ethanporcaro/eric-fulltext-meta huggingface dataset:
Predicting Student Performance in a Master of Data Science Program Using Admissions Data
Author: Zhao, Yijun
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Grade Prediction,Data Analysis,Masters Programs,Admission Criteria,College Admission,Data Use,Graduate Study,College Entrance Examinations,Language Tests,English (Second Language),Second Language Learning

Predicting student success in a data science degree program is a challenging task due to the interdisciplinary nature of the field, the diverse backgrounds of the students, and an incomplete understanding of the precise skills that are most critical to success. In this study, the applicant's future academic performance in a Master of Data Science program is assessed using information from the admission application, such as standardized test scores, undergraduate grade point average, declared major, and school ranking. Simple data analysis methods and visualization techniques are used to gain a better understanding of how these variables impact student performance, and several classification algorithms are used to induce models to distinguish between students that will perform very well and those that will perform very poorly. Historical admissions and grading data are used to perform these analyses and build the classification models. The analyses and predictive models that are generated provide insight into the factors that identify good and poor candidates, and can aid in future admissions decisions. [For the full proceedings, see ED607784.]
Incorporating Task-Specific Features into Deep Models to Classify Argument Components
Author: Xue, Linting
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Persuasive Discourse,Classification,Artificial Intelligence,Statistical Analysis,Models,Writing Evaluation

In order to effectively grade persuasive writing we must be able to reliably identify and extract extract argument structures. In order to do this we must classify arguments by their structural roles (e.g., major claim, claim, and premise). Current approaches to classification typically rely on statistical models with heavy feature-engineering or on deep neural-networks that do not consider prior knowledge or other secondary features. Little research has been carried out to investigate if we can incorporate features into deep models to address AM tasks. In this work, we propose to incorporate lightweight features into deep models to classify argument components. We experimented with two state of the art (SOTA) approaches: 1) linear-Long-Short-Term Memory (LSTM) models with concatenated feature vectors; or 2) Directed Acyclic Graph (DAG) structured LSTMs. In our models we incorporated the features of argument position (e.g., if the argument is in the first paragraph) and prior knowledge of discourse indicators (e.g., in conclusion, for example). We use two baselines in our work: 1) prior work using SVM models with heavy feature engineering; 2) traditional linear-Bi-LSTMs with no task-specific features. Our results show that with a comparatively small number of lightweight features, both linear-Bi-LSTMs and DAG-Bi-LSTMs outperform SVM models that depend on more heavy feature engineering, and outperform linear-Bi-LSTMs with only general word embedding features. These results suggest that incorporating task-specific elements into deep models may potentially benefit argument mining tasks. [For the full proceedings, see ED607784.]
Pick the Moment: Identifying Critical Pedagogical Decisions Using Long-Short Term Rewards
Author: Ju, Song
Date: 2020-07-00
Type: Reports - Research
Subjects: Decision Making,Reinforcement,Artificial Intelligence,Man Machine Systems,Intelligent Tutoring Systems,Computer Simulation,Computer Games,Educational Technology

Identifying critical decisions is one of the most challenging decision-making problems in real-world applications. In this work, we propose a novel Reinforcement Learning (RL) based Long-Short Term Rewards (LSTR) framework for critical decisions identification. RL is a machine learning area concerning with inducing effective decision-making policies, following which result in the maximum cumulative &quot;reward.&quot; Many RL algorithms find the optimal policy via estimating the optimal Q-values, which specify the maximum cumulative reward the agent can receive. In our LSTR framework, the &quot;long term&quot; rewards are defined as &quot;Q-values&quot; and the &quot;short term&quot; rewards are determined by the &quot;reward function.&quot; Experiments on a synthetic GridWorld game and real-world Intelligent Tutoring System datasets show that the proposed LSTR framework indeed identifies the critical decisions in the sequences. Furthermore, our results show that carrying out the critical decisions alone is as effective as a fully-executed policy. [For the full proceedings, see ED607784.]
PIPE: Predicting Logical Programming Errors in Programming Exercises
Author: Miao, Dezhuang
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Programming,Prediction,Error Patterns,Models,Educational Technology,Technology Uses in Education,Feedback (Response),Undergraduate Students

