ethanporcaro/eric-fulltext-meta huggingface dataset:
Proceedings of the International Conference on Educational Data Mining (EDM) (13th, Online, July 10-13, 2020)
Author: Rafferty, Anna N., Ed.
Date: 2020-07-00
Type: Collected Works - Proceedings
Subjects: Educational Improvement,Teaching Methods,Information Retrieval,Data Processing,Data Analysis,Equal Education,Student Diversity,Cooperative Learning,Online Courses,Demography,Transformative Learning,Blended Learning,Learning Strategies,Large Group Instruction,Artificial Intelligence,Educational Technology,Privacy,Learner Engagement,Student Motivation,Programming,Models,Game Based Learning,Competence,Problem Solving,Information Sources,Critical Theory,At Risk Students,Elementary Secondary Education,Spatial Ability,Visualization,Intervention,Student Characteristics,Automation,Remedial Reading,Handheld Devices,Video Technology,Item Response Theory,Reinforcement,Student Behavior,Prediction,Success,College Students,Data Collection,Masters Programs,Reaction Time,Reading Comprehension,College Admission,Essays,Peer Teaching,Performance,Teamwork,Student Projects,Inquiry,Electronic Publishing,Textbooks,Tests,Ethics,Legal Responsibility,Higher Education,Measurement Techniques,Reading Achievement,Grade 9,Video Games,Peer Evaluation,Secondary Schools,Computer Science Education,Physics,Feedback (Response),Semitic Languages,Technical Writing,Telecommunications,Personal Autonomy,Computer Software,Plagiarism,Curriculum,Collaborative Writing,Dropouts,Educational Assessment,Gender Differences,Difficulty Level,Social Media,Mental Health,Innovation,Student Role,Eye Movements,Foreign Countries,Bayesian Statistics,Scores,Distance Education,Student Attitudes,Scoring,Natural Language Processing,Middle School Students,Social Networks,Employment Qualifications,Intelligent Tutoring Systems,Anxiety,Second Languages,Cues,Visual Aids

The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of presentations all paper presenters pre-recorded their presentation as a video and then hosted it on YouTube with closed-captioning (CC). The official theme of this year's conference is Improving Learning Outcomes for All Learners. The theme comprises two parts: (1) Identifying actionable learning or teaching strategies that can be used to &quot;improve&quot; learning outcomes, not just predict them; (2) Using EDM to promote more &quot;equitable&quot; learning across diverse groups of learners, and to benefit underserved communities in particular. This year's conference features three invited talks: Alina von Davier, Chief Officer at ACTNext; Abelardo Pardo, Professor and Dean of Programs (Engineering), at UniSA STEM, University of South Australia; and Kobi Gal, Associate Professor at the Department of Software and Information Systems Engineering at Ben-Gurion University of the Negev, and Reader at the School of Informatics at the University of Edinburgh.
Analyzing Student Strategies in Blended Courses Using Clickstream Data
Author: Akpinar, Nil-Jana
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Learning Strategies,Blended Learning,Learning Analytics,Student Behavior,Behavior Patterns,Undergraduate Students,Computer Science Education,Natural Language Processing,Interaction,Study Habits,Sequential Approach,Predictor Variables,Attendance,Homework,Tests

Educational software data promises unique insights into students' study behaviors and drivers of success. While much work has been dedicated to performance prediction in massive open online courses, it is unclear if the same methods can be applied to blended courses and a deeper understanding of student strategies is often missing. We use pattern mining and models borrowed from Natural Language Processing (NLP) to understand student interactions and extract frequent strategies from a blended college course. Finegrained clickstream data is collected through Diderot, a noncommercial educational support system that spans a wide range of functionalities. We find that interaction patterns differ considerably based on the assessment type students are preparing for, and many of the extracted features can be used for reliable performance prediction. Our results suggest that the proposed hybrid NLP methods can provide valuable insights even in the low-data setting of blended courses given enough data granularity. [For the full proceedings, see ED607784.]
Measuring Student Success Skills: A Review of the Literature on Complex Communication. 21st Century Success Skills
Author: Thompson, Jeri
Date: 2020-03-31
Type: Information Analyses
Subjects: Academic Achievement,Communication Skills,21st Century Skills,Success,Definitions,Skill Development,Language Proficiency,Teaching Methods,Scoring Rubrics

