ethanporcaro/eric-fulltext-meta huggingface dataset:
Exploring the Importance of Factors Contributing to Dropouts in Higher Education over Time
Author: Tanvir, Hasan
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Dropouts,College Students,Predictor Variables,Socioeconomic Status,Student Characteristics,Academic Achievement,Student Motivation,Multivariate Analysis,Foreign Countries

The aim of this work is to provide data-driven insights regarding the factors behind dropouts in Higher Education and their impact over time. To this end, we analyzed students' data collected by a Higher Education Institute over the last 11 years and we explored how socio-economic and academic changes may have impacted student dropouts and how these changes may have been reflected or captured by students' data. To analyze the data, we engineered features that may predict student dropouts on three dimensions: academic background, students' performance and students' effort. Then we carried out a correlation analysis to investigate the potential relationship between these features and dropouts, we performed a multivariate analysis of variance (MANOVA) to investigate whether the engineered features change significantly among student cohorts with different admission year and, finally, we carried out a regression analysis to confirm that the engineered features' impact on predicting dropouts changes over the years. The results suggest that the importance of features regarding the academic background of students (such as the students' prior experience with the academic institution), and the effort students make (for example, the number of days students spend on academic leave) may change over time. On the contrary, performance-based features (such as credit points and grades) do interact with time suggesting that performance measures are stable predictors of dropouts over time. On the basis of the findings, we argue that the performance of prediction models for assessing students at risk of dropping out of their studies can be affected by the age of data and we outline the possibility of including a forgetting factor for non-recent data in order to leverage their impact on prediction performance. [For the full proceedings, see ED615472.]
Finding the Optimal Topic Sequence for Online Courses Using SERPs as a Proxy
Author: Rüdian, Sylvio
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,Sequential Approach,Educational Technology,Computer Uses in Education,Search Engines,Automation,Readability,Artificial Intelligence

Finding the optimal topic sequence of online courses requires experts with lots of knowledge about taught topics. Having a good order is necessary for a good learning experience. By using educational recommender systems across different platforms we have the problem that the connection to an ontology sometimes does not exist. Thus, the state of the art recommenders can suggest courses with an optimal order within a platform. But on a more global view, a recommendation across different platforms with optimal order is not existing as long as no ontology was defined or courses are not connected to an existing ontology. Nowadays experimental approaches manipulate the learning paths to find the optimum. As this can impact the learning experience of participants, this approach is ethically unacceptable. To overcome this problem, we propose a data-driven approach using the search engine result pages (SERPs) of Google. In our experiment, we used pair-wise search queries to get access to web pages, those 38.000 texts were used to test some NLP metrics. 10 different metrics were examined to create an optimal order that was compared to the optimal sequence defined by experts. We observed that the Gunning Fog Index is a good estimator to determine the optimal order within a cluster of topics. [For the full proceedings, see ED615472.]
Tests Screening Writing Difficulty in Malayalam among Upper Primary Students
Author: Gafoor, K. Abdul
Date: 2014-00-00
Type: Journal Articles
Subjects: Screening Tests,Writing Difficulties,Foreign Countries,Elementary School Students,Ability Identification,Regression (Statistics),Alphabets,Phonology,Morphology (Languages),Spelling,Dravidian Languages

Design of a screening test for identifying students with writing difficulty in Malayalam and validation thereof among upper primary students of Kerala is made to help schools to understand factors leading to such difficulty and to intervene with such students. A battery of tests was developed based on extant literature on screening tests, reviewed difficulties in writing Malayalam, and discrimination power of the draft tests. 135 students identified as writing difficult and 476 students normal on writing in grade 5 to 7 were identified with clear criteria. Scored by cut-points, the battery of tests comprising tasks on ordering words by vowels or consonants, spelling (identification), choosing signs and glides, combining words correctly (phonology), vocabulary (picture), using appropriate one word (morphology), dictation and story development significantly screen upper primary writing difficult students with around 90percent sensitivity and specificity.
Strengthening Intensive Intervention Preparation: A Guide for Teacher Preparation Faculty
Author: Hayes, Lindsey
Date: 2021-00-00
Type: Guides - Classroom - Teacher
Subjects: Intervention,Guides,Special Education Teachers,Special Needs Students,Teacher Education Programs,Teacher Educators,Educational Resources,Preservice Teachers,Special Education,Introductory Courses,Practicums,Methods Courses,Student Evaluation,Evaluation Methods,Teacher Workshops,Program Descriptions,Learning Disabilities

