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Lasser, Jana; Manik, Debsankha; Silbersdorff, Alexander; Säfken, Benjamin; Kneib, Thomas – Teaching Statistics: An International Journal for Teachers, 2021
Data and its applications are increasingly ubiquitous in the rapidly digitizing world and consequently, students across different disciplines face increasing demand to develop skills to answer both academia's and businesses' increasing need to collect, manage, evaluate, apply and extract knowledge from data and critically reflect upon the derived…
Descriptors: Introductory Courses, Data, Interdisciplinary Approach, Programming Languages
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Shi, Yang; Chi, Min; Barnes, Tiffany; Price, Thomas W. – International Educational Data Mining Society, 2022
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…
Descriptors: Programming, Knowledge Level, Prediction, Instructional Innovation
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Rivera, Roberto; Marazzi, Mario; Torres-Saavedra, Pedro A. – Journal of Statistics Education, 2019
The 2016 Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report emphasized six recommendations to teach introductory courses in statistics. Among them: use of real data with context and purpose. Many educators have created databases consisting of multiple datasets for use in class; sometimes making hundreds of…
Descriptors: Introductory Courses, Statistics, Guidelines, Mathematics Instruction
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Saltan, Fatih – Journal of Education and Learning, 2017
Online Algorithm Visualization (OAV) is one of the recent developments in the instructional technology field that aims to help students handle difficulties faced when they begin to learn programming. This study aims to investigate the effect of online algorithm visualization on students' achievement in the introduction to programming course. To…
Descriptors: Information Technology, Control Groups, Experimental Groups, Programming