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Showing 1 to 15 of 21 results Save | Export
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Ronit Shmallo; Adi Katz – Computer Science Education, 2024
Background and Context: Gender research shows that women are better at reading comprehension. Other studies indicate a lower tendency in women to choose STEM professions. Since data modeling requires reading skills and also belongs in the areas of information systems and computer science (STEM professions), these findings provoked our curiosity.…
Descriptors: Gender Differences, Transfer of Training, Databases, Models
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Miedema, Daphne; Fletcher, George; Aivaloglou, Efthimia – ACM Transactions on Computing Education, 2023
Prior studies in the Computer Science education literature have illustrated that novices make many mistakes in composing SQL queries. Query formulation proves to be difficult for students. Only recently, some headway was made towards understanding why SQL leads to so many mistakes, by uncovering student misconceptions. In this article, we shed new…
Descriptors: Computer Science Education, Novices, Misconceptions, Programming Languages
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Growns, Bethany; Towler, Alice; Dunn, James D.; Salerno, Jessica M.; Schweitzer, N. J.; Dror, Itiel E. – Cognitive Research: Principles and Implications, 2022
Forensic science practitioners compare visual evidence samples (e.g. fingerprints) and decide if they originate from the same person or different people (i.e. fingerprint 'matching'). These tasks are perceptually and cognitively complex--even practising professionals can make errors--and what limited research exists suggests that existing…
Descriptors: Crime, Evidence, Sampling, Statistics Education
Villamor, Maureen M. – Research and Practice in Technology Enhanced Learning, 2020
High attrition and dropout rates are common in introductory programming courses. One of the reasons students drop out is loss of motivation due to the lack of feedback and proper assessment of their progress. Hence, a process-oriented approach is needed in assessing programming progress, which entails examining and measuring students' compilation…
Descriptors: Novices, Problem Solving, Computer Science Education, Introductory Courses
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Tsabari, Stav; Segal, Avi; Gal, Kobi – International Educational Data Mining Society, 2023
Automatically identifying struggling students learning to program can assist teachers in providing timely and focused help. This work presents a new deep-learning language model for predicting "bug-fix-time", the expected duration between when a software bug occurs and the time it will be fixed by the student. Such information can guide…
Descriptors: College Students, Computer Science Education, Programming, Error Patterns
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Miller, Craig S.; Settle, Amber – ACM Transactions on Computing Education, 2019
We investigate conditions in which novices make some reference errors when programming. We asked students from introductory programming courses to perform a simple code-writing task that required constructing references to objects and their attributes. By experimentally manipulating the nature of the attributes in the tasks, from identifying…
Descriptors: Error Patterns, Novices, Programming, Introductory Courses
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Rashkovits, Rami; Lavy, Ilana – International Journal of Information and Communication Technology Education, 2020
The present study examines the difficulties novice data modelers face when asked to provide a data model addressing a given problem. In order to map these difficulties and their causes, two short data modeling problems were given to 82 students who had completed an introductory course in database modeling. Both problems involve three entity sets…
Descriptors: Models, Data, Undergraduate Students, Computer Science Education
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McCall, Davin; Kölling, Michael – ACM Transactions on Computing Education, 2019
The types of programming errors that novice programmers make and struggle to resolve have long been of interest to researchers. Various past studies have analyzed the frequency of compiler diagnostic messages. This information, however, does not have a direct correlation to the types of errors students make, due to the inaccuracy and imprecision…
Descriptors: Computer Software, Programming, Error Patterns, Novices
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Sibbald, Matt; Sherbino, Jonathan; Ilgen, Jonathan S.; Zwaan, Laura; Blissett, Sarah; Monteiro, Sandra; Norman, Geoffrey – Advances in Health Sciences Education, 2019
There is an ongoing debate regarding the cause of diagnostic errors. One view is that errors result from unconscious application of cognitive heuristics; the alternative is that errors are a consequence of knowledge deficits. The objective of this study was to compare the effectiveness of checklists that (a) identify and address cognitive biases…
Descriptors: Bias, Check Lists, Novices, Graduate Medical Education
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Jegede, Philip Olu; Olajubu, Emmanuel Ajayi; Ejidokun, Adekunle Olugbenga; Elesemoyo, Isaac Oluwafemi – Journal of Information Technology Education: Innovations in Practice, 2019
Aim/Purpose: The study examined types of errors made by novice programmers in different Java concepts with students of different ability levels in programming as well as the perceived causes of such errors. Background: To improve code writing and debugging skills, efforts have been made to taxonomize programming errors and their causes. However,…
Descriptors: Programming Languages, Programming, Low Achievement, High Achievement
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Harrison, Gina L.; Goegan, Lauren D.; Macoun, Sarah J. – Canadian Journal of School Psychology, 2019
This study examined the scoring errors across three widely used achievement tests (Kaufman Test of Educational Achievement--Second Edition [KTEA-2], Woodcock--Johnson Tests of Achievement--Third Edition [WJ-III], and the Wechsler Individual Achievement Test--Third Edition [WIAT-III]) by novice examiners. A total of 114 protocols were evaluated for…
Descriptors: Scoring, Error Patterns, Achievement Tests, Novices
Rebecca Smith – ProQuest LLC, 2019
In recent years, computer science has become a cornerstone of modern society. As a result, enrollment in undergraduate computer science programs has expanded rapidly. While the influx of talent into the field will undoubtedly lead to countless technological developments, this growth also brings new pedagogical challenges. Educational resources,…
Descriptors: Computer Science Education, Individualized Instruction, Interaction, Learning Experience
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Schneider, Kent N.; Becker, Lana L.; Berg, Gary G. – Accounting Education, 2017
Given that the usage and complexity of spreadsheets in the accounting profession are expected to increase, it is more important than ever to ensure that accounting graduates are aware of the dangers of spreadsheet errors and are equipped with design skills to minimize those errors. Although spreadsheet mechanics are prevalent in accounting…
Descriptors: Accounting, Spreadsheets, Error Patterns, Error Correction
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Conrad, Susan – Journal of Engineering Education, 2017
Background: Numerous studies have identified a gap between the writing skills of engineering program graduates and the demands of writing in the workplace; however, few studies have analyzed the writing of practitioners and students to better understand that gap and inform teaching materials. Purpose: This study sought to compare word-level,…
Descriptors: College Students, Engineering Education, Civil Engineering, Writing Skills
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Holden, Mark P.; Newcombe, Nora S.; Resnick, Ilyse; Shipley, Thomas F. – Cognitive Science, 2016
Memory for spatial location is typically biased, with errors trending toward the center of a surrounding region. According to the category adjustment model (CAM), this bias reflects the optimal, Bayesian combination of fine-grained and categorical representations of a location. However, there is disagreement about whether categories are malleable.…
Descriptors: Memory, Spatial Ability, Bias, Bayesian Statistics
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