ethanporcaro/eric-fulltext-meta huggingface dataset:
Analyzing Early At-Risk Factors in Higher Education E-Learning Courses
Author: Baker, Ryan S.
Date: 2015-06-00
Type: Reports - Research
Subjects: College Students,At Risk Students,Online Courses,Educational Technology,Technology Uses in Education,College Faculty,Predictor Variables,Formative Evaluation,History Instruction,Web Based Instruction,Grades (Scholastic),Academic Achievement,Assignments,Regression (Statistics),Success

College students enrolled in online courses lack many of the supports available to students in traditional face-to-face classes on a campus such as meeting the instructor, having a set class time, discussing topics in-person during class, meeting peers and having the option to speak with them outside of class, being able to visit faculty during office hours, and so on. Instructors also lack these interactions, which typically provide meaningful indications of how students are doing individually and as a cohort. Further, online instructors typically carry a heavier teaching load, making it even more important for them to find quick, reliable, and easily understandable indicators of student progress, so that they can prioritize their interventions based on which students are most in need. In this paper, we study very early predictors of student success and failure. Our data is based on student activity, and is drawn from courses offered online by a large private university. Our data source is the Soomo Learning Environment, which hosts the course content as well as extensive formative assessment. We find that students who access the resources early, continue accessing the resources throughout the early weeks of the course, and perform well on formative activities are more likely to succeed. Through use of these indicators in early weeks, it is possible to derive actionable, understandable, and reasonably reliable predictions of student success and failure. [For complete proceedings, see ED560503.]
Automated Session-Quality Assessment for Human Tutoring Based on Expert Ratings of Tutoring Success
Author: Nye, Benjamin D.
Date: 2015-06-00
Type: Reports - Research
Subjects: Tutoring,Instructional Effectiveness,Tutors,Models,Outcomes of Education,Data Analysis,Computer Assisted Instruction,Classification,Statistical Analysis,Correlation,Evaluators,Accuracy,Natural Language Processing,Dialogs (Language),Regression (Statistics),Measurement Techniques

Archived transcripts from tens of millions of online human tutoring sessions potentially contain important knowledge about how online tutors help, or fail to help, students learn. However, without ways of automatically analyzing these large corpora, any knowledge in this data will remain buried. One way to approach this issue is to train an estimator for the learning effectiveness of an online tutoring interaction. While significant work has been done on automated assessment of student responses and artifacts (e.g., essays), automated assessment has not traditionally automated assessments of human-to-human tutoring sessions. In this work, we trained a model for estimating tutoring session quality based on a corpus of 1438 online tutoring sessions rated by expert tutors. Each session was rated for evidence of learning (outcomes) and educational soundness (process). Session features for this model included dialog act classifications, mode classifications (e.g., Scaffolding), statistically distinctive subsequences of such classifications, dialog initiative (e.g., statements by tutor vs. student), and session length. The model correlated more highly with evidence of learning than educational soundness ratings, in part due to the greater difficulty of classifying tutoring modes. This model was then applied to a corpus of 242k online tutoring sessions, to examine the relationships between automated assessments and other available metadata (e.g., the tutor's self-assessment). On this large corpus, the automated assessments followed similar patterns as the expert rater's assessments, but with lower overall correlation strength. Based on the analyses presented, the assessment model for online tutoring sessions emulates the ratings of expert human tutors for session quality ratings with a reasonable degree of accuracy. [For complete proceedings, see ED560503.]
Automatic Classification of Question &amp; Answer Discourse Segments from Teacher's Speech in Classrooms
Author: Blanchard, Nathaniel
Date: 2015-06-00
Type: Reports - Research
Subjects: Automation,Classification,Questioning Techniques,Speech,Classroom Communication,Middle School Teachers,Audio Equipment

Question-answer (Q&amp;A) is fundamental for dialogic instruction, an important pedagogical technique based on the free exchange of ideas and open-ended discussion. Automatically detecting Q&amp;A is key to providing teachers with feedback on appropriate use of dialogic instructional strategies. In line with this, this paper studies the possibility of automatically detecting segments of Q&amp;A in live classrooms based solely on audio recordings of teacher speech. The proposed approach has two steps. First, teacher utterances were automatically detected from the audio stream via an amplitude envelope thresholding-based approach. Second, supervised classifiers were trained on speech-silence patterns derived from the teacher utterances. The best models were able to detect Q&amp;A segments in windows of 90 seconds with an AUC (Area Under the Receiver Operating Characteristic Curve) of 0.78 in a manner that generalizes to new classes. Implications of the findings for automatic coding of classroom discourse are discussed. [For complete proceedings, see ED560503.]
Understanding Student Discipline Practices in Charter Schools: A Research Agenda
Author: Denice, Patrick
Date: 2015-09-00
Type: Reports - Research
Subjects: Discipline,Charter Schools,Public Schools,Comparative Analysis,Discipline Policy,Teacher Supply and Demand,Faculty Mobility,School Culture,Educational Research,Research Methodology

