ethanporcaro/eric-fulltext-meta huggingface dataset:
Clustering the Learning Patterns of Adults with Low Literacy Skills Interacting with an Intelligent Tutoring System
Author: Fang, Ying
Date: 2018-00-00
Type: Speeches/Meeting Papers
Subjects: Intelligent Tutoring Systems,Educational Technology,Technology Uses in Education,Adult Literacy,Teaching Methods,Student Characteristics,Adult Students,Reading Comprehension,Reading Skills,Reading Difficulties,Accuracy,Achievement Tests,Behavior Patterns

A common goal of Intelligent Tutoring Systems (ITS) is to provide learning environments that adapt to the varying abilities and characteristics of users. To do this, researchers must identify the learning patterns exhibited by those interacting with the system. In the present work, we use clustering analysis to capture learning patterns in over 250 adults who used the ITS, CSAL (Center for the Study of Adult Literacy) &quot;AutoTutor,&quot; to gain reading comprehension skills. AutoTutor has conversational agentsth at teach literacy adults with low literacy skills comprehension strategies in 35 lessons. These comprehension strategies align with one or more of the following levels specified in the Graesser-McNamara theoretical framework of comprehension: &quot;word,&quot; &quot;textbase,&quot; &quot;situation model&quot; and &quot;rhetorical structure.&quot; We used the adult learners' average response times per question and performance across lessons to cluster the students' learning behavior. Performance was measured as the proportion of 3-alternative-response questions answered correctly. Lessons were coded on one of the four theoretical levels of comprehension. Results of the cluster analyses converged on four types of learners: proficient readers, struggling readers, conscientious readers and disengaged readers. Proficient readers were fast and accurate; struggling readers worked slowly but were not accurate; conscientious readers worked slowly and performed comparatively well; disengaged readers were fast but did not perform well. Interestingly, the behaviors of learners in different clusters varied across the four theoretical levels. Identifying types of readers can enhance the adaptivity of AutoTutor by allowing for more personalized feedback and interventions designed for particular learning behaviors. [For the full proceedings, see ED593090. For the corresponding grantee submission, see ED588062.]
Finding Topics in Enrollment Data
Author: Motz, Benjamin
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Data Collection,Data Analysis,Enrollment,Higher Education,College Students,Student Records,Classification,Educational Research,Models,Information Retrieval

Analyses of student data in post-secondary education should be sensitive to the fact that there are many different topics of study. These different areas will interest different kinds of students, and entail different experiences and learning activities. However, it can be challenging to identify the distinct academic themes that students might pursue in higher education, where students commonly have the freedom to sample from thousands of courses in dozens of degree programs. In this paper, we describe the use of topic modeling to identify distinct themes of study and classify students according their observed course enrollments, and present possible applications of this technique for the broader field of educational data mining. [For the full proceedings, see ED593090.]
Identifying Profiles of Collaborative Problem Solvers in an Online Electronics Environment
Author: Andrews-Todd, Jessica
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Problem Solving,Cooperation,Student Behavior,Data Analysis,Identification,Electronics,Profiles,College Students,Multivariate Analysis

In this paper, we describe a theoretically-grounded data mining approach to identify types of collaborative problem solvers based on students' interactions with an online simulation-based task about electronics concepts. In our approach, we developed an ontology to identify the theoretically-grounded features of collaborative problem solving (CPS). After interaction with the task, students' log files were tagged for the presence of 11 CPS skills from the ontology. The frequencies of the skills were clustered to identify four unique profiles of collaborative problem solvers--Chatty Doers, Social Loafers, Group Organizers, and Active Collaborators. Relationships among cluster membership, task performance, and external ratings of collaboration provide initial validity evidence that these are meaningful profiles of collaborative problem solvers. [For the full proceedings, see ED593090.]
Mining Student Misconceptions from Pre- and Post-Test Data
Author: Pérez-Lemonche, Ángel
Date: 2018-07-00
Type: Reports - Evaluative
Subjects: Data Collection,Knowledge Level,Misconceptions,Pretests Posttests,Science Tests,Mechanics (Physics),Introductory Courses,Accuracy,Scientific Concepts,Test Results,College Students,Correlation,Test Items,Robustness (Statistics),Item Response Theory

