Replication Data for: Profiling the Skill Mastery of Introductory Programming Students

Dataset Description This dataset comprises four files that collectively provide detailed information on the item responses of students in introductory programming courses and a validated Q-matrix for cognitive diagnostic modeling (CDM). The files included are: 1. Item Response Data (Combined) Filename: item_response_data_combined.csv Description: This file contains the combined item response data for all students, regardless of their academic program. Each row represents a student's responses, and each column represents one of the 100 assessment items. The entries are binary, with 0 indicating an incorrect response and 1 indicating a correct response. 2. Item Response Data for IT Filename: item_response_data_IT.csv Description: This file contains the item response data specifically for Information Technology (IT) students. Similar to the combined data, each row represents an IT student's responses, and each column represents one of the 100 assessment items. The entries are binary (0 for incorrect, 1 for correct). 3. Item Response Data for CS Filename: item_response_data_CS.csv Description: This file contains the item response data specifically for Computer Science (CS) students. Each row represents a CS student's responses, and each column represents one of the 100 assessment items. The entries are binary (0 for incorrect, 1 for correct). 4. Validated Q-Matrix Filename: validated_q_matrix.csv Description: The Q-matrix file is a crucial component for CDM. It specifies the relationship between the 100 assessment items and the underlying cognitive attributes they are intended to measure. Each row represents an item, and each column represents a cognitive attribute. Entries in the Q-matrix are binary, indicating whether a particular item measures a specific attribute (1 for yes, 0 for no). Purpose and Use The dataset is designed for researchers and educators interested in analyzing the cognitive skill profiles of programming students using CDM. The item response data files provide the raw responses needed for such analysis, while the validated Q-matrix offers a structured framework for interpreting these responses in terms of specific cognitive attributes. Potential Applications Cognitive Diagnostic Modeling (CDM): To identify specific cognitive strengths and weaknesses of students. Educational Research: To study the effectiveness of different teaching methods and curricula in programming education. Program Improvement: To inform curriculum development and targeted instructional interventions based on identified skill gaps. This dataset supports comprehensive analysis and insights into the cognitive processes involved in learning programming, ultimately contributing to improved educational practices and student outcomes in the field of computer science and information technology.