ethanporcaro/eric-fulltext-meta huggingface dataset:
Reflecting on Systems Change: Learning from the Partnership for Pre-K Improvement
Author: Connors, Maia
Date: 2022-03-00
Type: Reports - Descriptive
Subjects: Educational Improvement,Preschool Education,Partnerships in Education,Educational Change,Educational Strategies,Educational Practices

The Partnership for Pre-K Improvement (PPI) was launched in 2016 as a project with a vision to develop and sustain high-quality, equitable Pre-K programs that produce improved and more-equitable kindergarten readiness and greater academic success for children through the early elementary grades. The multiyear, multistate (Oregon, Tennessee, and Washington) effort aimed to learn in partnership with states across government leaders, advocates and researchers about how to systematically improve the quality of Pre-K at scale. This report highlights the work achieved by the state leaders, researchers and advocates in Oregon, Tennessee and Washington as part of their PPI strategic vision for improvement. It also describes five foundational assumptions implicit in the PPI strategy that together shaped the design, approach and implementation of the work.
An Evaluation of CODE2VEC Embeddings for Scratch
Author: Fein, Benedikt
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Artificial Intelligence,Learning Analytics,Programming,Programming Languages,Classification,Children,Gender Differences,Computer Science Education

The recent trend of embedding source code for machine learning applications also enables new opportunities in learning analytics in programming education, but which code embedding approach is most suitable for learning analytics remains an open question. A common approach to embedding source code lies in extracting syntactic information from a program's syntax tree and learning to merge these into continuous distributed vectors (e.g., CODE2VEC). CODE2VEC has been predominantly investigated in the context of professional programming languages, but learning analytics are particularly important in the context of educational programming languages such as SCRATCH. In this paper, we therefore instantiate the popular embedding approach CODE2VEC for SCRATCH programs, create three different classification tasks with corresponding datasets, and empirically evaluate CODE2VEC on them. Our experiments demonstrate that a transfer of CODE2VEC to the educational environment of SCRATCH is feasible. Our findings serve as a basis to apply code embeddings to further educational tasks such as automated detection of misconceptions of programming concepts in SCRATCH programs. [For the full proceedings, see ED623995.]
Can Population-Based Engagement Improve Personalisation? A Novel Dataset and Experiments
Author: Bulathwela, Sahan
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Video Technology,Lecture Method,Data Analysis,Prediction,Learner Engagement,Computer Science Education,Models,Data,MOOCs,Electronic Learning

This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections. The paper introduces: (1) VLE, a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures coupled with implicit and explicit signals related to learner engagement; (2) two standard tasks related to predicting and ranking context-agnostic engagement in video lectures with preliminary baselines; and (3) a set of experiments that validate the usefulness of the proposed dataset. Our experimental results indicate that the newly proposed VLE dataset leads to building context-agnostic engagement prediction models that are significantly performant than ones based on previous datasets, mainly attributing to the increase of training examples. VLE dataset's suitability in building models towards Computer Science/ Artificial Intelligence education focused on e-learning/MOOC use-cases is also evidenced. Further experiments in combining the built model with a personalising algorithm show promising improvements in addressing the cold-start problem encountered in educational recommenders. This is the largest and most diverse publicly available dataset to our knowledge that deals with learner engagement prediction tasks. The dataset, helper tools, descriptive statistics and example code snippets are available publicly. [For the full proceedings, see ED623995.]
Challenges and Feasibility of Automatic Speech Recognition for Modeling Student Collaborative Discourse in Classrooms
Author: Southwell, Rosy
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Audio Equipment,Error Analysis (Language),Classroom Communication,Feedback (Response),Barriers,Speech Communication,Middle School Students,Cooperative Learning,Accuracy,Computer Software,Error Patterns,Scores,Semantics,Classification

