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Jiang, Bo; Wu, Simin; Yin, Chengjiu; Zhang, Haifeng – IEEE Transactions on Learning Technologies, 2020
Accurately tracing the state of learner knowledge contributes to providing high-quality intelligent support for computer-supported programming learning. However, knowledge tracing is difficult when learners have only had a few practice opportunities, which is often common in block-based programming. This article proposed two knowledge tracing…
Descriptors: Programming, Computer Assisted Instruction, Problem Solving, Task Analysis
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Chen, Fu; Cui, Ying – Journal of Learning Analytics, 2020
Predictive analytics in higher education has become increasingly popular in recent years with the growing availability of educational big data. Particularly, a wealth of student activity data is available from learning management systems (LMSs) in most academic institutions. However, previous investigations into predictive analytics in higher…
Descriptors: Time on Task, Student Behavior, Integrated Learning Systems, Grade Prediction
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Kane, Michael T.; Mroch, Andrew A. – ETS Research Report Series, 2020
Ordinary least squares (OLS) regression and orthogonal regression (OR) address different questions and make different assumptions about errors. The OLS regression of Y on X yields predictions of a dependent variable (Y) contingent on an independent variable (X) and minimizes the sum of squared errors of prediction. It assumes that the independent…
Descriptors: Regression (Statistics), Least Squares Statistics, Test Bias, Error of Measurement
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Harris, Frank – School Science Review, 2020
The year 2020 saw the outbreak of the COVID-19 pandemic that not only had wide-reaching social and economic consequences but also put healthcare systems under stress. Strategies for coping with the virus depended heavily on the interpretation of data. This article uses information from a UK upper tier local authority to examine how closely the…
Descriptors: Pandemics, COVID-19, Data Interpretation, Prediction
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Yousafzai, Bashir Khan; Hayat, Maqsood; Afzal, Sher – Education and Information Technologies, 2020
The presented work is a student marks and grade prediction system using supervised machine learning techniques, the system is developed on the historic performance of students. The data used in this research is collected from Federal Board of Intermediate and Secondary Education Islamabad Pakistan, there are 7 regions in FBISE i.e. Punjab, Sindh,…
Descriptors: Artificial Intelligence, Foreign Countries, Prediction, Grades (Scholastic)
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Cheng, Bonnie B. Y.; Worrall, Linda E.; Copland, David A.; Wallace, Sarah J. – International Journal of Language & Communication Disorders, 2020
Background: Prognostication is a complex clinical task that involves forming a prediction about recovery and communicating prognostic information to patients and families. In aphasia, recovery is difficult to predict and evidence-based guidance on prognosis delivery is lacking. Questions about aphasia prognosis commonly arise, but it is unknown…
Descriptors: Prediction, Aphasia, Speech Language Pathology, Allied Health Personnel
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Liu, Ruitao; Tan, Aixin – Journal of Educational Data Mining, 2020
In this paper, we describe our solution to predict student STEM career choices during the 2017 ASSISTments Datamining Competition. We built a machine learning system that automatically reformats the data set, generates new features and prunes redundant ones, and performs model and feature selection. We designed the system to automatically find a…
Descriptors: Career Choice, Prediction, Automation, Artificial Intelligence
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Op den Kamp, Emma M.; Bakker, Arnold B.; Tims, Maria; Demerouti, Evangelia – Journal of Creative Behavior, 2020
Integrating proactivity and creativity literatures, we argue that people can perform more creatively at work when they proactively manage their levels of vitality. Proactive vitality management is defined as individual, goal-oriented behavior aimed at managing physical and mental energy to promote optimal functioning at work. We hypothesize that…
Descriptors: Creativity, Social Support Groups, Goal Orientation, Metacognition
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Bower, Corinne; Odean, Rosalie; Verdine, Brian N.; Medford, Jelani R.; Marzouk, Maya; Golinkoff, Roberta Michnick; Hirsh-Pasek, Kathy – Journal of Cognition and Development, 2020
Block-building skills at age 3 are related to spatial skills at age 5 and spatial skills in grade school are linked to later success in science, technology, engineering, and mathematics (STEM) fields. Though studies have focused on block-building behaviors and design complexity, few have examined these variables in relation to future spatial and…
Descriptors: Preschool Children, Difficulty Level, Spatial Ability, Mathematics Skills
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Alonzo, Crystle N.; McIlraith, Autumn L.; Catts, Hugh W.; Hogan, Tiffany P. – Journal of Speech, Language, and Hearing Research, 2020
Purpose: In this study, we examine how well kindergarten letter identification and phonological awareness predict 2nd grade word reading and dyslexia in children with developmental language disorder (DLD) and their age- and grade-matched peers with typical language (TL). Method: We employ (a) logistic regression to determine how letter…
Descriptors: Prediction, Dyslexia, Children, Kindergarten
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Mohan, Kaushik; Bergner, Yoav; Halpin, Peter – Technology, Knowledge and Learning, 2020
Technology-based assessments that involve collaboration among students offer many sources of process data, although it remains unclear which aspects of these data are most meaningful for making inferences about students' collaborative skills. Recent research has focused mainly on theory-based rubrics for qualitative coding of process data (e.g.,…
Descriptors: Computer Assisted Testing, Student Evaluation, Cooperation, Grade 12
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Lemay, David John; Doleck, Tenzin – Education and Information Technologies, 2020
Massive open online courses (MOOCs) hold the promise of democratizing the learning process. However, providing effective feedback has proven hard to offer at scale since most methods require a teacher or tutor. Leveraging big data in MOOCs offers a mechanism to develop predictive models that can inform computer-based pedagogical tutors. We review…
Descriptors: Grades (Scholastic), Prediction, Online Courses, Video Technology
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Sutter, Claudia C.; Campbell, Laurie O.; Lambie, Glenn W. – Computers in the Schools, 2020
The present study investigated the predictability of a computer-adaptive, curriculum-based reading assessment for measuring second-grade students' overall and comprehension reading achievement on a standardized reading achievement test. Specifically, second-grade student scores (N = 428) of the Istation's Indicators of Progress for Early Reading…
Descriptors: Prediction, Standardized Tests, Reading Tests, Grade 2
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Varsamis, Panagiotis; Gkouvatzi, Anastasia; Nanou, Andromachi; Ntarilli, Ioanna; Simeonidou, Magdalini – Journal of the International Association of Special Education, 2020
The purpose of the present study is to examine the properties of a curriculum-based pre-referral screening tool. According to the current Greek preschool curriculum, preschoolers' participation is evaluated across five core axes, namely Play, Routines, Daily Activities, Explorations, and Organized Activities. Teacher evaluations sorted 201…
Descriptors: Preschool Curriculum, Screening Tests, Foreign Countries, Prereferral Intervention
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Ma, Boxuan; Taniguchi, Yuta; Konomi, Shin'ichi – International Educational Data Mining Society, 2020
Recommending courses to students is a fundamental and also challenging issue in the traditional university environment. Not exactly like course recommendation in MOOCs, the selection and recommendation for higher education is a non-trivial task as it depends on many factors that students need to consider. Although many studies on this topic have…
Descriptors: Course Selection (Students), College Students, Online Courses, Student Attitudes
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