There is a growing interest in conceptualizing, defining, and assessing what are often called 21st century skills or deeper learning competencies. The purpose of this literature review is to explore the conceptualizations, definitions, and understandings in the research literature related to complex communication. Key initial questions include: What is complex communication? How is complex communication related to other success skill concepts? And to what extent does complex communication develop over time? This foundational information will then be used to examine: (1) instructional approaches to promote complex communication; (2) benefits of complex communication on valued student outcomes such as student learning; and (3) ways teachers can collect evidence that reveals the benefits of complex communication using student artifacts and other appropriate measures.
Automatic Subject-Based Contextualisation of Programming Assignment Lists
Author: Fonseca, Samuel C.
Date: 2020-00-00
Type: Speeches/Meeting Papers
Subjects: Programming,Introductory Courses,Computer Science Education,Automation,Assignments,Undergraduate Students,Nonmajors,Relevance (Education),Individualized Instruction,Taxonomy,Classification,Natural Language Processing,Foreign Countries

As programming must be learned by doing, introductory programming course learners need to solve many problems, e.g., on systems such as 'Online Judges'. However, as such courses are often compulsory for non-Computer Science (nonCS) undergraduates, this may cause difficulties to learners that do not have the typical intrinsic motivation for programming as CS students do. In this sense, contextualised assignment lists, with programming problems related to the students' major, could enhance engagement in the learning process. Thus, students would solve programming problems related to their academic context, improving their comprehension of the applicability and importance of programming. Nonetheless, preparing these contextually personalised programming assignments for classes for different courses is really laborious and would increase considerably the instructors'/monitors' workload. Thus, this work aims, for the first time, to the best of our knowledge, to automatically classify the programming assignments in Online Judges based on students' academic contexts by proposing a new context taxonomy, as well as a comprehensive pipeline evaluation methodology of cutting edge competitive Natural Language Processing (NLP). Our comprehensive methodology pipeline allows for comparing state of the art data augmentation, classifiers, beside NLP approaches. The context taxonomy created contains 23 subject matters related to the non-CS majors, representing thus a challenging multi-classification problem. We show how even on this problem, our comprehensive pipeline evaluation methodology allows us to achieve an accuracy of 95.2%, which makes it possible to automatically create contextually personalised program assignments for non-CS with a minimal error rate (4.8%). [For the full proceedings, see ED607784.]
Confident Learning Curves in Additive Factors Modeling
Author: Goutte, Cyril
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Learning,Models,Computation,Statistical Analysis,Geometry,Mathematics Instruction,Graphs

Learning curves are an important tool in cognitive diagnostics modeling to help assess how well students acquire new skills, and to refine and improve knowledge component models. Learning curves are typically obtained from a model estimated on real data obtained from a finite, and usually limited, sample of students. As a consequence, there is some uncertainty associated with estimating the model from that sample, and a risk that the inferences made using learning curves derived from the estimated model are over-confident one way or another. Based on previous work modeling the uncertainty on Additive Factors Model parameters, we derive a principled way to quantify the confidence in learning curves associated with each knowledge component. We show that our approach leads to relatively tight bounds on the learning curves, much tighter than a naive approach relying only on parameter uncertainty. This also reveals a disparity across knowledge components regarding how confident one can be in how well these skills are mastered. [For the full proceedings, see ED607784.]
Can We Take Advantage of Time-Interval Pattern Mining to Model Students Activity?
Author: Dermy, Oriane
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Learning Analytics,Time,College Freshmen,Intervals,Student Behavior,Behavior Patterns,Integrated Learning Systems

