ethanporcaro/eric-fulltext-meta huggingface dataset:
From Detail to Context: Modeling Distributed Practice Intensity and Timing by Multiresolution Signal Analysis
Author: Chung, Cheng-Yu
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Models,Drills (Practice),Pacing,Computer Uses in Education,Student Behavior,Behavior Patterns,Time Management,Data Analysis,Pattern Recognition,Undergraduate Students,Grades (Scholastic),Multivariate Analysis

The distributed practice effect suggests that students retain learning content better when they pace their practice over time. The key factors are practice dosage (intensity) and timing (when to practice and how in between). Inspired by the thriving development of image recognition, this study adopts one of the successful techniques, multiresolution analysis (MRA), to model distributed and spaced practice (SP). We consider a sequence of practice sessions as a signal of the student's learning strategy. Then, we apply the stationary wavelet transform (SWT) to extract practice patterns spaced by three periods: small, medium, large. The result reveals a positive correlation between the small-spaced practice and the exam grade. The benchmark against baseline feature models shows that the SP patterns significantly improve the goodness-of-fit and complements the baseline models. This work successfully demonstrates 1) the use of MRA in modeling sequential patterns by event intensity and event timing; 2) the MRA approach can be used as an alternative method to improve existing student models of practice effort. [For the full proceedings, see ED615472.]
Generative Grading: Near Human-Level Accuracy for Automated Feedback on Richly Structured Problems
Author: Malik, Ali
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Grading,Accuracy,Computer Assisted Testing,Automation,Feedback (Response),Problem Solving,Assignments,Computation,Student Evaluation,Probability,Coding,Efficiency

Access to high-quality education at scale is limited by the difficulty of providing student feedback on open-ended assignments in structured domains like programming, graphics, and short response questions. This problem has proven to be exceptionally difficult: for humans, it requires large amounts of manual work, and for computers, until recently, achieving anything near human-level accuracy has been unattainable. In this paper, we present generative grading: a novel computational approach for providing feedback at scale that is capable of accurately grading student work and providing nuanced, interpretable feedback. Our approach uses generative descriptions of student cognition, written as probabilistic programs, to synthesise millions of labelled example solutions to a problem; we then learn to infer feedback for real student solutions based on this cognitive model. We apply our methods to three settings. In block-based coding, we achieve a 50% improvement upon the previous best results for feedback, exceeding human-level accuracy. In two other widely different domains--graphical tasks and short text answers--we achieve improvements over the previous state of the art by about 4x and 1.5x respectively, approaching human accuracy. In a real classroom, we ran an experiment with our system to augment human graders, yielding doubled grading accuracy while halving grading time. [For the full proceedings, see ED615472.]
Gaining Insights on Student Course Selection in Higher Education with Community Detection
Author: Sturludóttir, Erla Guðrún
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Course Selection (Students),Undergraduate Students,Engineering Education,Business Administration Education,Computer Science Education,Foreign Countries,Data Collection,Network Analysis

Gaining insight into course choices holds significant value for universities, especially those who aim for flexibility in their programs and wish to adapt quickly to changing demands of the job market. However, little emphasis has been put on utilizing the large amount of educational data to understand these course choices. Here, we use network analysis of the course selection of all students who enrolled in an undergraduate program in engineering, business or computer science at a Nordic university over a five year period. With these methods, we have explored student choices to identify their distinct fields of interest. This was done by applying community detection (CD) to a network of courses, where two courses were connected if a student had taken both. We compared our CD results to actual major specializations within the computer science department and found strong similarities. Analysis with our proposed methodology can be used to offer more tailored education, which in turn allows students to follow their interests and adapt to the ever-changing career market. [For the full proceedings, see ED615472.]
Going Online: A Simulated Student Approach for Evaluating Knowledge Tracing in the Context of Mastery Learning
Author: Zhang, Qiao
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Electronic Learning,Mastery Learning,Computer Simulation,Intelligent Tutoring Systems,Learning Analytics,Knowledge Level,Skill Development,Bayesian Statistics,Models,Problem Solving,Fractions,Arithmetic,Probability,Prediction

