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Learning Motivation Mediates Growth Mindset, Self-Efficacy, and AI Usefulness Impact on Programming Problem-Solving Skills Fardan, Muhammad; Fathahillah, Fathahillah; Fakhri, M. Miftach; Sanatang, Sanatang; Adiba, Fhatiah; Soeharto, Soeharto; Amukune, Stephen
Tadris: Jurnal Keguruan dan Ilmu Tarbiyah Vol 10 No 1 (2025): Tadris: Jurnal Keguruan dan Ilmu Tarbiyah
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/tadris.v10i1.23743

Abstract

Artificial Intelligence has rapidly developed, especially in education and programming, providing advantages in improving learning efficiency and personalizing educational content. This study examines the relationships between learning motivation and problem-solving skills, as well as factors influencing learning motivation, namely growth mindset, self-efficacy, and perceived usefulness of Artificial Intelligence. Data collected from 276 students were analyzed using Partial Least Squares Structural Equation Modeling. The results show that growth mindset, self-efficacy, and perceived usefulness significantly influence learning motivation. Additionally, learning motivation strongly predicts problem-solving skills in programming tasks. These findings emphasize the critical role of psychological factors in fostering learning motivation and improving problem-solving abilities within Artificial Intelligence-enhanced programming environments. This research offers valuable insights for educators and instructional designers to develop effective strategies that integrate psychological support and Artificial Intelligence tools, ultimately enhancing student learning outcomes.
AI Literacy Meets Ethics: Critical Appraisal's Mediating Role in Shaping Ethical Awareness in Higher Education Syukur, Pramudya Asoka; Fakhri, M. Miftach; Firdaus, Firdaus; Putra, Kurnia Prima; Adiba, Fhatiah; Arifiyanti, Fitria
Online Learning In Educational Research (OLER) Vol 5, No 1 (2025): Online Learning in Educational Research
Publisher : CV FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/oler.v5i1.508

Abstract

As artificial intelligence increasingly permeates higher education systems worldwide, developing students' ethical awareness has become essential for responsible AI implementation. This study seeks to examine the connections between technical understanding, applied knowledge, and critical appraisal in shaping ethical awareness within the context of AI literacy. The study utilizes a quantitative method, applying Partial Least Squares Structural Equation Modeling (PLS-SEM) to data gathered from 322 university students. The findings indicate that technical understanding has a direct favorable influence of 0.180 on ethical awareness, while applied knowledge demonstrates a stronger impact of 0.467. Critical appraisal serves as a significant complementary partial mediator, with indirect path coefficients of 0.083 for technical understanding and 0.155 for applied knowledge, strengthening their relationships with ethical awareness. This study concludes that AI literacy educational programs should not only emphasize technical and applied knowledge but also foster critical appraisal skills to promote ethical AI usage.
Recommendation of Assistant Lecturer for Advanced Programming Course using Fuzzy Tahani Amaliah, Annisa Shela; Sulmadani, Fitriah; Khaida, Fatihah; Adiba, Fhatiah; Nasrullah, Asmaul Husna; Munawir, Munawir
Media of Computer Science Vol. 2 No. 1 (2025): June 2025
Publisher : CV. Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mcs.v2i1.224

Abstract

The recruitment of teaching assistants for certain courses is a regular activity conducted during specific periods to meet the needs of teaching and learning both inside and outside the classroom. The main objective of the recruitment is to obtain the best teaching assistants who can perform their duties optimally. However, selecting teaching assistants based solely on grades and GPA without considering other criteria is ineffective and subjective. This research proposes the use of the Fuzzy Tahani method in a recommendation system to select teaching assistants for the Advanced Programming course. The aim is to develop a recommendation system for selecting teaching assistants using the Fuzzy Tahani method and to improve objectivity and accuracy in the decision-making process for selecting teaching assistants by considering four criteria: grades, recommendations, availability, and students' GPA. This recommendation system approach is necessary to minimize subjectivity and ensure that the selected teaching assistants can effectively carry out their duties. The result obtained is a recommendation system for selecting teaching assistants, where there is a high level of accuracy between the system's results and the calculation results in Excel, with a difference of 0.00 between them.
A Decision Support Model for Scholarship Recipient Selection Based on Tsukamoto Fuzzy Logic Muhtadi; Aksa, Muhammad; Naoval, Ahmad; Adiba, Fhatiah; Nasurllah, Asmaul Husna
Media of Computer Science Vol. 2 No. 1 (2025): June 2025
Publisher : CV. Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mcs.v2i1.225

Abstract

This study proposes a decision support model for scholarship recipient selection based on the Tsukamoto fuzzy logic method to overcome the inefficiencies and subjectivity inherent in manual selection processes. The model incorporates three key criteria: Grade Point Average (GPA), parents’ income, and number of dependents. Experiments were conducted using a dataset of 25 students obtained from a public Kaggle repository. The model employs fuzzification, rule formulation, and defuzzification to compute a final decision score for each applicant. The experimental results demonstrate that the proposed model achieves an accuracy rate of 92%, indicating its effectiveness in supporting objective and efficient scholarship selection decisions.