In colleges, programming is increasingly becoming a general education course of almost all STEM majors as well as some art majors, resulting in an emerging demand for scalable programming education. To support scalable education, teaching activities such as grading and feedback have to be automated. Recently, online judge systems have been extensively used for programming training, because they are able to automatically evaluate the correctness of programs in real time and thereby make grading work scalable. However, existing online judge systems lack of the ability to give effective feedback on logical programming errors. As such, instructors and teaching assistants are still overwhelmed by the work of helping students fix programs, especially for those novice students. To tackle the challenge, we develop &quot;PIPE,&quot; a deep learning model that is able to &quot;P&quot;redict log&quot;I&quot;cal &quot;P&quot;rogramming &quot;E&quot;rrors in student programs. The model seamlessly integrates a representation learning model for obtaining the latent feature of a program and a multi-label classification model for predicting the error types in the program, thereby allowing end-to-end learning and prediction. We use the C programs submitted in our online judge system to train PIPE, and demonstrate its superior performance over the baseline models. We use PIPE to implement the error-feedback feature in our online judge system and enable automated feedback on logical programming errors to the students. [For the full proceedings, see ED607784.]
Predicting Engagement in Video Lectures
Author: Bulathwela, Sahan
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Prediction,Open Educational Resources,Learner Engagement,Video Technology,Teaching Methods,Evaluation Methods,Data Analysis,Models,Quality Assurance,Distance Education,Comparative Analysis,Intellectual Disciplines,Educational Quality

The explosion of Open Educational Resources (OERs) in the recent years creates the demand for scalable, automatic approaches to process and evaluate OERs, with the end goal of identifying and recommending the most suitable educational materials for learners. We focus on building models to find the characteristics and features involved in contextagnostic engagement (i.e. population-based), a seldom researched topic compared to other contextualised and personalised approaches that focus more on individual learner engagement. Learner engagement, is arguably a more reliable measure than popularity/number of views, is more abundant than user ratings and has also been shown to be a crucial component in achieving learning outcomes. In this work, we explore the idea of building a predictive model for population-based engagement in education. We introduce a novel, large dataset of video lectures for predicting context-agnostic engagement and propose both cross-modal and modality specific feature sets to achieve this task. We further test different strategies for quantifying learner engagement signals. We demonstrate the use of our approach in the case of data scarcity. Additionally, we perform a sensitivity analysis of the best performing model, which shows promising performance and can be easily integrated into an educational recommender system for OERs. [For the full proceedings, see ED607784.]
Increasing Enrollment by Optimizing Scholarship Allocations Using Machine Learning and Genetic Algorithms
Author: Aulck, Lovenoor
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Resource Allocation,Scholarships,Artificial Intelligence,Data Analysis,Enrollment Management,Public Colleges,College Freshmen,Classification,Prediction,Data Use,Computer Uses in Education

Effectively estimating student enrollment and recruiting students is critical to the success of any university. However, despite having an abundance of data and researchers at the forefront of data science, traditional universities are not fully leveraging machine learning and data mining approaches to improve their enrollment management strategies. In this project, we use data at a large, public university to increase their student enrollment. We do this by first predicting the enrollment of admitted first-year, first-time students using a suite of machine learning classifiers (AUROC = 0.85). We then use the results from these machine learning experiments in conjunction with genetic algorithms to optimize scholarship disbursement. We show the effectiveness of this approach using real-world enrollment metrics. Our optimized model was expected to increase enrollment yield by 15.8% over previous disbursement strategies. After deploying the model and confirming student enrollment decisions, the university actually saw a 23.3% increase in enrollment yield. This resulted in millions of dollars in additional annual tuition revenue and a commitment by the university to employ the method in subsequent enrollment cycles. We see this as a successful case study of how educational institutions can more effectively leverage their data. [For the full proceedings, see ED607784.]
Measuring Ability-to-Learn Using Parametric Learning-Gain Functions
Author: Piech, Chris
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Academic Ability,Measurement,Middle School Students,Achievement Gains,Statistical Analysis,Bayesian Statistics,Undergraduate Students,Foreign Countries

One crucial function of a classroom, and a school more generally, is to prepare students for future learning. Students should have the capacity to learn new information and to acquire new skills. This ability to &quot;learn&quot; is a core competency in our rapidly changing world. But how do we measure ability to learn? And how can we measure how well a school has prepared their students to learn? In this paper we formally pose the problem, and introduce a grounded theory of how to measure ability to learn. Using simulations of students learning we provide initial evidence that this theory provides an elegant solution to this problem. We further validate our ideas using real world data from 70k middle-school students and show that our theory is more accurate and interpretable than current state-of-the-art models of learning gains. We consider our results a modest yet interesting first step for a novel type of test. [For the full proceedings, see ED607784.]
Washington State&apos;s Integrated Basic Education and Skills Training (I-BEST) Program in Three Colleges: Implementation and Early Impact Report. Pathways for Advancing Careers and Education. OPRE Report No. 2018-87
Author: Martinson, Karin
Date: 2018-09-00
Type: Reports - Research
Subjects: Adult Basic Education,Community Colleges,Vocational Education,Team Teaching,Basic Skills,English (Second Language),Student Financial Aid,Academic Advising,Program Effectiveness,Access to Education,College Credits,Educational Attainment,Program Implementation,Outcomes of Education,Two Year College Students,Adult Students

