ethanporcaro/eric-fulltext-meta huggingface dataset:
What Can We Learn from College Students&apos; Network Transactions? Constructing Useful Features for Student Success Prediction
Author: Pytlarz, Ian
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: College Freshmen,Grade Point Average,At Risk Students,Academic Achievement,Computer Networks,Attendance,Learner Engagement,On Campus Students,Study Habits,Geographic Location,Student Behavior,Artificial Intelligence,Correlation,Data Analysis,Prediction

Identifying at-risk students at an early stage is a challenging task for colleges and universities. In this paper, we use students' oncampus network traffic volume to construct several useful features in predicting their first semester GPA. In particular, we build proxies for their attendance, class engagement, and out-of-class study hours based on their network traffic volume. We then test how much these network-based features can increase the performance of a model with only conventional features (e.g., demographics, high school GPA, standardized test scores, etc.). We labeled students as &quot;above median&quot; and &quot;below median&quot; students based on their first term GPA. Several machine learning models were then applied, ranging from logistic regression, SVM, and random forests, to AdaBoost. The result shows that the model with network-based features consistently outperforms the ones without, in terms of accuracy, f1 score, and AUC. Given that network activity data is readily available data in most colleges and universities, this study provides practical insights on how to build more powerful models to predict student success. [For the full proceedings, see ED593090.]
Using Student Logs to Build Bayesian Models of Student Knowledge and Skills
Author: Nguyen, Huy
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Bayesian Statistics,Intelligent Tutoring Systems,Data Analysis,Error Patterns,Student Evaluation,Skills,Problem Solving,Prediction,Accuracy

Recent works on Intelligent Tutoring Systems have focused on more complicated knowledge domains, which pose challenges in automated assessment of student performance. In particular, while the system can log every user action and keep track of the student's solution state, it is unable to determine the hidden intermediate steps leading to such state or what the student is trying to achieve. In this paper, we show that this information can be acquired through data mining, along with the type, frequency and context of errors that students made. Our technique has been implemented as part of the student model in a tutor that teaches red-black trees. The system was evaluated on three semesters of student data. Analysis of the results shows that the proposed framework of error analysis can help the system in predicting student performance with good accuracy and the instructor in determining difficulties that students encounter, both individually and collectively as a class. [For the full proceedings, see ED593090.]
Exploring Collaboration Using Motion Sensors and Multi-Modal Learning Analytics
Author: Reilly, Joseph M.
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Cooperative Learning,Programming,Nonverbal Communication,Motion,Time,Human Posture,Data Analysis,Learning,Artificial Intelligence,Equipment Utilization,Proximity,Group Activities

In this paper, we describe the analysis of multimodal data collected on small collaborative learning groups. In a previous study, we asked pairs (N=84) with no programming experience to program a robot to solve a series of mazes. The quality of the dyad's collaboration was evaluated, and two interventions were implemented to support collaborative learning. In the current study, we present the analysis of Kinect[TM] and speech data gathered on dyads during the programming task. We first show how certain movements and patterns of gestures correlate positively with collaboration and learning gains. We next use clustering algorithms to find prototypical body positions of participants and relate amount of time spent in certain postures with learning gains as in Schneider &amp; Blikstein's work. Finally, we examine measures of proxemics and physical orientation within the dyads to explore how to detect good collaboration. We discuss the relevance of these results to designing and assessing collaborative small group activities and outline future work related to other collected sensor data. [For the full proceedings, see ED593090.]
Identifying User Engagement Patterns in an Online Video Discussion Platform
Author: Lee, Seung Yeon
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Video Technology,Discussion (Teaching Technique),Computer Mediated Communication,Learner Engagement,Asynchronous Communication,Users (Information),Interaction,Hypermedia,Student Behavior,Educational Technology

In this study we conducted behavioral analyses to gain insights into patterns of user interaction in a video discussion platform, &quot;Vialogues.&quot; Vialogues provides an asynchronous online discussion environment around video. Using a hierarchical clustering analysis on users' clickstream data, we identified four different behavior patterns: (1) video watchers with no discussion activity, (2) opinion seekers and active repliers with little to no video watching activity, (3) users who watched and discussed videos, and (4) users focused on viewing and/or creating metadata. Despite being the largest group, Cluster (3) had the least classifiable characteristics. Consequently we conducted additional analyses to examine finer-grained user segments. For each segment we created a transition network using weighted directed networks in order to understand the transition pattern between two consecutive click activities. [For the full proceedings, see ED593090.]
Is the Doer Effect Robust across Multiple Data Sets?
Author: Koedinger, Kenneth R.
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,Time Management,Causal Models,Student Behavior,Learning,College Students,Psychology,Biology,Statistics,Computer Science,Information Science

