality,Computer Software,Teacher Student Relationship,Teaching Methods,Acoustics,Suprasegmentals,Intonation,Scores,Reflection,Speech Communication,Natural Language Processing,Vocabulary,Computational Linguistics,Prediction

We examine the ability of supervised text classification models to identify several discourse properties from teachers' speech with an eye for providing teachers with meaningful automated feedback about the quality of their classroom discourse. We collected audio recordings from 28 teachers from 10 schools in 164 authentic classroom sessions, which we then automatically transcribed into text utterances and then manually coded to identify whether: (1) the utterance contained a question (as opposed to a statement), (2) the question or statement was Instructional vs. Non-Instructional, and (3) the question or statement was Content-Specific. We experimented with Random Forest classifiers and engineered (linguistic, acoustic-prosodic, and contextual) features vs. open-vocabulary n-grams as features to discriminate these discourse variables at the utterance level in a teacher-independent fashion. We achieved AUC scores ranging from 0.71 to 0.77 using open-vocabulary language modeling, which were well above chance (AUC = 0.5), an important step towards our predominant goal of constructing of an automated feedback system for teacher reflection and learning. [For the full proceedings, see ED599096.]
Tutorbot Corpus: Evidence of Human-Agent Verbal Alignment in Second Language Learner Dialogues
Author: Sinclair, Arabella
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Second Language Learning,Second Language Instruction,Dialogs (Language),Teaching Methods,Intelligent Tutoring Systems,Comparative Analysis,Bayesian Statistics,Computational Linguistics,Learning Processes,Vocabulary Development,English (Second Language),Discourse Analysis,Foreign Countries

Prior research has shown that, under certain conditions, Human-Agent (H-A) alignment exists to a stronger degree than that found in Human-Human (H-H) communication. In an H-H Second Language (L2) setting, evidence of alignment has been linked to learning and teaching strategy. We present a novel analysis of H-A and H-H L2 learner dialogues using automated metrics of alignment. Our contributions are twofold: firstly we replicated the reported H-A alignment within an educational context, finding L2 students align to an automated tutor. Secondly, we performed an exploratory comparison of the alignment present in comparable H-A and H-H L2 learner corpora using Bayesian Gaussian Mixture Models (GMMs), finding preliminary evidence that students in H-A L2 dialogues showed greater variability in engagement. [For the full proceedings, see ED599096.]
Sparse Neural Attentive Knowledge-Based Models for Grade Prediction
Author: Morsy, Sara
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Grades (Scholastic),Models,Prediction,Predictor Variables,Knowledge Level,Learning Analytics,Undergraduate Students,Attention,Artificial Intelligence,Course Selection (Students),Courses

Grade prediction for future courses not yet taken by students is important as it can help them and their advisers during the process of course selection as well as for designing personalized degree plans and modifying them based on their performance. One of the successful approaches for accurately predicting a student's grades in future courses is Cumulative Knowledge-based Regression Models (CKRM). CKRM learns shallow linear models that predict a student's grades as the similarity between his/her knowledge state and the target course. A student's knowledge state is built by linearly accumulating the learned provided knowledge components of the courses he/she has taken in the past, weighted by his/her grades in them. However, not all the prior courses contribute equally to the target course. In this paper, we propose a novel Neural Attentive Knowledge-based model (NAK) that learns the importance of each historical course in predicting the grade of a target course. Compared to CKRM and other competing approaches, our experiments on a large real-world dataset consisting of ~1.5 grades show the effectiveness of the proposed NAK model in accurately predicting the students' grades. Moreover, the attention weights learned by the model can be helpful in better designing their degree plans. [For the full proceedings, see ED599096.]
A Randomization-Based Perspective of Analysis of Variance: A Test Statistic Robust to Treatment Effect Heterogeneity
Author: Ding, Peng
Date: 2017-07-24
Type: Reports - Research
Subjects: Statistical Analysis,Statistical Inference,Causal Models,Error Patterns,Statistical Distributions,Probability,Validity,Mathematical Logic