This report describes the implementation and early impacts of the Washington State Integrated Basic Education and Skills Training (I-BEST) program at three colleges: Bellingham Technical College, Everett Community College, and Whatcom Community College. I-BEST is a nationally known program that aims to increase access to and completion of college-level occupational training in a variety of in-demand occupational areas. Its signature feature is team teaching by a basic skills instructor and an occupational instructor during at least 50 percent of occupational training class time. Colleges operated I-BEST programs in one or more occupational areas including automotive, electrical, office skills, nursing, precision machining, and welding. I-BEST is one of nine career pathways programs being evaluated under the Pathways for Advancing Careers and Education (PACE) study sponsored by the Administration for Children and Families. The I-BEST program was launched in Washington in the 2006-07 academic year by the Washington State Board for Community and Technical Colleges. I-BEST aims to help students in basic skills programs (e.g., Adult Basic Education, English as a Second Language), who otherwise might have spent time in remediation, to enroll and succeed in college-level occupational training courses. Each I-BEST program is a course of study within a structured career pathway, and it offers students the opportunity to obtain credentials and college credits in in-demand occupations. Besides the team teaching, the I-BEST program evaluated in PACE also included two enhancements: financial support for tuition and associated materials; and additional advising services focused on supporting students' academic needs, navigating college procedures, and career planning. Using a rigorous research design, the study found that the I-BEST programs at the three colleges increased participation in college level courses, number of credits earned and credential attainment. Future reports will examine whether the I-BEST program resulted in gains in employment and earnings.
Changing Digital Learning from the Inside Out: System-Level Considerations for Shaping Online Experiences That Engage Learners and Teachers
Author: Rouleau, Kristin
Date: 2020-09-00
Type: Reports - Descriptive
Subjects: Online Courses,Distance Education,Educational Technology,Technology Uses in Education,Teaching Methods,School Districts,Educational Planning,COVID-19,Pandemics,School Closing,Educational Change,Electronic Learning,Values,Inquiry,Learner Engagement,Leadership Responsibility,Cooperation,Goal Orientation

Online teaching and learning can be more engaging and effective for students and teachers alike if districts take into account several key considerations when formatting their digital plans and processes. Combining and building on ideas from change management, improvement science, and inside-out systems development, Dr. Kris Rouleau from McREL's Learning Services team offers seven guiding principles for district leaders who are looking to refine their systemwide approach to digitally mediated learning.
Online Academic Course Performance Prediction Using Relational Graph Convolutional Neural Network
Author: Karimi, Hamid
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,Academic Achievement,Prediction,Teaching Methods,Achievement Gains,Outcomes of Education,Models,Networks,Data Analysis,Evaluation Methods,Student Behavior,Computer Simulation,Computer Mediated Communication,Classification,Online Systems,Open Universities,College Students,Graphs,Visualization

Online learning has attracted a large number of participants and is increasingly becoming very popular. However, the completion rates for online learning are notoriously low. Further, unlike traditional education systems, teachers, if any, are unable to comprehensively evaluate the learning gain of each student through the online learning platform. Hence, we need to have an effective framework for evaluating students' performance in online education systems and to predict their expected outcomes and associated early failures. To this end, we introduce Deep Online Performance Evaluation (DOPE), which first models the student course relations in an online system as a knowledge graph, then utilizes an advanced graph neural network to extract course and student embeddings, harnesses a recurrent neural network to encode the system's temporal student behavioral data, and ultimately predicts a student's performance in a given course. Comprehensive experiments on six online courses verify the effectiveness of DOPE across multiple settings against representative baseline methods. Furthermore, we perform ablation feature analysis on the student behavioral features to better understand the inner workings of DOPE. The code and data are available from https://github.com/hamidkarimi/dope. [For the full proceedings, see ED607784.]
Literature in Language Learning: New Approaches
Author: Almeida, Ana Bela, Ed.
Date: 2020-09-14
Type: Books
Subjects: Modern Languages,Second Language Learning,Second Language Instruction,English (Second Language),Teaching Methods,Literature,Singing,Poetry,Creative Writing,Writing Workshops,Best Practices,Language Teachers,Researchers

Which are the new directions in learning and teaching Modern Languages and English through literature? How can we use songs to talk about poetry in the language classroom, and how can creative writing workshops help with language teaching beyond the classroom? These are just a few questions addressed in this volume. Researchers and practitioners in Modern Languages and English as a Foreign Language share theory and their best practice on this pedagogical approach. [This content is provided in the format of an e-book. Individual papers are indexed in ERIC.]
Moving beyond Test Scores: Analyzing the Effectiveness of a Digital Learning Game through Learning Analytics
Author: Nguyen, Huy Anh
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Computer Games,Educational Games,Game Based Learning,Instructional Effectiveness,Learning Analytics,Mastery Learning,Difficulty Level,Arithmetic,Number Concepts,Middle School Students,Emotional Response,Student Attitudes,Gender Differences,Grade 5,Grade 6

