ethanporcaro/eric-fulltext-meta huggingface dataset:
Incidence of Teacher Curricular Emphasis in Reading Achievement of Uruguayan Ninth-Grade Students
Author: Borba, Elisa
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Secondary School Teachers,Teaching Methods,Secondary School Students,Reading Achievement,Grade 9,Foreign Countries,Reading Assignments,Student Characteristics,Socioeconomic Status,Cultural Influences,Reading Habits,Educational Opportunities,Learning Activities,Reading Strategies,Reading Tests,Scores,Middle School Students

Assessing Opportunities to Learn (OTL) implies the measurement of different aspects of the curricular implementation in the classrooms, such as the contents that the teacher selects for the course and the time of exposure and the frequency of the tasks proposed. Based on recent studies that demonstrate the influence of this curricular dimension on students' performance, this work analyzes the relationships between the emphasis made by teachers of third grade of secondary school on different reading contents, and students' performance in reading tests. The aim of this research is to establish the effect of the emphasis with which teachers propose different types of reading activities (literal, inferential, and critical) on students' performance. From the analysis of compositional data, this study concludes that the students of those teachers who report working a greater extent on the critical dimension of reading obtains--on average--higher scores on the national reading test. This result holds even when socio-economic and cultural context and reading habits are controlled for. [For the full proceedings, see ED607784.]
Identifying At-Risk K-12 Students in Multimodal Online Environments: A Machine Learning Approach
Author: Li, Hang
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: At Risk Students,Online Courses,Elementary Secondary Education,Learning Modalities,Time,Dropouts,Academic Persistence,Artificial Intelligence,Large Group Instruction,Prediction,Models,Influences,Middle School Students,High School Students,Probability

With the rapid emergence of K-12 online learning platforms, a new era of education has been opened up. It is crucial to have a dropout warning framework to preemptively identify K-12 students who are at risk of dropping out of the online courses. Prior researchers have focused on predicting dropout in Massive Open Online Courses (MOOCs), which often deliver higher education, i.e., graduate level courses at top institutions. However, few studies have focused on developing a machine learning approach for students in K-12 online courses. In this paper, we develop a machine learning framework to conduct accurate at-risk student identification specialized in K-12 multimodal online environments. Our approach considers both online and offline factors around K-12 students and aims at solving the challenges of (1) multiple modalities, i.e., K-12 online environments involve interactions from different modalities such as video, voice, etc.; (2) length variability, i.e., students with different lengths of learning history; (3) time sensitivity, i.e., the dropout likelihood is changing with time; and (4) data imbalance, i.e., only less than 20% of K-12 students will choose to drop out the class. We conduct a wide range of offline and online experiments to demonstrate the effectiveness of our approach. In our offline experiments, we show that our method improves the dropout prediction performance when compared to state-of-the-art baselines on a real-world educational dataset. In our online experiments, we test our approach on a third-party K-12 online tutoring platform for two months and the results show that more than 70% of dropout students are detected by the system. [For the full proceedings, see ED607784.]
Image Reconstruction of Tablet Front Camera Recordings in Educational Settings
Author: Wampfler, Rafael
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Photography,Handheld Devices,Student Behavior,Affective Behavior,Attention,Human Body,Learning Processes,Eye Movements,Mathematics Instruction,Problem Solving,Task Analysis,Emotional Response,Prediction,Classification,Undergraduate Students,Recognition (Psychology)

Front camera data from tablets used in educational settings offer valuable clues to student behavior, attention, and affective state. Due to the camera's angle of view, the face of the student is partially occluded and skewed. This hinders the ability of experts to adequately capture the learning process and student states. In this paper, we present a pipeline and techniques for image reconstruction of front camera recordings. Our setting consists of a cheap and unobtrusive mirror construction to improve the visibility of the face. We then process the image and use neural inpainting to reconstruct missing data in the recordings. We demonstrate the applicability of our setting and processing pipeline on affective state prediction based on front camera recordings (i.e., action units, eye gaze, eye blinks, and movement) during math-solving tasks (active) and emotional stimuli from pictures (passive) shown on a tablet. We show that our setup provides comparable performance for affective state prediction to recordings taken with an external and more obtrusive GoPro camera. [For the full proceedings, see ED607784.]
Erroneous Answers Categorization for Sketching Questions in Spatial Visualization Training
Author: Li, Tiffany Wenting
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Error Patterns,Cluster Grouping,Visualization,Spatial Ability,Training Methods,Questioning Techniques,Engineering Education,Freehand Drawing,Computer Assisted Instruction,College Students,Feedback (Response),Evaluation Criteria,Formative Evaluation