The &quot;doer effect&quot; is the assertion that the amount of interactive practice activity a student engages in is much more predictive of learning than the amount of passive reading or watching video the same student engages in. Although the evidence for a doer effect is now substantial, the evidence for a causal doer effect is not as well developed. To address this, we mined data for evidence of a causal doer effect across multiple domains. We examined data from two online courses in Psychology, one in Biology, one in Statistics, and two in Information Science, applying causal discovery algorithms in Tetrad to each. Assuming that factors driving a student's choices regarding how to spend their time in an online course are temporally prior to their performance on quizzes and exams, we found evidence of a causal relationship in every domain we studied. We did not find evidence that a unique causal model held in every domain we studied, but when we estimated the size of the causal relationships in the models we found in each domain, we did find evidence in every case that doing has a much stronger quantitative effect on learning than either reading or watching video. This work may be the first EDM effort to explore the generalizability of a causal claim about learning across multiple datasets from a variety of courses and contexts of use. It makes vivid the role of causal data mining algorithms in educational research. The evidence presented furthers the case for doer effect causality, but also recommends a need for richer data with more student background and learning process variables to better isolate causal directionality without assumptions about temporal order and unmeasured confounds. [For the full proceedings, see ED593090.]
Standard Error Considerations on AFM Parameters
Author: Durand, Guillaume
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Error of Measurement,Models,Goodness of Fit,Predictive Validity,Data Analysis,Learning,Intelligent Tutoring Systems,Geometry,Mathematics Achievement,Computation

Knowledge tracing is a fundamental area of educational data modeling that aims at gaining a better understanding of the learning occurring in tutoring systems. Knowledge tracing models fit various parameters on observed student performance and are evaluated through several goodness of fit metrics. Fitted parameter values are of crucial interest in order to diagnose learning mastery as well as knowledge models and qualitative aspects of the learning environment. Unfortunately, parameter values are rarely associated with standard errors or confidence intervals, both of which are critical information to validate the inferences that can be made from the model. Taking the example of the Additive Factor Model, we describe how to obtain standard errors on the model parameters. We propose two methods to compute those and discuss results obtained on a public dataset. [For the full proceedings, see ED593090.]
The Influence of Task Activity and the Learner&apos;s Personal Characteristics on Self-Confidence during an Online Explanation Activity with a Conversational Agent
Author: Hayashi, Yugo
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Self Efficacy,Task Analysis,Cognitive Processes,Individual Characteristics,Intelligent Tutoring Systems,College Students,Questioning Techniques,Metacognition,Learning Activities,Prediction,Cognitive Psychology,Foreign Countries,Majors (Students),Discourse Analysis,Correlation,Feedback (Response),Computer Simulation,Interpersonal Competence,Autism,Pervasive Developmental Disorders,Imagination,Attention Control,Communication Skills,Creativity,Web Based Instruction

This study investigated the factors underlying the estimation of learner self-confidence during explanations with a conversational agent in an online explanation task. Based on reviews of previous studies, we focused on how factors such as the learner's task activities and personal characteristics can be predictors. To examine these points, we used an online explanation task, which was run by a conversational agent for students in a classroom on information processing psychology (n=99). We asked the participants to make text-based explanations to the agent in a question-and-answer (Q&amp;A) style, and clarified a particular concept that was taught in a previous lecture in the class. The results show that an increase in the amount of actual task work for explanations and personal characteristics (such as social skills) helped to predict higher self-confidence. The findings have implications not only for knowledge of how such factors might influence learner self-confidence in an online explanation task, but also for the design of online tutoring systems that can automatically detect learner confidence using these variables, and facilitate learning adequately based on such data. {For the full proceedings, see ED593090.]
Your Actions or Your Associates? Predicting Certification and Dropout in MOOCs with Behavioral and Social Features
Author: Gitinabard, Niki
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,Dropouts,Dropout Characteristics,Prediction,Student Behavior,Social Behavior,Certification,Academic Failure,Models,Reliability