A challenge in digital learning games is assessing students' learning behaviors, which are often intertwined with game behaviors. How do we know whether students have learned enough or needed more practice at the end of their game play? To answer this question, we performed post hoc analyses on a prior study of the game &quot;Decimal Point,&quot; which teaches decimal numbers and decimal operations to middle school students. Using Bayesian Knowledge Tracing, we found that students had the most difficulty with mastering the number line and sorting skills, but also tended to over-practice the skills they had previously mastered. In addition, using students' survey responses and in-game measurements, we identified the best feature sets to predict test scores and self-reported enjoyment. Analyzing these features and their connections with learning outcomes and enjoyment yielded useful insights into areas of improvement for the game. We conclude by highlighting the need for combining traditional test measures with rigorous learning analytics to critically evaluate the effectiveness of learning games. [For the full proceedings, see ED607784.]
More Data and Better Keywords Imply Better Educational Transcript Classification?
Author: Danciulescu, Theodora Ioana
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Data Analysis,Classification,Information Retrieval,Video Technology,Teaching Methods,Web Sites,Collaborative Writing,Accuracy,Comparative Analysis,Authors,Models,Foreign Countries,Universities,Information Systems

Building and especially improving a classification kernel represents a challenging task. The works presented in this paper continue an already developed semi-supervised classification approach that aimed at labelling transcripts from educational videos. We questioned whether the size of the ground-truth data-set (Wikipedia articles) or the quality of the keywords used in the semi-supervised labelling have a significant impact on the accuracy metrics of the final obtained data model. Experimental results took into consideration three Wikipedia data-sets of &quot;Small,&quot; &quot;Medium&quot; and &quot;Large&quot; sizes. For each data-set there were used three sets of keywords: offered by video authors, determined by &quot;rake-nltk&quot; on available transcripts and determined by &quot;rake-nltk&quot; on Wikipedia articles that serve as training and testing data for the LDA [latent Dirichlet allocation] model that determine keywords on the transcripts. Experiments show that the size of the data-set has little importance, while the quality of the keywords has a more significant impact. Therefore, an improved version of the previously developed classifier has been obtained by improving the quality of the keywords involved in semi-supervised training. This result paves the way towards further improvements that may finally be deployed as within a recommender system of educational videos at the Universitat Politècnica de València. [For the full proceedings, see ED607784.]
Physics Doctorates: Skills Used and Satisfaction with Employment. Data from the Degree Recipient Follow-Up Survey for the Classes of 2015 and 2016. Focus On
Author: Mulvey, Patrick
Date: 2020-08-00
Type: Reports - Research
Subjects: Graduate Students,Physics,College Graduates,Outcomes of Education,Employment Patterns,Job Skills,Teamwork,Technical Writing,Public Speaking,Personnel Management,Teaching Skills,Money Management,Problem Solving,Programming,Job Satisfaction,Career Planning,Educational Attitudes

By the time physics students receive their PhDs, they have a broad understanding of many areas of physics and in-depth knowledge in a specific subfield. They have developed a comprehensive knowledge of mathematics, modeling, and programming. Graduate students in physics also develop skills in areas not specifically related to physics and not explicitly associated with research. These include how to work on a team, manage projects, and technical writing. This Focus On looks at the knowledge and skills used by new physics PhDs in the first positions they held after receiving their degrees. It also looks at how new PhDs perceive the subjective aspects of their first post-degree position.
Methodology of Measure of Similarity in Student Video Sequence of Interactions
Author: Mbouzao, Boniface
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Comparative Analysis,Video Technology,Interaction,Measurement Techniques,Markov Processes,Task Analysis,Online Courses,Mass Instruction,Learner Engagement,Accuracy,Prediction,Data Analysis

Massive online Open Courses (MOOCs) make extensive use of videos. Students interact with them by pausing, seeking forward or backward, replaying segments, etc. We can reasonably assume that students have different patterns of video interactions, but it remains hard to compare student video interactions. Some methods were developed, such as Markov Chain and Edit Distance. However, these methods have caveats as we show with prototypical examples. This paper proposes a new methodology of comparing video sequences of interaction based both on time spent in each state and the succession of states by computing the distance between the transition matrices of the video interaction sequences. Results show the proposed methodology can better characterize video interaction in a task to discriminate which student is interacting with a video, or which video a student is interacting with. [For the full proceedings, see ED607784.]