ethanporcaro/eric-fulltext-meta huggingface dataset:
Early Childhood Education Needs Assessment: &quot;Minneapolis Public Schools -- Early Childhood Education&quot;
Author: Valorose, Jennifer
Date: 2016-02-00
Type: Reports - Research
Subjects: Early Childhood Education,Needs Assessment,Public Schools,Young Children,Minority Group Students,Barriers

Over 27,000 children under age 5 live in Minneapolis. All are eligible to be served by Minneapolis Public Schools (MPS) early childhood programming -- Early Childhood Education, Early Childhood Family Education (ECFE), Early Childhood Screening, and the High Five and Three School preschool programs. To better understand the needs of families with young children and improve services, MPS partnered with Wilder Research in spring 2015 to conduct a community needs assessment. Wilder Research analyzed census and birth record data to describe the families with young children who are living in Minneapolis, conducted 21 interviews with key stakeholders, and conducted 300 surveys from parents. In addition, Wilder conducted seven focus groups with families in communities that were identified as least reached. This report shares findings from this assessment, along with recommendations for MPS Early Childhood Education to consider as it moves forward in serving children under 5 and their families.
Improving Automated Scoring of Student Open Responses in Mathematics
Author: Baral, Sami
Date: 2021-00-00
Type: Reports - Research
Subjects: Scoring,Automation,Mathematics Tests,Student Evaluation,Evaluation Methods,Models,Scores,Error Patterns,Sentences,Semantics,Grading,Middle School Teachers

Open-ended questions in mathematics are commonly used by teachers to monitor and assess students' deeper conceptual understanding of content. Student answers to these types of questions often exhibit a combination of language, drawn diagrams and tables, and mathematical formulas and expressions that supply teachers with insight into the processes and strategies adopted by students in formulating their responses. While these student responses help to inform teachers on their students' progress and understanding, the amount of variation in these responses can make it difficult and time-consuming for teachers to manually read, assess, and provide feedback to student work. For this reason, there has been a growing body of research in developing AI-powered tools to support teachers in this task. This work seeks to build upon this prior research by introducing a model that is designed to help automate the assessment of student responses to open-ended questions in mathematics through sentence-level semantic representations. We find that this model outperforms previously-published benchmarks across three different metrics. With this model, we conduct an error analysis to examine characteristics of student responses that may be considered to further improve the method. [For the full proceedings, see ED615472.]
Integrating Deep Learning into an Automated Feedback Generation System for Automated Essay Scoring
Author: Lu, Chang
Date: 2021-00-00
Type: Reports - Research
Subjects: Learning Processes,Automation,Computer Assisted Testing,Scoring,Feedback (Response),Computation,Mathematics,Essays,Natural Language Processing,Student Evaluation,Accuracy,Semantics,Markov Processes

Digitalization and automation of test administration, score reporting, and feedback provision have the potential to benefit large-scale and formative assessments. Many studies on automated essay scoring (AES) and feedback generation systems were published in the last decade, but few connected AES and feedback generation within a unified framework. Recent advancements in machine learning algorithms enable researchers to develop more models that explore the potential of automated assessments in education. This study makes the following contributions. First, it implements, compares, and contrasts three AES algorithms with word-embedding and deep learning models (CNN, LSTM, and BiLSTM). Second, it proposes a novel automated feedback generation algorithm based on the Constrained Metropolis-Hastings Sampling (CGMH). Third, it builds a classifier to integrate AES and feedback generation into a systematic framework. Results show that: (1) the scoring accuracy of the AES algorithm outperforms that of state-of-the-art models; and (2) the CGMH method generates semantically-related feedback sentences. The findings support the feasibility of an automated system that combines essay scoring with feedback generation. Implications may lead to the development of models that reveal linguistic features, while achieving high scoring accuracy, as well as to the creation of feedback corpora to generate more semantically-related and sentiment-appropriate feedback. [For the full proceedings, see ED615472.]
Investigating SMART Models of Self-Regulation and Their Impact on Learning
Author: Hutt, Stephen
Date: 2021-00-00
Type: Reports - Research
Subjects: Models,Self Control,Learning Strategies,Student Behavior,Educational Environment,Simulated Environment,Artificial Intelligence,Middle School Students,Grade 6,Prediction