Spatial visualization skills are essential and fundamental to studying STEM subjects, and sketching is an effective way to practice those skills. One significant challenge of supporting practice using sketching questions is the vast number of possible mistakes, making it time-consuming for instructors to provide customized and actionable feedback to students. The same challenge persists for computer programs as well. This paper introduces a clustering model designed to categorize sketching answers based on the severity and characteristics of their mistakes. The model is designed to be used by a computer-based training platform to provide customized, actionable formative feedback to students in real-time. The promising results also suggest a new and comprehensive set of evaluation criteria to assess a student's performance on sketching questions. As a broader contribution, our work is a proof-of-concept for a modeling approach to automatically evaluate and provide formative feedback on complex free-hand sketches using abstract features that may be generalized to a variety of disciplines that involve the creation of technical drawings. [For the full proceedings, see ED607784.]
Evaluating Sources of Course Information and Models of Representation on a Variety of Institutional Prediction Tasks
Author: Jiang, Weijie
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Course Descriptions,Models,Prediction,Course Selection (Students),Enrollment Trends,Data Analysis,Data Use,Integrated Learning Systems,Enrollment

Data mining of course enrollment and course description records has soared as institutions of higher education begin tapping into the value of these data for academic and internal research purposes. This has led to a more than doubling of papers on course prediction tasks every year. The papers often center around a single prediction task and introduce a single novel modeling approach utilizing one or two data sources. In this paper, we provide the most comprehensive evaluation to date of data sources, models, and their performance on downstream prediction tasks. We separately incorporate syllabus, catalog description, and enrollment history data to represent courses using graph embedding, course2vec (i.e., skip-gram), and classic bag-of-words models. We evaluate these representations on the tasks of predicting course prerequisites, credit equivalencies, student next semester enrollments, and student course grades. Most notably, our results show that syllabi bag-of-words representations performed better than course descriptions in predicting prerequisite relationships, though enrollment-based graph embeddings performed substantially better still. Course descriptions provided the highest single representation accuracy in predicting course similarity, with descriptions, syllabi, and course2vec combined representations providing the highest ensembled accuracy on this task. [For the full proceedings, see ED607784.]
Harbingers of Collaboration? The Role of Early-Class Behaviors in Predicting Collaborative Problem Solving
Author: Hur, Paul
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Cooperative Learning,Interaction,Peer Relationship,Handheld Devices,Learning Analytics,Group Dynamics,Problem Solving,Predictor Variables,Undergraduate Students,Computer Assisted Instruction,Engineering Education,Introductory Courses,Time Factors (Learning),Accuracy

Collaborative problem solving behaviors are difficult to identify and foster due to their amorphous and dynamic nature. In this paper, we investigate the value of considering early class period behaviors, based on small group development theory, for building predictive machine learning models of collaborative behaviors during problem solving. Over 12 weeks, 20 small groups of undergraduate students solved problems facilitated by a digital joint problem space tool on tablet computers, in the 50-minute discussion component of an engineering course. We annotated 16,270 video clips of groups for collaborative behaviors including task relatedness, talk content, peer interaction, teaching assistant interaction, and tablet usage. We engineered two subsets of features from tablet log file data: onset features (early collaborative problem solving behavior characteristics calculated from the first ten minutes of the class) and concurrent features (more general collaborative behaviors from the whole class period). We compared accuracy between the onset, concurrent, and onset + concurrent features in machine learning models. Results exhibited a U-shaped pattern of accuracy over class time, and showed that onset features alone could not be used to effectively model groups' collaborative behaviors over the entire class time. Furthermore, analysis did not show support for significant gain in accuracy when onset features were combined with concurrent features. Finally, we discuss implications for studying collaborative learning and development of software to facilitate collaboration. [For the full proceedings, see ED607784.]
Exploring Homophily in Demographics and Academic Performance Using Spatial-Temporal Student Networks
Author: Nguyen, Quan
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Social Networks,Network Analysis,Data Analysis,Computer Networks,Academic Achievement,Student Characteristics,Gender Differences,Ethnic Groups,Self Concept,Family Income,Grades (Scholastic),Correlation,Minority Group Students,College Students,Educational Experience,Proximity,Peer Relationship