Special education teachers must have the skills to design and deliver intensive interventions for students with severe and persistent learning and behavioral needs. To ensure effective instruction for these students, preservice preparation programs must provide their teacher candidates with opportunities to learn, apply, and practice intensive intervention skills. Teacher preparation faculty play a critical role in ensuring the next generation of teachers have these opportunities. This guide explains how teacher preparation faculty can incorporate resources from the National Center on Intensive Intervention (NCII) into preservice teacher preparation coursework and clinical experiences. The guide is organized by four types of courses that teacher candidates are likely to experience during the preparation program: introductory courses, methods courses, assessment courses, and clinical or practicum courses. For each type of course, the guide lists common learning objectives for preservice teachers. Next, the guide identifies applicable NCII resources to address the learning objectives and offers suggestions for how faculty or candidates can use the resources in the course. The guide also includes program spotlights that feature examples of how faculty who participated in NCII's faculty summer workshop have integrated NCII resources into their courses.
Knowledge Transfer by Discriminative Pre-Training for Academic Performance Prediction
Author: Kim, Byungsoo
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Academic Achievement,Intelligent Tutoring Systems,Prediction,Scores,Natural Language Processing,Transfer of Training,Models,Learning Analytics,Computer Software,Handheld Devices,Telecommunications,Foreign Countries,Independent Study,Second Language Learning,Second Language Instruction,English (Second Language),Language Tests

The needs for precisely estimating a student's academic performance have been emphasized with an increasing amount of attention paid to Intelligent Tutoring System (ITS). However, since labels for academic performance, such as test scores, are collected from outside of ITS, obtaining the labels is costly, leading to label-scarcity problem which brings challenge in taking machine learning approaches for academic performance prediction. To this end, inspired by the recent advancement of pre-training method in natural language processing community, we propose DPA, a transfer learning framework with Discriminative Pre-training tasks for Academic performance prediction. DPA pre-trains two models, a generator and a discriminator, and fine-tunes the discriminator on academic performance prediction. In DPA's pre-training phase, a sequence of interactions where some tokens are masked is provided to the generator which is trained to reconstruct the original sequence. Then, the discriminator takes an interaction sequence where the masked tokens are replaced by the generator's outputs, and is trained to predict the originalities of all tokens in the sequence. We conduct extensive experimental studies on a real-world dataset obtained from a multi-platform ITS application and show that DPA outperforms the previous state-of-the-art generative pre-training method with a reduction of 4.05% in mean absolute error and more robust to increased label-scarcity. [For the full proceedings, see ED615472.]
Practical Guide to Blended/Remote Learning and Children with Disabilities
Author: United Nations Children's Fund (UNICEF)
Date: 2021-05-00
Type: Guides - General
Subjects: Blended Learning,Students with Disabilities,COVID-19,Pandemics,School Closing,Distance Education,At Risk Students,Access to Education,Inclusion,Student Needs,Leadership Responsibility,Teacher Role,Individualized Education Programs,Educational Technology,Technology Uses in Education,Professional Development,Technological Literacy,Family School Relationship,Academic Accommodations (Disabilities),Teaching Methods,Planning

The &quot;Practical Guide to Blended/Remote Learning and Children with Disabilities&quot; was created by the United Nations Children's Fund (UNICEF) in response to the situation created by the coronavirus disease 2019 (COVID-19) pandemic in 2020. However, it is applicable to other circumstances when blended or remote learning might be necessary, including during emergencies. The guide aims to support school directors, teachers and other professionals in making decisions that ensure children from vulnerable populations -- particularly children with disabilities -- continue to receive education and associated services even when schools are physically closed. The sections in the guide discuss: (1) The assumptions about inclusive education that are at the core of this guide; (2) What should happen before remote or blended education is implemented; (3) The basic concepts behind remote or blended education; (4) What education and school leaders/decision-makers should know and do in the event that remote or blended education becomes necessary; (5) What a teacher or service provider should know and do if their school is implementing remote or blended learning; and (6) Suggestions on how to create more inclusive and equitable school systems upon return to school.
Learning from Non-Assessed Resources: Deep Multi-Type Knowledge Tracing
Author: Wang, Chunpai
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Models,Knowledge Level,Artificial Intelligence,Computer Uses in Education,Learning Activities,Prediction,Electronic Learning,Academic Achievement

