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Espinal, Alejandro; Vieira, Camilo; Guerrero-Bequis, Valeria – Computer Science Education, 2023
Background and context: Transfer is a process where students apply their learning to different contexts. This process includes using their knowledge to solve problems with similar complexity, and in new contexts. In the context of programming, transfer also includes being able to understand and use different programming languages. Objective: This…
Descriptors: Block Scheduling, Computer Science Education, Programming Languages, Coding
Neelima Bhatnagar; Victoria Causer; Michael J. Lucci; Michael Pry; Dorothy M. Zilic – Information Systems Education Journal, 2024
Data analytics is a rapidly growing field that plays a crucial role in extracting valuable insights from large volumes of data. A data analytics practicum course provides students with hands-on experience in applying data analytics techniques and tools to real-world scenarios. This practicum is intended to serve as a bridge between the student's…
Descriptors: Statistics Education, Data Analysis, Practicums, Education Work Relationship
Whitney, Michael; Lipford, Heather Richter; Chu, Bill; Thomas, Tyler – Journal of Educational Computing Research, 2018
Many of the software security vulnerabilities that people face today can be remediated through secure coding practices. A critical step toward the practice of secure coding is ensuring that our computing students are educated on these practices. We argue that secure coding education needs to be included across a computing curriculum. We are…
Descriptors: Computer Security, Programming, Coding, Computer Science Education
Johnson, Marina E.; Misra, Ram; Berenson, Mark – Decision Sciences Journal of Innovative Education, 2022
In the era of artificial intelligence (AI), big data (BD), and digital transformation (DT), analytics students should gain the ability to solve business problems by integrating various methods. This teaching brief illustrates how two such methods--Bayesian analysis and Markov chains--can be combined to enhance student learning using the Analytics…
Descriptors: Bayesian Statistics, Programming Languages, Artificial Intelligence, Data Analysis
Jia, Jiyou; He, Yunfan – Interactive Technology and Smart Education, 2022
Purpose: The purpose of this study is to design and implement an intelligent online proctoring system (IOPS) by using the advantage of artificial intelligence technology in order to monitor the online exam, which is urgently needed in online learning settings worldwide. As a pilot application, the authors used this system in an authentic…
Descriptors: Artificial Intelligence, Supervision, Computer Assisted Testing, Electronic Learning
Sentiment and Sentence Similarity as Predictors of Integrated and Independent L2 Writing Performance
Uzun, Kutay; Ulum, Ömer Gökhan – Acuity: Journal of English Language Pedagogy, Literature and Culture, 2022
This study aimed to utilize sentiment and sentence similarity analyses, two Natural Language Processing techniques, to see if and how well they could predict L2 Writing Performance in integrated and independent task conditions. The data sources were an integrated L2 writing corpus of 185 literary analysis essays and an independent L2 writing…
Descriptors: Natural Language Processing, Second Language Learning, Second Language Instruction, Writing (Composition)
García, José Manuel García-Vandewalle; García-Carmona, Marina; Trujillo Torres, Juan Manuel; Moya-Fernández, Pablo – Contemporary Educational Technology, 2022
Digital competence has become a new type of literacy that significantly impacts on people's lives, as it is paramount to their social and occupational integration. In educational institutions, teachers play a major role in developing the digital skills of their students. This study aims to determine the key elements of teacher training in the use…
Descriptors: Teacher Education Programs, Educational Change, Technological Literacy, Pedagogical Content Knowledge
Picones, Gio; PaaBen, Benjamin; Koprinska, Irena; Yacef, Kalina – International Educational Data Mining Society, 2022
In this paper, we propose a novel approach to combine domain modelling and student modelling techniques in a single, automated pipeline which does not require expert knowledge and can be used to predict future student performance. Domain modelling techniques map questions to concepts and student modelling techniques generate a mastery score for a…
Descriptors: Prediction, Academic Achievement, Learning Analytics, Concept Mapping
Strawhacker, Amanda; Bers, Marina Umaschi – Educational Technology Research and Development, 2019
Computer programming for young children has grown in popularity among both educators and product developers, but still relatively little is known about what skills children are developing when they code. This study investigated N = 57 Kindergarten through second grade children's performance on a programming assessment after engaging in a 6-week…
Descriptors: Coding, Programming, Computer Science Education, Kindergarten
Ipek, Jale – Journal of Education and Learning, 2021
This study aimed to examine the preservice teachers' views on the process after entering Code.org and block-based programming (Scratch) training programs, which are carried out by the peer learning method. The study group of the research consists of 41 preservice teachers at the Computer Education and Instructional Technologies departments of a…
Descriptors: Peer Teaching, Preservice Teachers, Student Satisfaction, Computer Science Education
Wang, Sabrina Luxin; Zhang, Anna Yinqi; Messer, Samuel; Wiesner, Andrew; Pearl, Dennis K. – Journal of Statistics and Data Science Education, 2021
This article describes a suite of student-created Shiny apps for teaching statistics and a field test of their short-term effectiveness. To date, more than 50 Shiny apps and a growing collection of associated lesson plans, designed to enrich the teaching of both introductory and upper division statistics courses, have been developed. The apps are…
Descriptors: Student Centered Learning, Teaching Methods, Statistics Education, Introductory Courses
Myers, Matthew C.; Wilson, Joshua – International Journal of Artificial Intelligence in Education, 2023
This study evaluated the construct validity of six scoring traits of an automated writing evaluation (AWE) system called "MI Write." Persuasive essays (N = 100) written by students in grades 7 and 8 were randomized at the sentence-level using a script written with Python's NLTK module. Each persuasive essay was randomized 30 times (n =…
Descriptors: Construct Validity, Automation, Writing Evaluation, Algorithms
Silvia García-Méndez; Francisco de Arriba-Pérez; Francisco J. González-Castaño – International Association for Development of the Information Society, 2023
Mobile learning or mLearning has become an essential tool in many fields in this digital era, among the ones educational training deserves special attention, that is, applied to both basic and higher education towards active, flexible, effective high-quality and continuous learning. However, despite the advances in Natural Language Processing…
Descriptors: Higher Education, Artificial Intelligence, Computer Software, Usability
Zhanxia Yang; Patricia Moore Shaffer; Courtney Hagan; Parastu Dubash; Marina Bers – Grantee Submission, 2023
The aim of this study was to explore how the Coding as Another Language (CAL) curriculum, developed by Boston College's DevTech Research Group and utilizing the ScratchJr app, impacted students' computational thinking, coding skills, and reading comprehension. To accomplish this, the research team randomly assigned thirteen schools in a…
Descriptors: Coding, Second Language Learning, Curriculum Development, Programming Languages
Qian, Yizhou – ProQuest LLC, 2018
With the expansion of computer science (CS) education, CS teachers in K-12 schools should be cognizant of student misconceptions and be prepared to help students establish accurate understanding of computer science and programming. This exploratory design-based research (DBR) study implemented a data-driven approach to identify secondary school…
Descriptors: Misconceptions, Data, Decision Making, Computer Science Education

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