We analyze results from paired pre- and post-instruction administration of the Mechanics Baseline Test to 2238 students in introductory mechanics classes. We investigate pairs of specific wrong answers given with unusual frequency by students on the pretest. We also identify transitions between pre- and post-test answers on the same question which elucidate student learning due to instruction. We define criteria for excess transitions above a random response model. Some common transitions are found to be associated specifically with students within a particular range of skills. Further, transitions from pre- to post-test revealed that incorrect pretest answers that were frequently repeated on the posttest often correspond to known misconceptions from physics or math. Thus, our data mining techniques can elucidate common student misunderstandings of mechanics concepts and how instruction affects these misunderstandings. This opens the way for finding improved interventions for specific misunderstandings revealed by analyzing results from pre- and post conceptual tests. [For the full proceedings, see ED593090.]
Predicting Student Enrollment Based on Student and College Characteristics
Author: Slim, Ahmad
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Enrollment Trends,College Students,Student Characteristics,Institutional Characteristics,Enrollment Management,Predictor Variables,Probability,Gender Differences,Ethnicity,Racial Differences,Scores,Grade Point Average,Place of Residence,Student Financial Aid,Federal Aid,Time,First Generation College Students,Family Income,College Entrance Examinations

Colleges are increasingly interested in identifying the factors that maximize their enrollment. These factors allow enrollment management administrators to identify the applicants who have higher tendency to enroll at their institutions and accordingly to better allocate their money rewards (i.e., scholarship and financial aid). In this paper we identify factors that affect the likelihood of enrolling. We use machine learning methods to statistically analyze the enrollment predictability of such factors. In particular, we use logistic regression (LR), support vector machines (SVMs) and semi-supervised probability methods. The LR and the SVMs methods predict the enrollment of applicants at an individual level whereas the semi-supervised probability method does that at a cohort level. We validate our methods using real data for applicants admitted to the university of New Mexico (UNM). The results show that a small set of factors related to student and college characteristics are highly correlated to the applicant decision of enrollment. This outcome is supported by the relatively high prediction accuracy of the proposed methods. [For the full proceedings, see ED593090.]
Predicting Student Performance Based on Online Study Habits: A Study of Blended Courses
Author: Sheshadri, Adithya
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,Large Group Instruction,Educational Technology,Technology Uses in Education,Study Habits,Blended Learning,Computer Science Education,Predictor Variables,Performance,College Students,Conventional Instruction

Online tools provide unique access to research students' study habits and problem-solving behavior. In MOOCs [Massive Open Online Courses], this online data can be used to inform instructors and to provide automatic guidance to students. However, these techniques may not apply in blended courses with face to face and online components. We report on a study of integrated user-system interaction logs from 3 computer science courses using four online systems: LMS [Learning Management System], forum, version control, and homework system. Our results show that students rarely work across platforms in a single session, and that final class performance can be predicted from students' system use. [For the full proceedings, see ED593090.]
Mining MOOC Lecture Transcripts to Construct Concept Dependency Graphs
Author: ALSaad, Fareedah
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Data Collection,Educational Research,Online Courses,Large Group Instruction,Lecture Method,Transcripts (Written Records),Graphs,Alternative Assessment,Concept Mapping,Accuracy,Recall (Psychology)

This paper addresses the question of identifying a concept dependency graph for a MOOC through unsupervised analysis of lecture transcripts. The problem is important: extracting a concept graph is the first step in helping students with varying preparation to understand course material. The problem is challenging: instructors are unaware of the student preparation diversity and may be unable to identify the right resolution of the concepts, necessitating costly updates; inferring concepts from groups suffers from polysemy; the temporal order of concepts depends on the concepts in question. We propose innovative unsupervised methods to discover a directed concept dependency within and between lectures. Our main technical innovation lies in exploiting the temporal ordering amongst concepts to discover the graph. We propose two measures--the Bridge Ensemble Measure and the Global Direction Measure--to infer the existence and the direction of the dependency relations between concepts. The bridge ensemble measure identifies concept overlap between lectures, determines concept co-occurrence within short windows, and the lecture where concepts occur first. The global direction measure incorporates time directly by analyzing the concept time ordering both globally and within lectures. Experiments over real-world MOOC data show that our method outperforms the baseline in both AUC and precision/recall curves. [For the full proceedings, see ED593090.]
ELBA: Exceptional Learning Behavior Analysis
Author: Du, Xin
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Student Behavior,Assignments,Large Group Instruction,Online Courses,Educational Technology,Technology Uses in Education,Study Habits,Data Collection,Dropout Rate,Correlation,Grades (Scholastic),Behavior Patterns