Analyzing students' activities in their learning process is an issue that has received significant attention in the educational data mining research field. Many approaches have been proposed, including the popular sequential pattern mining. However, the vast majority of the works do not focus on the time of occurrence of the events within the activities. This paper relies on the hypothesis that we can get a better understanding of students' activities, as well as design more accurate models, if time is considered. With this in mind, we propose to study time-interval patterns. To highlight the benefits of managing time, we analyze the data collected about 113 first-year university students interacting with their LMS. Experiments reveal that frequent time-interval patterns are actually identified, which means that some students' activities are regulated not only by the order of learning resources but also by time. In addition, the experiments emphasize that the sets of intervals highly influence the patterns mined and that the set of intervals that represents the human natural time (minute, hour, day, etc.) seems to be the most appropriate one to represent time gap between resources. Finally, we show that time-interval pattern mining brings additional information compared to sequential pattern mining. Indeed, not only the view of students' possible future activities is less uncertain (in terms of learning resources and their temporal gap) but also, as soon as two students differ in their time-intervals, this difference indicates that their following activities are likely to diverge. [For the full proceedings, see ED607784.]
Higher Education in Focus 2019: Illinois. Essential Performance Indicators for Midwestern States
Author: Midwestern Higher Education Compact
Date: 2020-00-00
Type: Reports - Evaluative
Subjects: Student Evaluation,Higher Education,Educational Attainment,Educational Indicators,Postsecondary Education,Employment Qualifications,Labor Market,Occupations,Racial Differences,Ethnicity,Academic Degrees,High School Graduates,High School Students,Income,Preschool Education,Enrollment,Grade 8,Mathematics Achievement,Reading Achievement,Science Achievement,Graduation Rate,College Entrance Examinations,Core Curriculum,College Readiness,College Attendance,College Credits,Young Adults,Adults,College Transfer Students,Public Colleges,College Graduates,Time to Degree,Student Financial Aid,Grants,Family Income,Paying for College,Two Year Colleges,Colleges,Student Loan Programs,Debt (Financial),State Aid,Tuition,Educational Finance,Expenditure per Student,Institutional Characteristics,Low Income Students,Federal Aid,Elementary Secondary Education

In the United States, approximately 65 percent of all jobs in 2020 will require some level of postsecondary education, and the demand will reach 70 percent in Illinois. This report seeks to inform public discourse on higher education by providing key performance indicators relevant to the goal of improving educational attainment for a healthy economy and society. Performance indicators are categorized within five areas: (1) Preparation; (2) Participation; (3) Completion; (4) Affordability; and (5) Finance. Several indicators provide the national and Midwestern Higher Education Compact (MHEC) regional values as well as the median of the top five states in the nation as possible benchmarks. Performance indicators are disaggregated by race/ethnicity and family income when data are available.
Higher Education in Focus 2019: Michigan. Essential Performance Indicators for Midwestern States
Author: Midwestern Higher Education Compact
Date: 2020-00-00
Type: Reports - Evaluative
Subjects: Student Evaluation,Higher Education,Educational Attainment,Educational Indicators,Postsecondary Education,Employment Qualifications,Labor Market,Occupations,Racial Differences,Ethnicity,Academic Degrees,High School Graduates,High School Students,Income,Preschool Education,Enrollment,Grade 8,Mathematics Achievement,Reading Achievement,Science Achievement,Graduation Rate,College Entrance Examinations,Core Curriculum,College Readiness,College Attendance,College Credits,Young Adults,Adults,College Transfer Students,Public Colleges,College Graduates,Time to Degree,Student Financial Aid,Grants,Family Income,Paying for College,Two Year Colleges,Colleges,Student Loan Programs,Debt (Financial),State Aid,Tuition,Educational Finance,Expenditure per Student,Institutional Characteristics,Low Income Students,Federal Aid,Elementary Secondary Education