Knowledge tracing algorithms are embedded in Intelligent Tutoring Systems (ITS) to keep track of students' learning process. While knowledge tracing models have been extensively studied in offline settings, very little work has explored their use in online settings. This is primarily because conducting experiments to evaluate and select knowledge tracing models in classroom settings is expensive. To fill this gap, we introduce a novel way of using machinelearning models to generate simulated students. We conduct experiments using agents generated by the Apprentice Learner Architecture to investigate the online use of different knowledge tracing models (Bayesian Knowledge Tracing, the Streak model, and Deep Knowledge Tracing). An analysis of our simulation results revealed an error in the initial implementation of our Bayesian knowledge tracing model that was not identified in our previous work. Our simulations also revealed a more fundamental limitation of Deep Knowledge Tracing that prevents the model from supporting mastery learning on multi-step problems. Together, these two findings suggest that Apprentice agents provide a practical means of evaluating knowledge tracing models prior to more costly classroom testing. Lastly, our analysis identifies a positive correlation between the Bayesian knowledge tracing parameters estimated from human data and the parameters estimated from simulated learners. This suggests that model parameters might be initialized using simulated data when no human-student data is yet available. [For the full proceedings, see ED615472.]
Total Moral Quality: A New Approach for Character Education in &quot;Pesantren&quot;
Author: Baharun, Hasan
Date: 2017-06-00
Type: Journal Articles
Subjects: Values Education,Foreign Countries,Islamic Culture,Religious Education,Religious Schools,Islam,Boarding Schools,Moral Development,Student Development,Teaching Methods

This paper presents the concept of character education in &quot;pesantren&quot; which gives valuable contributions to the success of moral development for students. It also offers a different paradigm in developing the concept of character building in educational institution. This article is inspired by the lack of effective character learning in a variety of formal educational institutions. Hence, the schools which succeed in instilling character education can be used as a reference to develop character education. This study focuses on character education model developed by &quot;pesantren&quot; and offers an alternative perspective of the development of character education in Indonesia. This study adopts a qualitative research approach and uses a case study design. The study shows that the model of character education in &quot;pesantren&quot; is carried out through a multi-disciplinary approach so as to provide maximum results for the development of character education. This study suggests that the Total Moral Quality (TMQ) is the further development of Thomas Lickona's concept of character education of moral modeling, moral knowing, moral feeling and moral habituation and is applicable in the school.
The Rights of English Learner Students and Limited English Proficient Parents during the COVID-19 Pandemic
Author: Education Law Center
Date: 2021-04-15
Type: Reports - Descriptive
Subjects: English Language Learners,Limited English Speaking,Parent Background,COVID-19,Pandemics,Student Rights,Elementary Secondary Education,Distance Education,English (Second Language),Second Language Learning

Students have the right to be screened and identified for English Learner (EL) status during remote learning. Even if schools are operating by remote instruction, students, between the ages of 3 and 20, have the right to be screened and to be identified as an EL within 30 days of enrollment. This short fact sheet describes some of the basic rights of English Learner (EL) students and Limited English Proficient (LEP) parents in the State of New Jersey during the COVID-19 pandemic.
Strengthening Students&apos; Character in &quot;Akhlaq&quot; Subject through Problem Based Learning Model
Author: Baharun, Hasan
Date: 2018-00-00
Type: Journal Articles
Subjects: Values Education,Moral Values,Problem Based Learning,Emotional Intelligence,Critical Thinking,Thinking Skills,Skill Development,Moral Development,Religious Schools,Islam,Foreign Countries

This study examines the development of &quot;Akhlaq&quot; learning based on character strengthening through Problem Based Learning (PBL) model in Madrasah Aliyah Nurul Jadid, Paiton, Probolinggo. Character learning is the value engraved within a person that determines his attitude and behavior; this character can usually be shaped by education, experience, habituation, and environmental influences. &quot;Akhlaq&quot; education is an education that prioritizes the aspect of manners that always involves aspects of knowledge, feelings, and actions. Through this moral education, it is hoped to achieve a better character and can help to foster the emotional intelligence of learners in Madrasah Aliyah Nurul Jadid, Paiton, Probolinggo. The method used in this study is literary study located at Madrasah Aliyah Nurul Jadid, Paiton, Probolinggo. The results showed that &quot;Akhlaq&quot; learning through PBL approach can shape the students' thinking activities naturally and can solve the learning problem concerned with the learners' morality. So that learners can learn about how to think critically and can be skilled in solving problems.
The Transition to High School: Implications for Educational Success in High School. Policy Brief
Author: Benner, Aprile
Date: 2017-12-00
Type: Reports - Research
Subjects: Student Adjustment,High School Students,Academic Achievement,Middle School Students,Attendance Patterns,Grade 8,Grade 9,Student Characteristics,Institutional Characteristics