Self-regulated learning (SRL) is a critical 21st -century skill. In this paper, we examine SRL through the lens of the searching, monitoring, assessing, rehearsing, and translating (SMART) schema for learning operations. We use microanalysis to measure SRL behaviors as students interact with a computer-based learning environment, Betty's Brain. We leverage interaction data, survey data, &quot;in situ&quot; student interviews, and supervised machine learning techniques to predict the proportion of time spent on each of the SMART schema facets, developing models with prediction accuracy ranging from &quot;rho&quot; = 0.19 for translating to &quot;rho&quot; = 0.66 for assembling. We examine key interactions between variables in our models and discuss the implications for future SRL research. Finally, we show that both ground truth and predicted values can be used to predict future learning in the system. In fact, the inferred models of SRL outperform the ground truth versions, demonstrating both their generalizability and their potential for using these models to improve adaptive scaffolding for students who are still developing SRL skills. [For the full proceedings, see ED615472.]
Do Common Educational Datasets Contain Static Information? A Statistical Study
Author: Barollet, Théo
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Intelligent Tutoring Systems,Prediction,Internet,Purchasing,Multimedia Materials,Computer Software,Data Analysis,Statistics,Learning Analytics,Accuracy,Comparative Analysis,Learning Processes,Online Courses,Algebra,Mathematics Instruction,Problem Solving,Films,Mathematics Tests

In Intelligent Tutoring Systems (ITS), methods to choose the next exercise for a student are inspired from generic recommender systems, used, for instance, in online shopping or multimedia recommendation. As such, collaborative filtering, especially matrix factorization, is often included as a part of recommendation algorithms in ITS. One notable difference in ITS is the rapid evolution of users, who improve their performance, as opposed to multimedia recommendation where preferences are more static. This raises the following question: how reliably can we use matrix factorization, a tool tried and tested in a static environment, in a context where timelines seem to be of importance. In this article we tried to quantify empirically how much information can be extracted statically from datasets in education versus datasets in multimedia, as the quality of such information is critical to be able to accurately make predictions and recommendations. We found that educational datasets contain less static information compared to multimedia datasets, to the extent that vectors of higher dimensions only marginally increase the precision of the matrix factorization compared to a 1-dimensional characterization. These results show that educational datasets must be used with time information, and warn against the dangers of directly trying to use existing algorithms developed for static datasets. [For the full proceedings, see ED615472.]
Exploring Policies for Dynamically Teaming up Students through Log Data Simulation
Author: Yang, Kexin Bella
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Grouping (Instructional Purposes),Cooperative Learning,Teaching Methods,Kindergarten,Elementary Secondary Education,Educational Policy,Intelligent Tutoring Systems,Learning Analytics,Computer Simulation,Middle School Students,Policy Analysis,Comparative Analysis,Heterogeneous Grouping,Student Characteristics,Knowledge Level

Constructing effective and well-balanced learning groups is important for collaborative learning. Past research explored how group formation policies affect learners' behaviors and performance. With the different classroom contexts, many group formation policies work in theory, yet their feasibility is rarely investigated in authentic class sessions. In the current work, we define &quot;feasibility&quot; as the ratio of students being able to find available partners that satisfy a given group formation policy. Informed by user-centered research in K-12 classrooms, we simulated pairing policies on historical data from an intelligent tutoring system (ITS), a process we refer to as &quot;SimPairing.&quot; As part of the process for designing a pairing orchestration tool, this study contributes insights into the feasibility of four dynamic pairing policies, and how the feasibility varies depending on parameters in the pairing policies or different classes. We found that on average, dynamically pairing students based on their in-the-moment wheel-spinning status can pair most struggling students, even with moderate constraints of restricted pairings. In addition, we found there is a trade-off between the required knowledge heterogeneity and policy feasibility. Furthermore, the feasibility of pairing policies can vary across different classes, suggesting a need for customization regarding pairing policies. [For the full proceedings, see ED615472.]
Early Prediction of Museum Visitor Engagement with Multimodal Adversarial Domain Adaptation
Author: Henderson, Nathan
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Museums,Audiences,Participation,Exhibits,Interaction,Learning Modalities,Multimedia Instruction,Computer Simulation,Preadolescents,Learner Engagement,Prediction,Educational Technology,Technology Uses in Education,Video Technology