Automatic speech recognition (ASR) has considerable potential to model aspects of classroom discourse with the goals of automated assessment, feedback, and instructional support. However, modeling student talk is besieged by numerous challenges including a lack of data for child speech, low signal to noise ratio, speech disfluencies, and multiparty chatter. This raises the question as to whether contemporary ASR systems, which are benchmarked on adult speech in idealized conditions, can be used to transcribe child speech in classroom settings. To address this question, we collected a dataset of 32 audio recordings of 30 middle-school students engaged in small group work (dyads, triads and tetrads) in authentic classroom settings. The audio was sampled, segmented, and transcribed by humans as well as three ASR engines (Google, Rev.ai, IBM Watson). Whereas all three ASRs had high word error rates, these mainly consisted of deletion errors. Further, Google successfully transcribed a greater proportion of utterances than the other two, but with more word substitutions; insertions were low across the board. ASR accuracy was robust to different speakers and recording idiosyncrasies evidenced by &lt;5% of variance in error rates attributable to the student and recording session. We found that ASR errors had a larger negative effect on downstream natural language processing tasks at the word, phrase, and semantic levels rather than at the discourse level. Our findings indicate that ASR can be used to extract meaningful information from noisy classroom speech and might be more suitable for applications that require higher precision but are tolerant of lower recall. [For the full proceedings, see ED623995.]
[MCCS First-Generation Enrollment Report 2020]
Author: Daigler, David
Date: 2020-12-22
Type: Reports - Evaluative
Subjects: Community Colleges,First Generation College Students,Enrollment Trends,Graduation Rate,Outcomes of Education,Intervention,Parent Background,Educational Attainment,Student Personnel Services,Student Employment,College Readiness

The Maine Community College System provides this letter in accordance with 20-A MRSA §10013. This law requires the Maine Community College System (MCCS), University of Maine System (UMS), and Maine Maritime Academy (MMA) to provide the following information about first-generation college students by January 15th of each year: (1) Data regarding enrollment of first-generation college students and educational outcomes, including graduation rates for first-generation college students compared with other college students; (2) A summary of strategies used and activities undertaken to increase enrollment and graduation rates of first-generation college students and any available data indicating the effect of those strategies and activities; and (3) Plans for or recommendations regarding new strategies or actions designed to increase enrollment and graduation rates of first-generation college students. To meet the requirements of the legislation, MCCS worked with UMS and MMA to coordinate data collection and reporting in order to promote a common framework for determining enrollment and educational outcomes of first-generation college students. Additionally, the three institutions agreed that for the purpose of this report, a first-generation student is defined as one with neither parent having completed a college degree. Each institution determined the level of college completion appropriate to its mission. For MCCS, a student is first-generation if neither parent has earned an associate degree. For UMS and MMA, a student is first-generation if neither parent has earned a bachelor's degree. For consistency, data is also provided on MCCS students with neither parent having earned a bachelor's degree.
Frequently Asked Questions about Pennsylvania&apos;s Educational Improvement Tax Credit (EITC) and Vouchers for Private and Religious Schools
Author: Spicka, Susan
Date: 2022-06-00
Type: Reports - Descriptive
Subjects: Tax Credits,Educational Vouchers,Private Schools,Religious Schools,Elementary Secondary Education,Educational Finance

Pennsylvania has two programs that give tax money to private schools: the Educational Improvement Tax Credit (EITC) and the Opportunity Scholarship Tax Credit (OSTC) programs. Each year, EITC and OSTC provide $230 million to private scholarship organizations, or &quot;SOs,&quot; which award tuition vouchers to families whose children attend private and religious schools. Funding for K-12 private school vouchers via the EITC/OSTC programs has increased nearly twelvefold over the past two decades, yet Pennsylvania taxpayers know virtually nothing about who has benefited from these programs or that more than $1 billion in diverted tax dollars have been spent on K-12 private/religious school vouchers since 2001. They also know nothing about the academic outcomes of students who have received these vouchers. This &quot;frequently asked questions&quot; document focuses on the $230 million in funding for EITC and OSTC programs that provide vouchers to students who attend private/religious schools. [This report was co-produced by Education Voters of Pennsylvania.]
Emotional Intelligence and Emotional Expression as Predictors of Depression in University Students
Author: Arig, Seda
Date: 2020-00-00
Type: Journal Articles
Subjects: Prediction,Emotional Intelligence,Undergraduate Students,Emotional Response,Measures (Individuals),Depression (Psychology),Gender Differences,Family Size,Student Characteristics,Student Attitudes,Majors (Students),Correlation,Self Control,Foreign Countries