Fisher randomization tests for Neyman's null hypothesis of no average treatment effects are considered in a finite population setting associated with completely randomized experiments with more than two treatments. The consequences of using the F statistic to conduct such a test are examined both theoretically and computationally, and it is argued that under treatment effect heterogeneity, use of the F statistic in the Fisher randomization test can severely inflate the type I error under Neyman's null hypothesis. An alternative test statistic is proposed, its asymptotic distributions under Fisher's and Neyman's null hypotheses are derived, and its advantages demonstrated. [This is the online version of an article published in &quot;Biometrika.&quot;]
Towards the Prediction of Semantic Complexity Based on Concept Graphs
Author: Venant, Rémi
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Semantics,Difficulty Level,Concept Mapping,Graphs,English (Second Language),English Language Learners,Essays,Foreign Countries,Models,Predictive Measurement

The evaluation of text complexity is an important topic in education. While this objective has been addressed by approaches using lexical and syntactic analysis for decades, semantic complexity is less common, and the recent research works that tackle this question rely on machine learning algorithms that are hardly explainable and are not specifically designed to measure this variable. To address this issue, we explore in this paper the engineering of novel features to evaluate conceptual complexity. Through the construction of a knowledge graph that captures the concepts present in a text and their generalized forms, we measure different graph-based metrics to express such a complexity. Eventually, early-stage evaluations based on a well-known public corpus of students' productions show that the use of these metrics significantly improves performance compared to a state-of-the-art binary neural network classifier. [For the full proceedings, see ED599096.]
Using Latent Variable Models to Observe Academic Pathways
Author: Gruver, Nate
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Models,Course Selection (Students),Enrollment,Decision Making,Probability,Classification,College Students,Student Records,Inferences,Management Systems,Comparative Analysis,Student Interests,Private Colleges,Mathematical Models,Visualization,Statistical Analysis

Understanding large-scale patterns in student course enrollment is a problem of great interest to university administrators and educational researchers. Yet important decisions are often made without a good quantitative framework of the process underlying student choices. We propose a probabilistic approach to modelling course enrollment decisions, drawing inspiration from multilabel classification and mixture models. We use ten years of anonymized student transcripts from a large university to construct a Gaussian latent variable model that learns the joint distribution over course enrollments. The models allow for a diverse set of inference queries and robustness to data sparsity. We demonstrate the efficacy of this approach in comparison to others, including deep learning architectures, and demonstrate its ability to infer the underlying student interests that guide enrollment decisions. [For the full proceedings, see ED599096.]
Homeless Report: Health Risk Behaviors of Students Experiencing Homelessness. 2019 Montana Youth Risk Behavior Survey
Author: Montana Office of Public Instruction
Date: 2019-08-00
Type: Reports - Evaluative
Subjects: Health Behavior,Homeless People,Disadvantaged Youth,High School Students,Adolescents,Incidence,Risk,Hispanic American Students,American Indian Students,White Students,Safety,Suicide,Smoking,Drinking,Drug Abuse,Sexuality,Dietetics,Nutrition,Rape,Physical Activity Level,Sleep,Television Viewing,Computer Games,Grades (Scholastic),Bullying,Middle School Students

The Montana Youth Risk Behavior Survey (YRBS) is administered by the Montana Office of Public Instruction every two years to students in grades 7 through 12. The purpose of the survey is to help monitor the prevalence of behaviors that not only influence youth health, but also put youth at risk for the most significant health and social problems that can occur during adolescence. While all schools in Montana can participate in the survey on a volunteer basis, approximately 50 high schools are randomly selected to be included to provide the Montana statewide data to be used in other national YRBS reports. Data used in this report from the 2019 YRBS are based on a random sample survey of Montana high school students. The weighted data results contained in this report can be used to make inferences about the priority health-risk behaviors of all high school students in grades 9 through 12 in all schools in Montana.