Recent years have seen significant interest in multimodal frameworks for modeling learner engagement in educational settings. Multimodal frameworks hold particular promise for predicting visitor engagement in interactive science museum exhibits. Multimodal models often utilize video data to capture learner behavior, but video cameras are not always feasible, or even desirable, to use in museums. To address this issue while still harnessing the predictive capacities of multimodal models, we investigate adversarial discriminative domain adaptation for generating modality-invariant representations of both unimodal and multimodal data captured from museum visitors as they engage with interactive science museum exhibits. This approach enables the use of pre-trained multimodal visitor engagement models in circumstances where multimodal instrumentation is not available. We evaluate the visitor engagement models in terms of early prediction performance using exhibit interaction and facial expression data captured during visitor interactions with a science museum exhibit for environmental sustainability. Through the use of modality-invariant data representations generated by the adversarial discriminative domain adaptation framework, we find that pre-trained multimodal models achieve competitive predictive performance on interaction-only data compared to models evaluated using complete multimodal data. The multimodal framework outperforms unimodal and non-adapted baseline approaches during early intervals of exhibit interactions as well as entire interaction sequences. [For the full proceedings, see ED615472.]
Let Us Continue Learning: Lessons from Madagascar for Improving Access and Retention of Vulnerable Children in Secondary School. Innocenti Research Brief 2021-04
Author: Valenza, Marco
Date: 2021-04-00
Type: Reports - Descriptive
Subjects: Foreign Countries,Adolescents,Secondary School Students,Access to Education,Barriers,Low Income Groups,Educational Quality,At Risk Students,Academic Persistence,Out of School Youth,Numeracy,Literacy,Intervention,Financial Support,Remedial Instruction,Progress Monitoring,Reentry Students,Dropout Rate,Gender Differences,Grade Repetition,Program Effectiveness

Malagasy adolescents face severe challenges in accessing and completing basic education. Among those students who complete the primary cycle, one in four does not transition into lower secondary school. Economic constraints among vulnerable households coupled with low-quality education result in widespread dropout and poor learning outcomes. Acknowledging these multidimensional barriers, UNICEF Madagascar leveraged funds from the Let Us Learn (LUL) programme to implement a two-pronged strategy to support Malagasy children in accessing and continuing lower secondary school. The Catch-up Classes provide out-of-school adolescents with a learning pathway to build the foundational literacy and numeracy skills they need to resume studying in formal school. Conditional cash transfers target families with children who are at risk of abandoning school after completing the primary cycle. This brief builds on programme monitoring data, impact evaluations and qualitative insights from the field to highlight lessons learnt and actionable recommendations for accessing and continuing vulnerable children's secondary education.
Time to Teach: Teacher Attendance and Time on Task in Primary Schools in Mozambique
Author: Nugroho, Dita
Date: 2021-07-00
Type: Reports - Research
Subjects: Foreign Countries,Teacher Attendance,Developing Nations,Employee Absenteeism,Time Management,Elementary School Teachers,Teacher Attitudes,Teacher Persistence,Educational Policy,Educational Change,Influences,Teacher Characteristics,Rural Urban Differences,Sanctions,Teacher Education,Teaching Conditions,Gender Differences,Institutional Characteristics,Barriers

Teacher attendance is one of the prerequisites on the path toward universal learning in developing countries. Over the past decades, however, studies from across the developing world have found national rates of teacher absenteeism that range from 3 to 27 per cent. Therefore, enhancing teachers' presence in the classroom and ensuring that class time is spent teaching, can contribute significantly to the productivity and inclusive prosperity of a country. This Time to Teach study collates and strengthens the evidence base on primary school teacher absenteeism in Mozambique. The study uses a mix of qualitative and quantitative research methods to provide critical insights into the factors that underpin the multiple forms of teacher absenteeism and time on task. It also examines how factors vary across countries, school types, gender of teacher and other teacher characteristics. Despite high levels of teacher absenteeism, the study shows that teachers are generally committed and that what is needed is education system strengthening. It is hoped that findings will inform workable solutions and policies that will ensure a motivated teaching force, increase opportunities for children to learn at school and, ultimately, improve their life and work opportunities.
College after Prison: A Review of the Literature on Barriers and Supports to Postsecondary Education for Formerly Incarcerated College Students
Author: Donaldson, Vernisa M.
Date: 2021-10-00
Type: Information Analyses
Subjects: Higher Education,Institutionalized Persons,Correctional Institutions,Reentry Students,Student Needs,Access to Education,Employment Level,Educational Policy,Self Disclosure (Individuals),Foster Care,Barriers,Nontraditional Students,Adult Students,Military Personnel,Veterans Education,Females,Student Personnel Services,Peer Relationship,Mentors,Mental Health,Access to Health Care,At Risk Persons,Supervision,Trust (Psychology),Proprietary Schools,Correctional Education,Blended Learning