In this study, the degree of predicting depression of university students' emotional intelligence and emotional expression was investigated. For this purpose, the sub-dimensions of the research, the differentiation of depression level according to some demographic variables and the relationship between depression level and emotional intelligence and emotional expression were also examined. The participants of the study were 370 students who studying at Biruni University. To the participants, prepared by the researcher; Personal Information Form; including gender, number of siblings, parental status or separation, faculty and grade level was applied. In addition, Beck Depression Scale (Hisli, 1988), Emotional Intelligence Feature Scale (TEIQue-SF) Short Form (Deniz et al., 2013) and Emotional Expression Questionnaire (Kuzucu, 2006) were applied. In the study, no significant difference was found between depression and the demographic variables of the study. In the results related to depression and emotional intelligence; emotional intelligence and depression of students were negatively related. Accordingly, as students' emotional intelligence increases, their depression levels decrease. In addition, it was found that students' emotional intelligence significantly predicted their depression levels. According to this result, it may be possible to estimate the depression levels by looking at the emotional intelligence levels of the students. In the results related to depression and emotional expression, positive emotional expression and proximity expression were found to be negatively related to depression. In addition, there is a positive relationship between negative emotion expression and depression. Accordingly, as the students' proximity expression and positive emotional expression increase, their depression levels decrease and as negative emotional expression increase, their depression levels increase. At the same time, it was determined that students' emotional expression significantly predicted depression. Accordingly, it may be possible to predict depression levels by looking at students emotional expression.
Educated Arabs&apos; Reading Interests and Preferences Before, during and after the Pandemic
Author: Al-Jarf, Reima
Date: 2022-00-00
Type: Journal Articles
Subjects: Reading Interests,Arabs,Graduate Students,Undergraduate Students,Pandemics,COVID-19,College Faculty,Professional Personnel,Teacher Attitudes,Student Attitudes,Social Media,Computer Mediated Communication,Educational Attainment,Islam,Novels,Fiction,Preferences,Individual Development,Printed Materials,Electronic Publishing,Books,Comparative Analysis,Textbooks,Reading Habits,Clubs,Libraries,Reading Motivation,Computer Software,World Affairs,Teaching Methods

This study explored the reading interests, and preferences of educated Arabs such as students, faculty and professionals, the types and number of books they read before, during and after the Pandemic (between 2012 and 2022). Data were collected from Twitter threads, hashtags and surveys and responses to the question &quot;What are you currently reading&quot;. The Twitter data analysis showed that the preferred book titles with the highest percentages were novels/fiction (31.5% before the Pandemic and 41.5% during the Pandemic); Islamic books (22.5% before the Pandemic and 12% after the Pandemic);(iii) self-development books (12.5% before the Pandemic and 20% during the Pandemic). 80.7% preferred to read paper books and 19.3% electronic books. In most surveys, between 50%-60% of the respondents do not read at all. In 2 surveys, between 76%-80% do not read. The highest number of books read was during the Pandemic as 32.5% read 5 books and 38.5% read more than 20 books. The status of reading after the Pandemic was like that before the Pandemic (43.4% did not read). Even graduate and undergraduate students. 31% of graduate and undergraduate students read nothing outside their course textbooks. For enhancing students' reading interests, preferences and the types and amounts of books read, the study recommended using book clubs, BookTok, encouraging students to visit libraries, using online courses, blogs, and mobile apps for discussing books read, integrating global topics in the school and college curriculum and others.
Strategies for Effective Distance Learning in Foreign Language, Linguistics and Translation College Courses
Author: Al-Jarf, Reima
Date: 2022-00-00
Type: Journal Articles
Subjects: Translation,COVID-19,Pandemics,Second Language Learning,Second Language Instruction,Educational Change,Distance Education,Teacher Attitudes,Student Attitudes,Teacher Student Relationship,Online Courses,In Person Learning,College Faculty,College Students,Teaching Methods,Integrated Learning Systems,Instructional Effectiveness,Technological Literacy,Faculty Development,College Second Language Programs,Instructional Materials,Educational Resources,Feedback (Response),Computer Mediated Communication,Computer Assisted Testing,Pedagogical Content Knowledge,Computer Software,Foreign Countries