In the American K-12 educational system, school transitions are frequent and predictable--typically occurring when students enter kindergarten and when they move from elementary to middle school and again from middle to high school. Although much is known about the short- and long-term implications for the transition to formal K-12 schooling and the transition into middle school, how the transition to high school influences the immediate and long-term educational prospects of American students is relatively unknown and is the focus of the current study. Specifically, the author sought to determine whether the high school transition influences students' educational engagement (as assessed by attendance), which students are most at risk for experiencing transition difficulties as they move from 8th to 9th grade, in what types of schools are students doing best, and how success or challenges across the high school transition affects students' educational trajectories across high school and their early educational attainment. The findings suggest that it is not just 9th grade attendance, but it is the disruptions in attendance that students face as they negotiate physically moving from middle school to high school that can derail educational trajectories. Addressing Texas's ninth grade problem is important for ensuring the state increases its number of highly qualified high school graduates and that these high school graduates make successful transitions to college and the world of work, and the current study suggests that Texas schools and districts should place strong attention on enacting intervention strategies that better support students as they move from middle to high school.
Building Character Education Using Three &quot;Matra&quot; of Hasan Al-Banna&apos;s Perspective in &quot;Pesantren&quot;
Author: Baharun, Hasan
Date: 2018-12-00
Type: Journal Articles
Subjects: Values Education,Foreign Countries,Islamic Culture,Religious Education,Religious Schools,Islam,Educational Principles,Boarding Schools

This study aimed to describe the efforts of &quot;Pesantren&quot; Nurul Jadid to implement of Hasan al-Banna's thought about Three &quot;Matra&quot; of education, namely the education of mind, body, and heart (&quot;qalb&quot;), in constructing students' characters at &quot;Pesantren&quot; Nurul Jadid Paiton Probolinggo East Java. This research used a qualitative method of case study type. The results showed that; &quot;pesantren&quot; has implemented Three &quot;Matra&quot; by delivering education for mind, body, and heart for its &quot;santri.&quot; It has set the moto which is in line with Hasan al Banna's thought in building the character of students. It is supported by its vision and mission which was described in the trilogy of students and five/&quot;panca&quot; awareness of students, education and learning activity, habituation, making of regulation as the norm of binding and organizing activity students.
Automatic Assessment of the Design Quality of Python Programs with Personalized Feedback
Author: Orr, J. Walker
Date: 2021-00-00
Type: Reports - Research
Subjects: Programming Languages,Feedback (Response),Units of Study,Computer Science Education,Computer Software,Models,Networks,Evaluation Methods,Student Evaluation,Accuracy,Prediction,Scores,Comparative Analysis,Intelligent Tutoring Systems

The assessment of program functionality can generally be accomplished with straight-forward unit tests. However, assessing the design quality of a program is a much more difficult and nuanced problem. Design quality is an important consideration since it affects the readability and maintainability of programs. Assessing design quality and giving personalized feedback is very time consuming task for instructors and teaching assistants. This limits the scale of giving personalized feedback to small class settings. Further, design quality is nuanced and is difficult to concisely express as a set of rules. For these reasons, we propose a neural network model to both automatically assess the design of a program and provide personalized feedback to guide students on how to make corrections. The model's effectiveness is evaluated on a corpus of student programs written in Python. The model has an accuracy rate from 83.67% to 94.27%, depending on the dataset, when predicting design scores as compared to historical instructor assessment. Finally, we present a study where students tried to improve the design of their programs based on the personalized feedback produced by the model. Students who participated in the study improved their program design scores by 19.58%. [For the full proceedings, see ED615472.]
Context-Aware Knowledge Tracing Integrated with the Exercise Representation and Association in Mathematics
Author: Huang, Tao
Date: 2021-00-00
Type: Reports - Research
Subjects: Mathematics Instruction,Teaching Methods,Online Courses,Distance Education,COVID-19,Pandemics,Intelligent Tutoring Systems,Memory,Technical Support,Individualized Instruction,Mathematical Formulas,Correlation,Prediction,Accuracy,Instructional Effectiveness,Computer Software,Mathematics Achievement,Comparative Analysis,Natural Language Processing,Mathematics Tests,Elementary School Students,Foreign Countries

Influenced by COVID-19, online learning has become one of the most important forms of education in the world. In the era of intelligent education, knowledge tracing (KT) can provide excellent technical support for individualized teaching. For online learning, we come up with a new knowledge tracing method that integrates mathematical exercise representation and association of exercise (ERAKT). In the aspect of exercise representation, we represent the multi-dimensional features of the exercises, such as formula, text and associated concept, by using ontology replacement method, language model and embedding technology, so we can obtain the unified internal representation of exercise. Besides, we utilize the bidirectional long short memory neural network to acquire the association between exercises, so as to predict his performance in future exercise. Extensive experiments on a real dataset clearly proved the effectiveness of ERAKT method, they also verified that adding multi-dimensional features and exercise association can indeed improve the accuracy of prediction. [For the full proceedings, see ED615472.]
Deep-IRT with Independent Student and Item Networks
Author: Tsutsumi, Emiko
Date: 2021-00-00
Type: Reports - Research
Subjects: Item Response Theory,Prediction,Accuracy,Artificial Intelligence,Difficulty Level,Item Analysis,Networks,Learning Processes,Learning Analytics