The white paper synthesizes existing literature around higher education for formerly incarcerated individuals and describes the need for more work in this area. More than 650,000 individuals are released from state and federal prisons and return to the community each year. However, most programming and research have been concentrated on correctional education. As a result, formerly incarcerated individuals are largely not recognized as a student group needing support, programming, and research in the higher education space. [Additional funding for this report from the Mayor's Office of Criminal Justice.]
Associations between Children&apos;s Media Use and Language and Literacy Skills
Author: Dore, Rebecca A.
Date: 2020-00-00
Type: Reports - Research
Subjects: Correlation,Language Acquisition,Literacy Education,Literacy,Family Environment,Preschool Children,Elementary School Students,Parent Attitudes,Longitudinal Studies,Age Differences,Measurement,Time Management,Parent Child Relationship,Story Reading,Video Technology,Television Viewing,Achievement Tests,Mothers,Educational Attainment,Predictive Validity,Computer Games,Telecommunications,Handheld Devices,Mass Media Use

Media use is a pervasive aspect of children's home experiences but is often not considered in studies of the home learning environment. Media use could be detrimental to children's language and literacy skills because it may displace other literacy-enhancing activities like shared reading and decrease the quantity and quality of caregiver-child interaction. Thus, the current study asked whether media use is associated with gains in children's language and literacy skills both at a single time point and across a school year and whether age moderates any association. Children (N = 1583) were from preschool through third grade classrooms and language and literacy skills were measured in the fall and spring of the school year. Parents reported how much time their child spends using media on a typical school day. Regression analyses showed that using 4 hours or more of media was related to lower literacy gains, but not to language gains. Multilevel models conducted as a robustness check showed that this effect did not hold when accounting for classroom. In neither set of models was there an interaction between age and media use. Single-time-point models did show some associations that did not manifest in more stringent models, highlighting the limitations of correlational designs that do not have measures of children's skills over time. Given the concern and popular press coverage around children's media use, it is important to acknowledge nonsignificant effects in this domain. These nonsignificant associations suggest that societal fears around children's media use may be exaggerated. Notably, however, characteristics of children's media use, like educational content or adult co-use, may moderate any effects. The relation between media use and language and literacy growth did not differ across the age range investigated suggesting that, within this range, younger children are not more vulnerable to detrimental effects. [This paper was published in &quot;Frontiers in Psychology&quot; v11 Article 1734 2020.]
Characteristics of Children&apos;s Media Use and Gains in Language and Literacy Skills
Author: Dore, Rebecca A.
Date: 2020-00-00
Type: Reports - Research
Subjects: Literacy,Language Acquisition,Parent Child Relationship,Computer Games,Computer Software,Handheld Devices,Grade 1,Elementary School Students,Correlation,Mass Media Use,Achievement Gains,Kindergarten,Parent Attitudes,Longitudinal Studies,Time Management,Prediction,Learning Activities,Language Skills,Emergent Literacy,Preschool Teachers,Elementary School Teachers,Achievement Tests

Media use could be detrimental to children's language and literacy skills because it may displace other language-enhancing activities like shared reading and caregiver-child interactions. Furthermore, the extent to which children use media with adults (joint media engagement), the extent to which they use interactive media (apps/games), and the time of the day and week during which media use occurs may attenuate any negative effects. The current study examines the relation between characteristics of children's media use and gains in first graders' language and literacy skills. Children (N = 488) completed direct assessments of language and literacy skills in the spring of kindergarten and the spring of first grade. Parents reported how many hours children used both interactive and noninteractive media during different times of the day on the most recent weekday and weekend day, and responded to items about the extent to which they engage with their children during media use. A quadratic relationship between media use and language gains showed that a moderate amount of media use was related to larger language gains, whereas high use was related to smaller gains. For literacy, an interaction between media use and joint media engagement showed a small negative effect of media use at low levels of joint media engagement and little to no relation between media use and literacy gains at higher levels of joint media engagement. Children's language and literacy skills were not predicted by either the proportion of media time that was spent with apps/games or morning and weekday media use. These results show that moderate amounts of media use may not be a negative influence on children's developing language skills whereas high levels may displace other language-enhancing activities. Additionally, joint media engagement may play an important buffering role in the relation between media use and early literacy skills, aligned with current recommendations encouraging co-viewing. [This paper was published in &quot;Frontiers in Developmental Psychology&quot; v11 Article 2224 2020.]