The state of the art knowledge tracing approaches mostly model student knowledge using their performance in assessed learning resource types, such as quizzes, assignments, and exercises, and ignore the non-assessed learning resources. However, many student activities are non-assessed, such as watching video lectures, participating in a discussion forum, and reading a section of a textbook, all of which potentially contributing to the students' knowledge growth. In this paper, we propose the first novel deep learning based knowledge tracing model (DMKT) that explicitly model student's knowledge transitions over both assessed and non-assessed learning activities. With DMKT we can discover the underlying latent concepts of each non-assessed and assessed learning material and better predict the student performance in future assessed learning resources. We compare our propose method with various state of the art knowledge tracing methods on four real-world datasets and show its effectiveness in predicting student performance, representing student knowledge, and discovering the underlying domain model. [For the full proceedings, see ED615472.]
Quizzing Policy Using Reinforcement Learning for Inferring the Student Knowledge State
Author: He-Yueya, Joy
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Tests,Educational Policy,Reinforcement,Teaching Methods,Online Courses,Individualized Instruction,Instructional Effectiveness,Knowledge Level,Heuristics

The prevalence of online education systems provides opportunities to deliver personalized learning at scale. Educational systems need to assess students so that they can provide better curricula tailored to each student's unique needs. Since there is a limited amount of time for quizzing a student, we need to test each student using those questions that capture the most information about their level of understanding of various concepts. In this paper, we formally pose the problem and present multiple approaches for learning a quizzing policy to determine a personalized sequence of questions for each student that best predicts their knowledge state. We first introduce simple heuristics including random selection and an uncertainty sampling approach inspired by an active learning framework. We then develop a reinforcement learning (RL) approach for designing a quizzing policy. Using simulations of students' knowledge states, we provide initial evidence that an RL-based approach can improve over simple heuristics. We further demonstrate the effectiveness of our approaches using a real-world dataset consisting of over 1.5 million examples of students' answers to mathematics questions from Eedi, an online educational platform. [For the full proceedings, see ED615472.]
More with Less: Exploring How to Use Deep Learning Effectively through Semi-Supervised Learning for Automatic Bug Detection in Student Code
Author: Shi, Yang
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Artificial Intelligence,Program Effectiveness,Coding,Computer Science Education,Identification,Error Correction

Automatically detecting bugs in student program code is critical to enable formative feedback to help students pinpoint errors and resolve them. Deep learning models especially code2vec and ASTNN have shown great success for &quot;large-scale&quot; code classification. It is not clear, however, whether they can be effectively used for bug detection when the amount of labeled data is limited. In this work, we investigated the effectiveness of code2vec and ASTNN against classic machine learning models by varying the amount of labeled data from 1% up to 100%. With a few exceptions, the two deep learning models outperform the classic models. More interestingly, our results showed that when the amount of labeled data is small, code2vec is more effective, while ASTNN is more effective with more training data; for both code2vec and ASTNN, the more labeled data, the better. To further improve their effectiveness, we investigated the potential of semi-supervised learning which can leverage a large amount of unlabeled data to improve their performance. Our results showed that semi-supervised learning is indeed beneficial especially for ASTNN. [For the full proceedings, see ED615472.]
Predicting Student Performance Using Teacher Observation Reports
Author: Fateen, Menna
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Grade Prediction,Academic Achievement,Observation,Middle School Students,Artificial Intelligence,Foreign Countries,Tutorial Programs

Studying for entrance examinations can be a distressing period for numerous students. Consequently, many students decide to attend cram schools to assist them in preparing for these exams. For such schools and for all educational institutes, it is necessary to obtain the best tools to provide the highest quality of learning and guidance. Performance prediction is one tool that can serve as a resource for insights that are valuable to all educational stakeholders. With accurate predictions of their grades, students can be further guided and fostered in order to achieve their optimal learning goals. In this regard, we target middle school students to be able to guide them on their educational journey as early as possible. We propose a method to predict the students' performance in entrance examinations using the comments that cram school teachers made throughout the lessons. Teachers in cram schools observe their student's behavior closely and give reports on the efforts taken in their subject material. We show that the teachers' comments are qualified to construct a tool that is capable of predicting students' grades efficiently. This is a new method because previous studies focus on predicting grades mainly using student data such as their reflection comments or earlier scores. Experimental results show that using readily available feedback from teachers can remarkably contribute to the accuracy of student performance prediction. [For the full proceedings, see ED615472.]
Minnesota&apos;s One-Child School Readiness Dividend: School Readiness in Minnesota Is Worth $56,000 per Low-Income Child
Author: Diaz, Jose
Date: 2012-07-00
Type: Reports - Research
Subjects: Preschool Education,Outcomes of Education,Effect Size,High School Graduates,Graduation Rate,Crime,Incidence,Expenditures,Costs,Special Education,Employment,Taxes,Low Income Students,At Risk Students,Economic Impact,Income,Institutionalized Persons,Correctional Institutions,School Readiness,Students with Disabilities,Elementary Secondary Education,Grade Repetition,Welfare Services,Victims of Crime,Substance Abuse