Knowledge tracing (KT), the task of tracking the knowledge state of each student over time, has been assessed actively by artificial intelligence researchers. Recent reports have described that Deep-IRT, which combines Item Response Theory (IRT) with a deep learning model, provides superior performance. It can express the abilities of each student and the difficulty of each item such as IRT. However, its interpretability and applicability remain limited compared to those of IRT because the ability parameter depends on each item. Namely, the ability estimate for the same student and time might differ if the student attempts a different item. To overcome those difficulties, this study proposes a novel Deep-IRT model that models a student response to an item by two independent networks: a student network and an item network. Results of experiments demonstrate that the proposed method improves prediction accuracy and the interpretability of earlier KT methods. [For the full proceedings, see ED615472.]
Personal Narrative as a &apos;Breeding Ground&apos; for Higher-Order Thinking Talk in Early Parent-Child Interactions
Author: Frausel, Rebecca R.
Date: 2021-00-00
Type: Reports - Research
Subjects: Parent Child Relationship,Thinking Skills,Family Environment,Personal Narratives,Inferences,Skill Development,Child Development,Imagination,Speech Communication,Discourse Analysis,Age Differences,Comparative Analysis,Socialization,Language Acquisition,Longitudinal Studies,Video Technology,Infants,Preschool Children,Metacognition

Personal narrative is decontextualized talk where individuals recount stories of personal experiences about past or future events. As an everyday discursive speech type, narrative potentially invites parents and children to explicitly link together, generalize from, and make inferences about representations--i.e., to engage in higher-order thinking talk (HOTT). Here we ask whether narratives in early parent-child interactions include proportionally more HOTT than other forms of everyday home language. Sixty-four children (31 girls; 36 White, 14 Black, 8 Hispanic, 6 mixed/other race) and their primary caregiver(s) (M[subscript income] = $61,000) were recorded in 90-minute spontaneous home interactions every 4 months from 14-58 months. Speech was transcribed and coded for narrative and HOTT. We found that parents at all visits and children after 38 months used more HOTT in narrative than non-narrative, and more HOTT than expected by chance. At 38- and 50-months, we examined HOTT in a related but distinct form of decontextualized talk--pretend, or talk during imaginary episodes of interaction--as a control to test whether other forms of decontextualized talk also relate to HOTT. While pretend contained more HOTT than other (non-narrative/non-pretend) talk, it generally contained less HOTT than narrative. Additionally, unlike HOTT during narrative, the amount of HOTT during pretend did not exceed the amount expected by chance, suggesting narrative serves as a particularly rich 'breeding ground' for HOTT in parent-child interactions. These findings provide insight into the nature of narrative discourse, and suggest narrative potentially may be used as a lever to increase children's higher-order thinking. [The paper was published in &quot;Developmental Psychology&quot; v57 n4 p519-534 2021 (EJ1291864).]
Influences of Teacher-Child Relationships and Classroom Social Management on Child-Perceived Peer Social Experiences during Early School Years
Author: Chen, Jing
Date: 2020-00-00
Type: Reports - Research
Subjects: Teacher Student Relationship,Peer Influence,Peer Relationship,Preschool Children,Preschool Education,Primary Education,Elementary School Students,Correlation,Student Attitudes,Conflict,Behavior Modification,Student Behavior,Friendship,Reputation,Victims,Bullying,Social Support Groups,Classroom Environment,Teacher Influence,Interaction,Classroom Techniques

Interactions with teachers and peers are critical for children's social, behavioral, and academic development in the classroom context. However, these two types of interpersonal interactions in the classroom are usually pursued via separate lines of inquiries. The current study bridges these two areas of research to examine the way in which teachers influence child-perceived peer social support and peer victimization for 2,678 children within 183 classrooms in preschool through grade three. Two levels of teacher influence are considered, namely teacher-child closeness and conflict relationships at the child-level, and teacher management of interpersonal interactions at the classroom-level. Results of multilevel regression models showed that teacher-child closeness was associated with the growth of child-perceived peer social support from fall to spring, whereas teacher-child conflict and teachers' behavior management practices were associated with the change in child-perceived peer victimization across the academic year. These associations were unique and above and beyond the influence of children's actual peer social interactions, including reciprocal friendships and the collective classroom reputation of peer victimization. Collectively, findings highlight the multi-faceted teacher roles in shaping children's perceptions of their peer social experiences during the earliest years of schooling. [This paper was published in &quot;Frontiers in Psychology&quot; v11 Article 2746 2020.]