Network analysis in educational research has primarily relied on self-reported relationships or connections inferred from online learning environments, such as discussion forums. However, a large part of students' social connections through day-to-day on-campus encounters has remained underexplored. The paper examines spatial-temporal student networks using campus WiFi log data throughout a semester, and their relations to the student demographics and academic performance. A tie in the spatial-temporal network was inferred when two individuals connected to the same WiFi access point at the same time intervals at the 'beyond chance' frequency. Our findings revealed that students were more likely to co-locate with the individuals of similar gender, ethnic group identity, family income, and grades. Analysis of homophily over the semester showed that students of the same gender were more likely to co-locate as the semester progressed. However, co-location of the students similar on ethnic minority identity, family income, and grades remained consistent throughout the semester. Mixed-effect regression models demonstrated that features derived from spatial-temporal networks, such as degree, the grade of the most frequently co-located peer, and average grade of five most frequently co-located peers were positively associated with academic performance. This study offers a unique exploration of the potential use of WiFi log data in understanding of student relationships integral to the quality of college experience. [For the full proceedings, see ED607784.]
Getting Too Personal(ized): The Importance of Feature Choice in Online Adaptive Algorithms
Author: Li, ZhaoBin
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Educational Technology,Technology Uses in Education,Student Needs,Student Characteristics,Mathematics,Computer Simulation,Models,Probability

Digital educational technologies offer the potential to customize students' experiences and learn what works for which students, enhancing the technology as more students interact with it. We consider whether and when attempting to discover how to personalize has a cost, such as if the adaptation to personal information can delay the adoption of policies that benefit all students. We explore these issues in the context of using multi-armed bandit (MAB) algorithms to learn a policy for what version of an educational technology to present to each student, varying the relation between student characteristics and outcomes and also whether the algorithm is aware of these characteristics. Through simulations, we demonstrate that the inclusion of student characteristics for personalization can be beneficial when those characteristics are needed to learn the optimal action. In other scenarios, this inclusion decreases performance and increases variation in student experiences. Moreover, including unneeded student characteristics can systematically disadvantage students with less common values for these characteristics. Our simulations do however suggest that real-time personalization will be helpful in particular real-world scenarios, and we illustrate this through case studies using existing experimental results in ASSISTments. Overall, our simulations show that adaptive personalization in educational technologies can be a double-edged sword: real-time adaptation improves student experiences in some contexts, but the slower adaptation and increased variability mean that a more personalized model is not always beneficial. [For the full proceedings, see ED607784.]
Early Words Together at Three in Leicester. A National Literacy Trust Research Report
Author: Cappellini, Chiara
Date: 2020-01-00
Type: Reports - Research
Subjects: Vocabulary Development,Foreign Countries,Family Environment,Low Income Groups,Program Effectiveness,Parent Education,Reading Programs,Literature Appreciation,Rhyme,Singing,Parent Attitudes,Self Concept,Toddlers,Behavior,Multilingualism

This report evaluates the impact of the National Literacy Trust's Early Words Together at Three programme in Leicester, which ran from September 2018 to December 2019. Early Words Together at Three trains school and nursery staff to help parents of three-year-olds to support their child's development. This programme enables parents to understand the value of the home learning environment, and equips them with evidence-based strategies and activities they can do at home with their child. It is an adaptation of our original programme, Early Words Together, and is designed for nursery practitioners working directly with families of three-year-olds. The programme ran in 15 primary school nurseries in Leicester and was funded by Leicester's maintained primary schools through the &quot;Closing the Gap&quot; initiative. An evaluation of the programme was undertaken by Chiara Cappellini, Evaluation Manager, Kirsty Cunnington, Early Years Programme Manager, and Rachel Duncan, Early Years Project Manager, at the National Literacy Trust. The evaluation report key findings include: (1) 78% of parents say they are more confident singing songs and sharing stories with their child after the programme; (2) 67% of parents say they read and share stories with their child more than before; (3) 78% of parents report that their child enjoys sharing books more than before; (4) 78% of parents report that they are more aware of why it is important for them to talk to their child; and (5) 91% of practitioners encourage their parents to listen to their child following the programme (32% before).
Feature Selection Metrics: Similarities, Differences, and Characteristics of the Selected Models
Author: Sanyal, Debopam
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Artificial Intelligence,Selection,Learning Analytics,Evaluation Criteria,Data,Models