Behavioral records collected through course assessments, peer assignments, and programming assignments in Massive Open Online Courses (MOOCs) provide multiple views about a student's study style. Study behavior is correlated with whether or not the student can get a certificate or drop out from a course. It is of predominant importance to identify the particular behavioral patterns and establish an accurate predictive model for the learning results, so that tutors can give well-focused assistance and guidance on specific students. However, the behavioral records of individuals are usually very sparse; behavioral records between individuals are inconsistent in time and skewed in contents. These remain big challenges for the state-of-the-art methods. In this paper, we engage the concept of subgroup as a trade-off to overcome the sparsity of individual behavioral records and inconsistency between individuals. We employ the framework of Exceptional Model Mining (EMM) to discover exceptional student behavior. Various model classes of EMM are applied on dropout rate analysis, correlation analysis between length of learning behavior sequence and course grades, and passing state prediction analysis. Qualitative and quantitative experimental results on real MOOCs datasets show that our method can discover significantly interesting learning behavioral patterns of students. [For the full proceedings, see ED593090.]
Accurate Measurement of Lexical Sophistication with Reference to ESL Learner Data
Author: Naismith, Ben
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: English (Second Language),Second Language Learning,Computational Linguistics,Native Speakers,Accuracy,Word Frequency,Word Lists,Measurement Techniques,Language Skills,Second Language Instruction,Language Variation,Native Language,Cultural Background,Validity,Language Proficiency,Comparative Analysis

One commonly used measure of lexical sophistication is the Advanced Guiraud (AG; [9]), whose formula requires frequency band counts (e.g., COCA; [13]). However, the accuracy of this measure is affected by the particular 2000-word frequency list selected as the basis for its calculations [27]. For example, possible issues arise when frequency lists that are based solely on native speaker corpora are used as a target for second language (L2) learners (e.g., [8]) because the exposure frequencies for L2 learners may vary from that of native speakers. Such L2 variation from comparable native speakers may be due to first language (L1) culture, home country teaching materials, or the text types which L2 learners commonly encounter. This paper addresses the aforementioned problem through an English as a Second Language (ESL) frequency list validation. Our validation is established on two sources: (1) the New General Service List (NGSL; [4]) which is based on the Cambridge English Corpus (CEC) and (2) written data from the 4.2 million-word Pitt English Language Institute Corpus (PELIC). Using open-source data science tools and natural language processing technologies, the paper demonstrates that more distinct measurable lexical sophistication differences across levels are discernible when learner-oriented frequency lists (as compared to general corpora frequency lists) are used as part of a lexical measure such as AG. The results from this research will be useful in teaching contexts where lexical proficiency is measured or assessed, and for materials and test developers who rely on such lists as being representative of known vocabulary at different levels of proficiency. This research applies data-driven exploration of learner corpora to vocabulary acquisition and pedagogy, thus closing a loop between educational data mining and classroom applications. [For the full proceedings, see ED593090.]
Prediction of Academic Achievement Based on Digital Campus
Author: Wang, Zheng
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Academic Achievement,Prediction,Data Analysis,Student Behavior,Behavior Patterns,Accuracy,Computer Uses in Education,Undergraduate Students

Academic achievement of a student in college always has a far-reaching impact on his further development. With the rise of the ubiquitous sensing technology, students' digital footprints in campus can be collected to gain insights into their daily behaviours and predict their academic achievements. In this paper, we propose a framework named AAPEDM (Academic Achievement Prediction via Educational Data Mining) to predict students' academic achievements based on the influencing factors we have discovered. Multisource heterogeneous data including Wi-Fi detection records, usage of smartcards, usage of campus network, is aggregated firstly. Then, instead of the self-reported features or traditional academic assessments like test scores, we extract features reflecting students' behavioural patterns. Specially, we define DOH (Degree of Hardworking) to improve the performance of the classifier. Finally, we analyze the features extracted and apply supervised learning methods to predict their academic achievements. Experiments are conducted on real-world data from 528 college students in one faculty, and the classification accuracy can be up to 88%. [For the full proceedings, see ED593090.]
Re-Designing the Structure of Online Courses to Empower Educational Data Mining
Author: Chen, Zhongzhou
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,Course Organization,Data Analysis,Instructional Design,Educational Technology,Time on Task,Instructional Materials,Instructional Effectiveness,College Students,Student Behavior,Multivariate Analysis,Outcomes of Education

The amount of information contained in any educational data set is fundamentally constrained by the instructional conditions under which the data are collected. In this study, we show that by redesigning the structure of traditional online courses, we can improve the ability of educational data mining to provide useful information for instructors. This new design, referred to as Online Learning Modules, blends frequent learning assessment as seen in intelligent tutoring systems into the structure of conventional online courses, allowing learning behavior data and learning outcome data to be collected from the same learning module. By applying relatively straightforward clustering analysis to data collected from a sequence of four modules, we are able to gain insight on whether students are spending enough time studying and on the effectiveness of the instructional materials, two questions most instructors ask each day. [For the full proceedings, see ED593090.]
Predicting Individualized Learner Models across Tutor Lessons
Author: Eagle, Michael
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Individualized Instruction,Models,Data Analysis,Knowledge Level,Prediction,Intelligent Tutoring Systems,Individual Differences,Undergraduate Students,Genetics,Biology,Science Instruction,Goodness of Fit,Bayesian Statistics