In the United States, approximately 65 percent of all jobs in 2020 will require some level of postsecondary education, and the demand will reach 70 percent in Michigan. This report seeks to inform public discourse on higher education by providing key performance indicators relevant to the goal of improving educational attainment for a healthy economy and society. Performance indicators are categorized within five areas: (1) Preparation; (2) Participation; (3) Completion; (4) Affordability; and (5) Finance. Several indicators provide the national and MHEC regional values as well as the median of the top five states in the nation as possible benchmarks. Performance indicators are disaggregated by race/ethnicity and family income when data are available.
Higher Education in Focus 2019: Iowa. Essential Performance Indicators for Midwestern States
Author: Midwestern Higher Education Compact
Date: 2020-00-00
Type: Reports - Evaluative
Subjects: Student Evaluation,Higher Education,Educational Attainment,Educational Indicators,Postsecondary Education,Employment Qualifications,Labor Market,Occupations,Racial Differences,Ethnicity,Academic Degrees,High School Graduates,High School Students,Income,Preschool Education,Enrollment,Grade 8,Mathematics Achievement,Reading Achievement,Science Achievement,Graduation Rate,College Entrance Examinations,Core Curriculum,College Readiness,College Attendance,College Credits,Young Adults,Adults,College Transfer Students,Public Colleges,College Graduates,Time to Degree,Student Financial Aid,Grants,Family Income,Paying for College,Two Year Colleges,Colleges,Student Loan Programs,Debt (Financial),State Aid,Tuition,Educational Finance,Expenditure per Student,Institutional Characteristics,Low Income Students,Federal Aid,Elementary Secondary Education

In the United States, approximately 65 percent of all jobs in 2020 will require some level of postsecondary education, and the demand will reach 68 percent in Iowa. This report seeks to inform public discourse on higher education by providing key performance indicators relevant to the goal of improving educational attainment for a healthy economy and society. Performance indicators are categorized within five areas: (1) Preparation; (2) Participation; (3) Completion; (4) Affordability; and (5) Finance. Several indicators provide the national and Midwestern Higher Education Compact (MHEC) regional values as well as the median of the top five states in the nation as possible benchmarks. Performance indicators are disaggregated by race/ethnicity and family income when data are available.
Higher Education in Focus 2019: Ohio. Essential Performance Indicators for Midwestern States
Author: Midwestern Higher Education Compact
Date: 2020-00-00
Type: Reports - Evaluative
Subjects: Student Evaluation,Higher Education,Educational Attainment,Educational Indicators,Postsecondary Education,Employment Qualifications,Labor Market,Occupations,Racial Differences,Ethnicity,Academic Degrees,High School Graduates,High School Students,Income,Preschool Education,Enrollment,Grade 8,Mathematics Achievement,Reading Achievement,Science Achievement,Graduation Rate,College Entrance Examinations,Core Curriculum,College Readiness,College Attendance,College Credits,Young Adults,Adults,College Transfer Students,Public Colleges,College Graduates,Time to Degree,Student Financial Aid,Grants,Family Income,Paying for College,Two Year Colleges,Colleges,Student Loan Programs,Debt (Financial),State Aid,Tuition,Educational Finance,Expenditure per Student,Institutional Characteristics,Low Income Students,Federal Aid,Elementary Secondary Education

In the United States, approximately 65 percent of all jobs in 2020 will require some level of postsecondary education, and the demand will reach 64 percent in Ohio. This report seeks to inform public discourse on higher education by providing key performance indicators relevant to the goal of improving educational attainment for a healthy economy and society. Performance indicators are categorized within five areas: (1) Preparation; (2) Participation; (3) Completion; (4) Affordability; and (5) Finance. Several indicators provide the national and MHEC regional values as well as the median of the top five states in the nation as possible benchmarks. Performance indicators are disaggregated by race/ethnicity and family income when data are available.
Higher Education in Focus 2019: South Dakota. Essential Performance Indicators for Midwestern States
Author: Midwestern Higher Education Compact
Date: 2020-00-00
Type: Reports - Evaluative
Subjects: Student Evaluation,Higher Education,Educational Attainment,Educational Indicators,Postsecondary Education,Employment Qualifications,Labor Market,Occupations,Racial Differences,Ethnicity,Academic Degrees,High School Graduates,High School Students,Income,Preschool Education,Enrollment,Grade 8,Mathematics Achievement,Reading Achievement,Science Achievement,Graduation Rate,College Entrance Examinations,Core Curriculum,College Readiness,College Attendance,College Credits,Young Adults,Adults,College Transfer Students,Public Colleges,College Graduates,Time to Degree,Student Financial Aid,Grants,Family Income,Paying for College,Two Year Colleges,Colleges,Student Loan Programs,Debt (Financial),State Aid,Tuition,Educational Finance,Expenditure per Student,Institutional Characteristics,Low Income Students,Federal Aid,Elementary Secondary Education