Fair use of exclusionary discipline is a rising concern in public schools. At issue is whether this type of discipline is disproportionately applied to certain groups of students and whether some charter schools use it more frequently. For the first time, data compiled by the Department of Education's Office for Civil Rights capture discipline practices from &quot;all&quot; public schools, allowing for comparison between the traditional public and charter sectors. However, because they are reported at the school level, not at the individual student level, these data paint an incomplete picture. To truly understand discipline practices between and within school sectors, a panel of experts recommends a more comprehensive approach to capturing discipline data and evaluating and comparing school discipline practices. A robust research agenda on school discipline might include the reasons why different approaches to discipline policy are developed, how schools define and conceptualize discipline practices, the impact of discipline practices on teacher supply and turnover, the interplay between school culture and discipline and the effects of exclusionary discipline on the affected students and their peers and teachers.
Considering the Influence of Prerequisite Performance on Wheel Spinning
Author: Wan, Hao
Date: 2015-06-00
Type: Reports - Research
Subjects: Skill Development,Problem Solving,Knowledge Level,Learning Processes,Predictor Variables,Intelligent Tutoring Systems,Mastery Learning,Performance,Models,Cognitive Ability,Mathematics Instruction,Mathematics Skills,Comparative Analysis,Student Behavior

The phenomenon of wheel spinning refers to students attempting to solve problems on a particular skill, but becoming stuck due to an inability to learn the skill. Past research has found that students who do not master a skill quickly tend not to master it at all. One question is why do students wheel spin? A plausible hypothesis is that students become stuck on a skill because they do not understand the necessary prerequisite knowledge, and so are unable to learn the current skill. We analyzed data from the ASSISTments system, and determined the impact of how student performance on prerequisite skills influenced ability to learn postrequisite skills. We found a strong gradient with respect to knowledge of prerequisites: students in the bottom 20% of pre-required knowledge exhibited wheel spinning behavior 50% of the time, while those in the top 20% of pre-required knowledge exhibited wheel spinning behavior only 10% of the time. This information is a statistically reliable predictor, and considering it results in a modest improvement in our ability to detect wheel spinning behaviors: R2 improves from 0.264 to 0.268, and AUC improves from 0.884 to 0.888. [For complete proceedings, see ED560503.]
Student Privacy and Educational Data Mining: Perspectives from Industry
Author: Sabourin, Jennifer
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Privacy,Student Records,Data Processing,Data Collection,Information Management,Information Policy,Social Responsibility,Public Opinion,Student Rights,Disclosure,Community Role,School Business Relationship,Accountability,Activism,Research Needs,Educational Legislation,Parent Rights

While the field of educational data mining (EDM) has generated many innovations for improving educational software and student learning, the mining of student data has recently come under a great deal of scrutiny. Many stakeholder groups, including public officials, media outlets, and parents, have voiced concern over the privacy of student data and their efforts have garnered national attention. The momentum behind and scrutiny of student privacy has made it increasingly difficult for EDM applications to transition from academia to industry. Based on experience as academic researchers transitioning into industry, we present three primary areas of concern related to student privacy in practice: policy, corporate social responsibility, and public opinion. Our discussion will describe the key challenges faced within these categories, strategies for overcoming them, and ways in which the academic EDM community can support the adoption of innovative technologies in large-scale production. [For complete proceedings, see ED560503.]
Choosing to Interact: Exploring the Relationship between Learner Personality, Attitudes, and Tutorial Dialogue Participation
Author: Ezen-Can, Aysu
Date: 2015-06-00
Type: Reports - Research
Subjects: Intelligent Tutoring Systems,Natural Language Processing,Computer Science Education,Problem Solving,Student Characteristics,Personality Traits,Profiles,Student Attitudes,Predictor Variables,Interaction,Learner Engagement,Task Analysis,Student Participation,Extraversion Introversion,Models,Individual Differences,Multiple Regression Analysis,College Students,Man Machine Systems