The high level of attrition and low rate of certification in Massive Open Online Courses (MOOCs) has prompted a great deal of research. Prior researchers have focused on predicting dropout based upon behavioral features such as student confusion, clickstream patterns, and social interactions. However, few studies have focused on combining student logs with forum data. In this work, we use data from two different offerings of the same MOOC. We conduct a survival analysis to identify likely dropouts. We then examine two classes of features, social and behavioral, and apply a combination of modeling and feature-selection methods to identify the most relevant features to predict both dropout and certification. We examine the utility of three different model types and we consider the impact of different definitions of dropout on the predictors. Finally we assess the reliability of the models over time by evaluating whether or not models from week 1 can predict dropout in week 2, and so on. The outcomes of this study will help instructors identify students likely to fail or dropout as soon as the first two weeks and provide them with more support. [For the full proceedings, see ED593090.]
Who Participates? An Analysis of School Participation Decisions in Two Voucher Programs in the United States. Policy Analysis No. 848
Author: DeAngelis, Corey A.
Date: 2018-09-17
Type: Reports - Research
Subjects: Educational Vouchers,School Choice,Private Schools,Participation,Educational Quality,Tuition,Enrollment,Elementary Secondary Education,Achievement Rating

The expansion of private school choice programs has been accompanied by a growing call for regulation of those programs. Individual private schools decide whether to participate in voucher programs based on expected benefits and expected costs. An unintended consequence of attaching heavy government regulation to voucher programs is that it raises the costs of participation, which could reduce the number of private schools available to the children who need them the most. Moreover, the authors hypothesize that lower-quality schools are more likely to participate in regulated voucher programs because they are the most desperate for additional enrollment and funding. They empirically examine this hypothesis using school-level data from two of the most highly regulated voucher programs in the United States: the Milwaukee Parental Choice Program (MPCP) and the Ohio Educational Choice Scholarship Program (EdChoice). Using tuition, enrollment, and customer review scores as proxies for school quality, they find strong evidence suggesting that lower-quality private schools are more likely to participate in voucher programs in both locations. This result is a big problem if their aim is to provide high-quality educational options for children who are currently in government schools.
Can Textbook Annotations Serve as an Early Predictor of Student Learning?
Author: Winchell, Adam
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Textbooks,Biology,Documentation,Science Instruction,Science Tests,College Students,Learning Strategies,Reading Comprehension,Inferences,Bayesian Statistics,Correlation,Electronic Publishing,Notetaking,Study Habits,Artificial Intelligence,Multiple Choice Tests,Adults

When engaging with a textbook, students are inclined to highlight key content. Although students believe that highlighting and subsequent review of the highlights will further their educational goals, the psychological literature provides no evidence of benefits. Nonetheless, a student's choice of text for highlighting may serve as a window into their mental state--their level of comprehension, grasp of the key ideas, reading goals, etc. We explore this hypothesis via an experiment in which 198 participants read sections from a college-level biology text, briefly reviewed the text, and then took a quiz on the material. During initial reading, participants were able to highlight words, phrases, and sentences, and these highlights were displayed along with the complete text during the subsequent review. Consistent with past research, the amount of highlighted material is unrelated to quiz performance. However, our main goal is to examine highlighting as a data source for inferring student understanding. We explored multiple representations of the highlighting patterns and tested Bayesian linear regression and neural network models, but we found little or no relationship between a student's highlights and quiz performance. Our long-term goal is to design digital textbooks that serve not only as conduits of information into the mind of the reader, but also allow us to draw inferences about the reader at a point where interventions may increase the effectiveness of the material. [For the full proceedings, see ED593090.]
Open Enrollment: What Is the Issue and Why Does It Matter? Policy Snapshot
Author: Wixom, Micah Ann
Date: 2019-01-00
Type: Reports - Evaluative
Subjects: Open Enrollment,School Choice,Transfer Policy,Transfer Students,State Policy,State Legislation,Educational Legislation

Open enrollment is a form of school choice allowing students to select and transfer to a school of their choice, rather than attending a school based on where they live. Students may be allowed to transfer to another school within their resident school district (intradistrict) or to a school in another district (interdistrict). Some state leaders look to open enrollment as an option to expand school choice options within the public sector. School districts have long engaged in transfer agreements with each other for reasons of practicality and convenience. States started extending student transfers -- both intradistrict and interdistrict -- as a form of school choice in the past few decades. The specifics of these policies vary across states. They can allow for voluntary or mandatory participation at the district level; and they can allow for intradistrict transfer, interdistrict transfer or both. This Policy Snapshot briefly examines open enrollment, reviews which states enacted bills related to open enrollment from 2014 to 2018 and includes examples of recently enacted legislation.
Does Deep Knowledge Tracing Model Interactions among Skills?
Author: Montero, Shirly
Date: 2018-07-00
Type: Reports - Research
Subjects: Artificial Intelligence,Individualized Instruction,Knowledge Level,Bayesian Statistics,Predictive Validity,Performance Factors,Probability,Models