Supervised machine learning has become one of the most important methods for developing educational and intelligent tutoring software; it is the backbone of many educational data mining methods for estimating knowledge, emotion, and other aspects of learning. Hence, in order to ensure optimal utilization of computing resources and effective analysis of models, it is essential that researchers know which evaluation metrics are best suited to educational data. In this article, we focus on the problem of wrapper feature selection, where predictors are added to models based on how much they improve model accuracy in terms of a given metric. We compared commonly-used machine learning algorithms including naive Bayes, support vector machines, logistic regression, and random forests on 11 diverse learning-related datasets. We optimized feature selection based on nine different metrics, then evaluated each to address research questions about how effective each metric was in terms of the others (e.g., does optimizing for precision also result in good F1?) as well as calibration (i.e., are predictions produced by models accurate probabilities of correctness?). We provide empirical evidence that the Matthews correlation coefficient (MCC) produced the overall best results across the other metrics, but that root mean squared error (RMSE) selected the best-calibrated models. Finally, we also discuss issues related to the number of features selected when optimizing for each metric, as well as the types of datasets for which certain metrics were more effective. [For the full proceedings, see ED607784.]
Video Game Playing and Literacy: A Survey of Young People Aged 11 to 16. A National Literacy Trust Research Report
Author: Picton, Irene
Date: 2020-08-00
Type: Reports - Evaluative
Subjects: Video Games,Adolescents,Preadolescents,Foreign Countries,Literacy,Reading Attitudes,Reading Motivation,Reading Skills,Writing (Composition),Interpersonal Communication,Friendship,Writing Attitudes,Time,Preferences,Books,Self Esteem,Empathy,Learning,Psychological Patterns,Age Differences,Gender Differences,Socioeconomic Status

Video games are a significant cultural and creative force, often involving a number of art forms, including narrative, design and audio composition. They have been the subject of a large-scale exhibition at the Victoria and Albert museums in London and Dundee, while the British Games Institute (BGI) was set up to support video game culture in 2019. The National Literacy Trust recognises the importance of technology as a tool for teaching literacy skills, and the way that technology can be used to support and enrich the literacy lives of children and young people is an integral part of our research and programme work. While a considerable number of international studies and reviews have explored game-based learning, fewer have focused specifically on video games and children's literacy. The following report outlines findings from a short survey of young people conducted in late 2019. The survey received 4,626 responses from young people aged 11 to 16 across the UK. This was intended to serve as a preliminary investigation of young people's literacy-related interactions both within, and in relation to, video games. Further qualitative work is planned to complement these early findings, to contribute to the evidence base investigating the potential for video games to engage children and young people with literacy.
Faculty Job Market in Physics and Astronomy Departments: Results from the 2018 Academic Workforce Survey. Focus On
Author: Porter, Anne Marie
Date: 2020-06-00
Type: Reports - Research
Subjects: College Faculty,Science Teachers,Physics,Astronomy,Science Departments,Faculty Mobility,Teacher Retirement,Teacher Recruitment,Teacher Selection,Tenure,Educational Background,Teacher Background,Part Time Faculty,Nontenured Faculty,Women Faculty

In any academic department, faculty members retire or leave for a variety of reasons, and new faculty members are hired to replace departing faculty or fill newly created positions. This report focuses on departures, retirements, recruitments, and new hires in physics and astronomy departments. In our Academic Workforce Surveys, we collect the number of faculty departures in the previous academic year, the number of retirements for the &quot;previous&quot; and the &quot;current&quot; academic year, the number of new hires in the &quot;current&quot; academic year, and the number of recruitments for the &quot;following&quot; academic year. These are the most current data the departments can report. Thus, the numbers for departures, retirements, recruitments, and new hires reflect different academic years.
Social Interoperability in Research Support: Cross-Campus Partnerships and the University Research Enterprise. OCLC Research Report
Author: Bryant, Rebecca
Date: 2020-00-00
Type: Reports - Research
Subjects: Cooperation,Research,Universities,Interprofessional Relationship,Stakeholders,College Environment,Research Administration,Models,Services

To develop robust research support services across the entire research life cycle, individuals and units from across the university, including the library, must collaborate across internal silos. Effective &quot;social interoperability&quot;--the creation and maintenance of working relationships between individuals and organizational units--in higher education requires a thorough knowledge of campus partners. The OCLC Research report &quot;Social Interoperability in Research Support&quot; explores the social and structural norms that shape cross-campus collaboration and offers a conceptual model of key university stakeholders in research support. Information about their goals, interests, expertise, and crucially, the importance of cross-campus relationships in their work was synthesized from interviews conducted with practitioners from a wide range of campus stakeholders in research support. The report describes the network of campus units involved in both the provision and consumption of major categories of research support services, and concludes with recommendations for establishing and maintaining successful cross-campus relationships.