In the United States, approximately 65 percent of all jobs in 2020 will require some level of postsecondary education, and the demand will reach 65 percent in South Dakota. This report seeks to inform public discourse on higher education by providing key performance indicators relevant to the goal of improving educational attainment for a healthy economy and society. Performance indicators are categorized within five areas: (1) Preparation; (2) Participation; (3) Completion; (4) Affordability; and (5) Finance. Several indicators provide the national and MHEC regional values as well as the median of the top five states in the nation as possible benchmarks. Performance indicators are disaggregated by race/ethnicity and family income when data are available.
Higher Education in Focus 2019: Missouri. Essential Performance Indicators for Midwestern States
Author: Midwestern Higher Education Compact
Date: 2020-00-00
Type: Reports - Evaluative
Subjects: Student Evaluation,Higher Education,Educational Attainment,Educational Indicators,Postsecondary Education,Employment Qualifications,Labor Market,Occupations,Racial Differences,Ethnicity,Academic Degrees,High School Graduates,High School Students,Income,Preschool Education,Enrollment,Grade 8,Mathematics Achievement,Reading Achievement,Science Achievement,Graduation Rate,College Entrance Examinations,Core Curriculum,College Readiness,College Attendance,College Credits,Young Adults,Adults,College Transfer Students,Public Colleges,College Graduates,Time to Degree,Student Financial Aid,Grants,Family Income,Paying for College,Two Year Colleges,Colleges,Student Loan Programs,Debt (Financial),State Aid,Tuition,Educational Finance,Expenditure per Student,Institutional Characteristics,Low Income Students,Federal Aid,Elementary Secondary Education

In the United States, approximately 65 percent of all jobs in 2020 will require some level of postsecondary education, and the demand will reach 66 percent in Missouri. This report seeks to inform public discourse on higher education by providing key performance indicators relevant to the goal of improving educational attainment for a healthy economy and society. Performance indicators are categorized within five areas: (1) Preparation; (2) Participation; (3) Completion; (4) Affordability; and (5) Finance. Several indicators provide the national and Midwestern Higher Education Compact (MHEC) regional values as well as the median of the top five states in the nation as possible benchmarks. Performance indicators are disaggregated by race/ethnicity and family income when data are available.
Higher Education in Focus 2019: Nebraska. Essential Performance Indicators for Midwestern States
Author: Midwestern Higher Education Compact
Date: 2020-00-00
Type: Reports - Evaluative
Subjects: Student Evaluation,Higher Education,Educational Attainment,Educational Indicators,Postsecondary Education,Employment Qualifications,Labor Market,Occupations,Racial Differences,Ethnicity,Academic Degrees,High School Graduates,High School Students,Income,Preschool Education,Enrollment,Grade 8,Mathematics Achievement,Reading Achievement,Science Achievement,Graduation Rate,College Entrance Examinations,Core Curriculum,College Readiness,College Attendance,College Credits,Young Adults,Adults,College Transfer Students,Public Colleges,College Graduates,Time to Degree,Student Financial Aid,Grants,Family Income,Paying for College,Two Year Colleges,Colleges,Student Loan Programs,Debt (Financial),State Aid,Tuition,Educational Finance,Expenditure per Student,Institutional Characteristics,Low Income Students,Federal Aid,Elementary Secondary Education

In the United States, approximately 65 percent of all jobs in 2020 will require some level of postsecondary education, and the demand will reach 71 percent in Nebraska. This report seeks to inform public discourse on higher education by providing key performance indicators relevant to the goal of improving educational attainment for a healthy economy and society. Performance indicators are categorized within five areas: (1) Preparation; (2) Participation; (3) Completion; (4) Affordability; and (5) Finance. Several indicators provide the national and Midwestern Higher Education Compact (MHEC) regional values as well as the median of the top five states in the nation as possible benchmarks. Performance indicators are disaggregated by race/ethnicity and family income when data are available.