Due to COVID-19, there was a sudden shift to distance learning (DL) without prior preparation. Students and faculty surveys showed that 51% of the students were dissatisfied with EL, online communication with their instructors, had difficulty understanding the online class lectures and material and had concerns about exams. Even when face to face instruction was resumed in Fall 2021, some courses were still delivered online. It seems that some departments, colleges, and universities will continue to offer some courses online. To make the best use of online teaching and learning, this article proposes a model consisting of strategies for making distance teaching and learning in foreign language, linguistics and translation college courses effective, interactive, engaging and fun. Those strategies are: Using some free platforms, supplying devices and good infrastructure to both students and instructors, practicing using the platform, supplementing the platform with one or more technologies, orientation, training and support, telling the students about online class logistics, selecting online course material and resources, integrating global topics and participation goals in the class material, practicing a variety of language skills with online videos, blogs, mobile apps and others, using effective teaching techniques, assignments, and activities, giving feedback, motivating and engaging the students, online communication and interaction, nurturing the online classroom community, preparing and following effective and reliable online testing procedures, the instructor's role in the distance learning environments, evaluating the students' and faculty DL experience, and providing teachers' professional development and support.
Optimizing Representations and Policies for Question Sequencing Using Reinforcement Learning
Author: Azhar, Aqil Zainal
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Reinforcement,Instructional Materials,Learning Analytics,Policy Analysis,Learning Processes,Academic Achievement,Monte Carlo Methods,Policy Formation,Markov Processes,MOOCs,Foreign Countries,Questioning Techniques,Models,Difficulty Level,English (Second Language),Language Tests,Second Language Learning

This paper studies the use of Reinforcement Learning (RL) policies for optimizing the sequencing of online learning materials to students. Our approach provides an end to end pipeline for automatically deriving and evaluating robust representations of students' interactions and policies for content sequencing in online educational settings. We conduct the training and evaluation offline based on a publicly available dataset of diverse student online activities used by tens of thousands of students. We study the influence of the state representations on the performance of the obtained policy and its robustness towards perturbations on the environment dynamics induced by stronger and weaker learners. We show that 'bigger may not be better', in that increasing the complexity of the state space does not necessarily lead to better performance, as measured by expected future reward. We describe two methods for offline evaluation of the policy based on importance sampling and Monte Carlo policy evaluation. This work is a first step towards optimizing representations when designing policies for sequencing educational content that can be used in the real world. [For the full proceedings, see ED623995.]
Predicting Cognitive Engagement in Online Course Discussion Forums
Author: Gorgun, Guher
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,Group Discussion,Learner Engagement,Student Participation,Predictor Variables,Graduate Students,Models,Classification,Foreign Countries,Error Patterns,Automation

The need to identify student cognitive engagement in online-learning settings has increased with our use of online learning approaches because engagement plays an important role in ensuring student success in these environments. Engaged students are more likely to complete online courses successfully, but this setting makes it more difficult for instructors to identify engagement. In this study, we developed predictive models for automating the identification o f cognitive engagement in online discussion posts. We adapted the Interactive, Constructive, Active, and Passive (ICAP) Engagement theory [15] by merging ICAP with Bloom's taxonomy. We then applied this adaptation of ICAP to label student posts (N = 4,217), thus capturing their level of cognitive engagement. To investigate the feasibility of automatically identifying cognitive engagement, the labelled data were used to train three machine learning classifiers (i.e., decision tree, random forest, and support vector machine). Model inputs included features extracted by applying CohMetrix to student posts and non-linguistic contextual features (e.g., number of replies). The support vector machine model outperformed the other classifiers. Our findings suggest it is feasible to automatically identify cognitive engagement in online learning environments. Subsequent analyses suggest that new language features (e.g., AWL use) should be included because they support the identification o f cognitive engagement. Such detectors could be used to help identify students who are in need of support or help adapt teaching practices and learning materials. [For the full proceedings, see ED623995.]
Code-DKT: A Code-Based Knowledge Tracing Model for Programming Tasks
Author: Shi, Yang
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Programming,Knowledge Level,Prediction,Instructional Innovation,Comparative Analysis,Introductory Courses,Computer Science Education,Case Studies,Assignments,Models,Bayesian Statistics,Evaluation Methods,Learning Processes,Programming Languages,Undergraduate Students

Knowledge tracing (KT) models are a popular approach for predicting students' future performance at practice problems using their prior attempts. Though many innovations have been made in KT, most models including the state-of-the-art Deep KT (DKT) mainly leverage each student's response either as correct or incorrect, ignoring its content. In this work, we propose Code-based Deep Knowledge Tracing (Code-DKT), a model that uses an attention mechanism to automatically extract and select domain-specific code features to extend DKT. We compared the effectiveness of Code-DKT against Bayesian and Deep Knowledge Tracing (BKT and DKT) on a dataset from a class of 50 students attempting to solve 5 introductory programming assignments. Our results show that Code-DKT consistently outperforms DKT by 3.07-4.00% AUC across the 5 assignments, a comparable improvement to other state-of-the-art domain-general KT models over DKT. Finally, we analyze problem-specific performance through a set of case studies for one assignment to demonstrate when and how code features improve Code-DKT's predictions. [For the full proceedings, see ED623995.]