In this work we use prior to tutor-session data to generate an individualized student knowledge model. Intelligent learning environments use student models to individualize curriculum sequencing and help messages. Researchers decompose the learning tasks into sets of Knowledge Components (KCs) that represent individual units of knowledge; the student model estimates a parameters for each KC, but not for each student. Using existing performance data to adjust parameters for each individual student improves model fit, and leads to different practice recommendations. However, in order to be implemented in a live system we need to have a method to estimate the student parameters using only the student's prior activities. In this work, we use data collected from student reading, prior tutor lessons, to predict individualized difference weights for parameters of a Bayesian Knowledge Tracing (BKT) variant. We find that best-fitting student parameters trained on previous lessons do not directly transfer to new lessons; however, we can effectively predict the student parameters for the new lesson by using features derived from prior lessons, and prior to tutor text-reading transaction data. [For the full proceedings, see ED593090.]
A Hybrid Multi-Criteria Approach Using a Genetic Algorithm for Recommending Courses to University Students
Author: Esteban, Aurora
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: College Students,Course Selection (Students),Computer Uses in Education,Elective Courses,Mathematics,Criteria,Foreign Countries,Data Analysis

This paper describes a multiple criteria approach based on a hybrid method of Collaborative Filtering (CF) and ContentBased Filtering (CBF) for discovering the most relevant criteria which could affect the elective course recommendation for university students. In order to determine which factors are the most important, it is proposed a genetic algorithm which automatically discovers the importance of the different criteria assigning weights to each one of them. We have carried out an in-depth study using a real data set with more than 1700 ratings of Computer Science graduates at University of Cordoba. We have used different proposals and different weights for each criterion in order to discover what is the combination of multiple criteria which provides better results. [For the full proceedings, see ED593090.]
Analyzing the Relative Learning Benefits of Completing Required Activities and Optional Readings in Online Courses
Author: Carvalho, Paulo F.
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,Learning Activities,Reading Materials,Drills (Practice),Student Behavior,Learner Engagement,Active Learning,Undergraduate Students,Outcomes of Education

Students who actively engage with learning materials, for example by completing more practice activities, show better learning outcomes. A straightforward step to stimulate this desirable behavior is to require students to complete activities and downplay the role of reading materials. However, this approach might have undesirable consequences, such as inflating the number of activities completed in a short period of time until maximum performance is achieved (&quot;gaming the system&quot;). In this paper, we analyze the relative benefits of completing activities vs. readings for learning outcomes in an online course that required students to perform practice activities. The results show that students who read more pages have better learning outcomes than students who completed more activities. This pattern of results holds even when considering different measures of active engagement but is reversed when considering only activities classified as effective active engagement by a &quot;gaming behavior&quot; classifier. Overall, these results suggest that, when completing activities is required, students benefit from complementing the activities with optional readings. One possibility is that completing optional readings can be an active learning activity in itself, driven by students who are going beyond the minimum requirement, and actively seeking further information and robust feedback that complements the activities. [For the full proceedings, see ED593090.]
Predicting Learning by Analyzing Eye-Gaze Data of Reading Behavior
Author: Rajendran, Ramkumar
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Eye Movements,Reading Processes,Reading Strategies,Middle School Students,Educational Technology,Technology Uses in Education,Artificial Intelligence,Science Instruction,Teaching Methods,Hypermedia,Classification,Causal Models,Maps,Predictor Variables

Researchers have highlighted how tracking learners' eye-gaze can reveal their reading behaviors and strategies, and this provides a framework for developing personalized feedback to improve learning and problem solving skills. In this paper, we describe analyses of eye-gaze data collected from 16 middle school students who worked with Betty's Brain, an open-ended learning environment, where students learn science by building causal models to teach a virtual agent. Our goal was to test whether newly available consumer-level eye trackers could provide the data that would allow us to probe further into the relations between students' reading of hypertext resources and building of graphical causal maps. We collected substantial amounts of gaze data and then constructed classifier models to predict whether students would be successful in constructing correct causal links. These models predicted correct map-building actions with an accuracy of 80% (F1 = 0.82; Cohen's kappa K = 0.62). The proportions of correct link additions are in turn directly related to learners' performance in Betty's Brain. Therefore, students' gaze patterns when reading the resources may be good indicators of their overall performance. These findings can be used to support the development of a real-time eye gaze analysis system, which can detect students reading patterns, and when necessary provide support to help them become better readers. [For the full proceedings, see ED593090.]