The tremendous effectiveness of intelligent tutoring systems is due in large part to their interactivity. However, when learners are free to choose the extent to which they interact with a tutoring system, not all learners do so actively. This paper examines a study with a natural language tutorial dialogue system for computer science, in which students interacted with the JavaTutor system through natural language dialogue over the course of problem solving. We explore the relationship between students' level of dialogue interaction and learner characteristics including personality profile and pre-existing attitudes toward the learning task. The results show that these learner characteristics are significant predictors of the extent to which students engage in dialogue with the tutoring system, as well as the number of task actions students make. By identifying students who may not engage with tutoring systems as readily, this work constitutes a step toward building adaptive systems that successfully support a variety of students with different attitudes and personalities. [For complete proceedings, see ED560503.]
Comparing Novice and Experienced Students within Virtual Performance Assessments
Author: Jiang, Yang
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Performance Based Assessment,Virtual Classrooms,Comparative Analysis,Novices,Expertise,Inquiry,Middle School Students,Prior Learning,Science Process Skills,Computer Assisted Testing,Vignettes,Grade 7,Grade 8,Problem Solving,Time on Task,Student Motivation,Student Behavior,Cognitive Style,Achievement Gains,Achievement Rating

Inquiry skills are an important part of science education standards. There has been particular interest in verifying that these skills can transfer across domains and instructional contexts [4,15,16]. In this paper, we study transfer of inquiry skills, and the effects of prior practice of inquiry skills, using data from over 2000 middle school students using an open-ended immersive virtual environment called Virtual Performance Assessments (VPAs) that aims to assess science inquiry skills in multiple virtual scenarios. To this end, we assessed and compared student performance and behavior within VPA between two groups: novice students who had not used VPA previously, and experienced students who had previously completed a different VPA scenario. Our findings suggest that previous experience in a different scenario prepared students to transfer inquiry skills to a new one, leading these experienced students to be more successful at identifying a correct final conclusion to a scientific question, and at designing causal explanations about these conclusions, compared to novice students. On the other hand, a positive effect of novelty was found for motivation. To better understand these results, we examine the differences in student patterns of behavior over time, between novice and experienced students. [For complete proceedings, see ED560503.]
Do Country Stereotypes Exist in PISA? A Clustering Approach for Large, Sparse, and Weighted Data
Author: Saarela, Mirka
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Weighted Scores,Stereotypes,Standardized Tests,Student Characteristics,Cluster Grouping,Data Analysis,Mathematical Applications,Data Processing,National Norms,International Studies,Foreign Countries

Certain stereotypes can be associated with people from different countries. For example, the Italians are expected to be emotional, the Germans functional, and the Chinese hard-working. In this study, we cluster all 15-year-old students representing the 68 different nations and territories that participated in the latest Programme for International Student Assessment (PISA 2012). The hypothesis is that the students will start to form their own country groups when clustered according to the scale indices that summarize many of the students' characteristics. In order to meet PISA data analysis requirements, we use a novel combination of our previously published algorithmic components to realize a weighted sparse data clustering approach. This enables us to work with around half a million observations with large number of missing values, which represent the population of more than 24 million students globally. Three internal cluster indices suitable for sparse data are used to determine the number of clusters and the whole procedure is repeated recursively to end up with a set of clusters on three different refinement levels. The results show that our final clusters can indeed be explained by the actual student performance but only to a marginal degree by the country. [For complete proceedings, see ED560503.]
Beyond Prediction: First Steps toward Automatic Intervention in MOOC Student Stopout
Author: Whitehill, Jacob
Date: 2015-06-00
Type: Reports - Research
Subjects: Online Courses,Stopouts,Intervention,Automation,Classification,Models,Decision Making,Student Surveys,Response Rates (Questionnaires),Accuracy

High attrition rates in massive open online courses (MOOCs) have motivated growing interest in the automatic detection of student &quot;stopout&quot;. Stopout classifiers can be used to orchestrate an intervention before students quit, and to survey students dynamically about why they ceased participation. In this paper we expand on existing stop-out detection research by (1) exploring important elements of classifier design such as generalizability to new courses; (2) developing a novel framework inspired by control theory for how to use a classifier's outputs to make intelligent decisions; and (3) presenting results from a &quot;dynamic survey intervention&quot; conducted on 2 HarvardX MOOCs, containing over 40000 students, in early 2015. Our results suggest that surveying students based on an automatic stopout classifier achieves higher response rates compared to traditional post-course surveys, and may boost students' propensity to &quot;come back&quot; into the course. [For complete proceedings, see ED560503.]
The Impact of Incorporating Student Confidence Items into an Intelligent Tutor: A Randomized Controlled Trial
Author: Lang, Charles
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Educational Assessment,Intelligent Tutoring Systems,Self Esteem,Mathematics Instruction,Electronic Learning,Computer Assisted Instruction,Research Methodology,Statistical Significance,Feedback (Response),Accuracy,Predictor Variables,Academic Achievement,Middle School Students,Grade 8,Educational Benefits