Personalized learning environments requiring the elicitation of a student's knowledge state have inspired researchers to propose distinct models to understand that knowledge state. Recently, the spotlight has shone on comparisons between traditional, interpretable models such as Bayesian Knowledge Tracing (BKT) and complex, opaque neural network models such as Deep Knowledge Tracing (DKT). Although DKT appears to be a powerful predictive model, little effort has been expended to dissect the source of its strength. We begin with the observation that DKT differs from BKT along three dimensions: (1) DKT is a neural network with many free parameters, whereas BKT is a probabilistic model with few free parameters; (2) a single instance of DKT is used to model all skills in a domain, whereas a separate instance of BKT is constructed for each skill; and (3) the input to DKT interlaces practice from multiple skills, whereas the input to BKT is separated by skill. We tease apart these three dimensions by constructing versions of DKT which are trained on single skills and which are trained on sequences separated by skill. Exploration of three data sets reveals that dimensions (1) and (3) are critical; dimension (2) is not. Our investigation gives us insight into the structural regularities in the data that DKT is able to exploit but that BKT cannot. [For the full proceedings, see ED593090.]
Data-Driven Approach towards a Personalized Curriculum
Author: Backenköhler, Michael
Date: 2018-00-00
Type: Speeches/Meeting Papers
Subjects: Data,Course Selection (Students),Information Utilization,Individualized Instruction,College Curriculum,Undergraduate Students,Computer Science Education,Foreign Countries,Statistical Analysis,Grade Prediction,Time to Degree,Graphs

Course selection can be a daunting task, especially for first year students. Sub-optimal selection can lead to bad performance of students and increase the dropout rate. Given the availability of historic data about student performances, it is possible to aid students in the selection of appropriate courses. Here, we propose a method to compose a personalized curriculum for a given student. We develop a modular approach that combines a context-aware grade prediction with statistical information on the useful temporal ordering of courses. This allows for meaningful course recommendations, both for fresh and senior students. We demonstrate the approach using the data of the computer science Bachelor students at Saarland University. [For the full proceedings, see ED593090.]
Feature Extraction for Classifying Students Based on Their Academic Performance
Author: Polyzou, Agoritsa
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Low Achievement,Predictor Variables,Classification,Student Characteristics,Undergraduate Students,Grades (Scholastic),Student Interests,Technology Uses in Education,Educational Technology,Artificial Intelligence,At Risk Students

Developing tools to support students and learning in a traditional or online setting is a significant task in today's educational environment. The initial steps towards enabling such technologies using machine learning techniques focused on predicting the student's performance in terms of the achieved grades. The disadvantage of these approaches is that they do not perform as well in predicting poor-performing students. The objective of our work is two-fold. First, in order to overcome this limitation, we explore if poorly performing students can be more accurately predicted by formulating the problem as binary classification. Second, in order to gain insights as to which are the factors that can lead to poor performance, we engineered a number of human-interpretable features that quantify these factors. These features were derived from the students' grades from the University of Minnesota, an undergraduate public institution. Based on these features, we perform a study to identify different student groups of interest, while at the same time, identify their importance. [For the full proceedings, see ED593090.]
Estimating the Treatment Effect of New Device Deployment on Uruguayan Students&apos; Online Learning Activity
Author: Aguerrebere, Cecilia
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Management Systems,Foreign Countries,Resource Allocation,Learner Engagement,Laptop Computers,Mathematics Instruction,Intelligent Tutoring Systems,Grade 6,Problem Solving,Program Effectiveness,Elementary School Students,Cost Effectiveness,Quasiexperimental Design

When implementing large-scale educational computing initiatives (e.g., One Laptop Per Child) it is vital to allocate resources for training, support, and device deployment judiciously. One question that arises is how learners' engagement with online educational resources is affected by receiving a new computer; do the benefits justify the costs? In this paper, we perform a quasi-experimental analysis to measure the effect of new device deployment on students' online learning activity, operationalized as either the number of interaction events with their LMS, or the number of attempted exercises in their math ITS. The focus is on 6th-grade learners in Uruguay, which to-date has delivered over 750,000 computers to pupils nationwide. Our results suggest that, relative to learners' online learning activity before device deployment, the absolute effects are small but the relative effect are stat. sig. and surprisingly strong: the estimated relative increase on 2016 students' overall LMS activity is 49%. The effects are positive for both 2015 and 2016 and persist several months after device delivery. Moreover, we find that students attempt to solve stat. sig. more (88%) math problems during the month after they receive a new device. We discuss possible reasons and implications for large-scale educational computing programs. [For the full proceedings, see ED593090.]