Numerous research studies have demonstrated the economic value of investing in effective early education and intervention programs that cognitively, socially, and emotionally prepare low-income children at risk of school failure for success in school. School readiness prevents or reduces needless public spending, starting with K-12 special education and lasting through adulthood in the social welfare and criminal justice systems. Without early education and support for healthy development, an at-risk child is more likely to start school at a disadvantage and ultimately more likely to drop out of school, earn lower wages, depend on public assistance, or commit crimes. Conversely, those who start school healthy and prepared are more likely, as adults, to be employed, earn higher incomes, and contribute more in taxes. This brief describes the lifetime economic benefit to the state of Minnesota per child of both targeted early learning investments and universal Pre-K.
Prevalence and Outcomes of Workplace-Based Delivery in VET. Research Report
Author: Osborne, Kristen
Date: 2021-00-00
Type: Reports - Research
Subjects: Incidence,Vocational Education,Foreign Countries,Work Experience Programs,Program Effectiveness,Delivery Systems,Employment Level,Intellectual Disciplines,Workplace Learning,Academic Degrees,Educational Certificates,Job Training,Student Characteristics,Student Attitudes,Predictor Variables,Majors (Students)

Extensive evidence demonstrates a trend of positive outcomes related to learning in the workplace. Despite this, no examination of the outcomes has been undertaken using the available Australian vocational education and training (VET) data. This publication uses VET administrative data to examine trends in the delivery of workplace-based training, including where entire programs are delivered in this way. Additionally, data from the National Student Outcomes Survey are used to model the effect on employment and achievement outcomes of a student receiving workplace-based delivery. As previous work has extensively examined the role of workplace learning in apprenticeships and traineeships, this publication does not include these students. The relationship between mode of delivery and factors such as the field of education studied complicates efforts to understand the effects of workplace-based delivery. As a likely consequence of this, the analysis of the impact of workplace-based delivery using administrative data did not identify a material impact on student outcomes. A more conclusive understanding of the extent of the impact exerted by workplace-based delivery on student outcomes may only be possible through a randomised trial, whereby the only difference in the program is the presence of workplace-based delivery.
Minnesota&apos;s One-Child School Readiness Dividend: School Readiness in Minnesota Is Worth about $43,000 per Low-Income Child Using Targeted Early Education Investments and Nearly $13,000 per Child in Universal Pre-K
Author: Diaz, Jose
Date: 2016-02-00
Type: Reports - Research
Subjects: Preschool Education,Outcomes of Education,Effect Size,High School Graduates,Graduation Rate,Crime,Incidence,Expenditures,Costs,Special Education,Health Services,Employment,Taxes,Low Income Students,At Risk Students,Economic Impact,Income,Institutionalized Persons,Correctional Institutions,School Readiness,Students with Disabilities,Elementary Secondary Education,Grade Repetition,Welfare Services,Victims of Crime,Substance Abuse,Child Health

Numerous research studies have demonstrated the economic value of investing in effective early education programs that cognitively, socially, and emotionally prepare low-income children at risk of school failure for success in school. School readiness prevents or reduces needless public spending, starting with K-12 special education and lasting through adulthood in the social welfare, health care, and criminal justice systems. Fewer studies have calculated the benefits associated with investing in universal preschool programs that serve all children. To calculate the present value of lifetime benefits, the researchers used effect sizes of early education programs in published studies, Minnesota Census data and incidence rates (e.g. high school graduation rates, crime rates), and actual expenditure data from state agencies on special education, health care, crime, and other areas and gains due to employment and tax returns. This brief describes the lifetime economic benefit to the state of Minnesota per child of both targeted early learning investments and universal Pre-K. This is a 2016 update of a 2012 brief. [For the 2012 brief, &quot;Minnesota's One-Child School Readiness Dividend: School Readiness in Minnesota Is Worth $56,000 per Low-Income Child,&quot; see ED615588.]