For at least the last century researchers have advocated the use of student confidence as a form of educational assessment and the growth of online and mobile educational software has made the implementation of this measurement far easier. The following short paper discusses our first study of the dynamics of student confidence in an online math tutor. We used a randomized controlled trial to test whether asking students about their confidence while using an Intelligent Tutor altered their performance. We observe that (1) Asking students about their confidence has no statistically significant impact on any of several performance measures (2) Student confidence is more easily reduced by negative feedback (being incorrect) than increased by positive feedback (being correct) and (3) confidence accuracy may be a useful predictor of student behavior. This paper demonstrates how psychological ideas can be imported into Educational Data Mining and our findings point to the possibility of using student confidence to better predict performance and differentiate between students based on the way they approach items. [For complete proceedings, see ED560503.]
Learning Instructor Intervention from MOOC Forums: Early Results and Issues
Author: Kumar, Muthu
Date: 2015-06-00
Type: Reports - Research
Subjects: Intervention,Open Education,Large Group Instruction,Group Discussion,Prediction,Facilitators (Individuals),Online Courses,Computer Mediated Communication,Teacher Role,Predictive Measurement,Predictor Variables,Discourse Analysis,Decision Making

With large student enrollment, MOOC instructors face the unique challenge in deciding when to intervene in forum discussions with their limited bandwidth. We study this problem of &quot;instructor intervention.&quot; Using a large sample of forum data culled from 61 courses, we design a binary classifier to predict whether an instructor should intervene in a discussion thread or not. By incorporating novel information about a forum's type into the classification process, we improve significantly over the previous state-of-the-art. We show how difficult this decision problem is in the real world by validating against indicative human judgment, and empirically show the problem's sensitivity to instructors' intervention preferences. We conclude this paper with our take on the future research issues in intervention. [For complete proceedings, see ED560503.]
Investigating How Student's Cognitive Behavior in MOOC Discussion Forums Affect Learning Gains
Author: Wang, Xu
Date: 2015-06-00
Type: Reports - Research
Subjects: Online Courses,Discussion Groups,Student Behavior,Cognitive Processes,Achievement Gains,Learning,Content Analysis,Student Participation,Automation,Computation,Pretesting

While MOOCs undoubtedly provide valuable learning resources for students, little research in the MOOC context has sought to evaluate students' learning gains in the environment. It has been long acknowledged that conversation is a significant way for students to construct knowledge and learn. However, rather than studying learning in MOOC discussion forums, the thrust of current research in that context has been to identify factors that predict dropout. Thus, cognitively relevant student behavior in the forums has not been evaluated for its impact on cognitive processes and learning. In this paper, we adopt a content analysis approach to analyze students' cognitively relevant behaviors in a MOOC discussion forum and further explore the relationship between the quantity and quality of that participation with their learning gains. As an integral part of our approach, we built a computational model to automate the analysis so that it is possible to extend the content analysis to all communication that occurred in the MOOC. We identified significant associations between discourse behavior and learning. Theoretical and practical implications are discussed. [For complete proceedings, see ED560503.]
Predicting Student Performance in a Collaborative Learning Environment
Author: Olsen, Jennifer K.
Date: 2015-06-00
Type: Reports - Research
Subjects: Educational Environment,Predictive Measurement,Predictor Variables,Cooperative Learning,Regression (Statistics),Models,Problem Solving,Data Collection,Academic Records,Data Processing,Progress Monitoring,Goodness of Fit,Fractions,Grade 4,Grade 5,Prediction,Predictive Validity,Achievement Gains,Learning Strategies,Learning Processes

Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression model for modeling individual learning, often used in conjunction with knowledge component models and tutor log data. The extended model predicts performance of students solving problems collaboratively with an ITS. Specifically, we address the open questions: Does adding collaborative features to a standard AFM provide a better fit than the standard AFM? Also, does the impact of these features change based on the nature of the knowledge (conceptual v. procedural) that is being acquired? In our extended AFM models, we include a variable indicating if students are working individually or in pairs. Also, for students working collaboratively, we model both the influence on learning of being helped by a partner and helping a partner. For each model, we analyzed conceptual and procedural datasets separately. We found that both collaborative features (being helped and helping) improve the model fit. In addition, the impact of these features differs between the collaborative and procedural datasets, suggesting collaboration may affect procedural and collaborative learning differently. By adding collaborative learning features into an existing regression model for individual learning over a series of skill opportunities, we gain a better understanding of the impact that working with a partner has on student learning, when working with a step-based collaborative ITS. This work also provides an improved model to better predict when students have reached mastery while collaborating. [For complete proceedings, see ED560503.]