Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Evaluation and Research in Education (IJERE)
Arar Al Tawil
Applied Science Private University
Computer Science & IT Education Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Evaluating gamified learning strategies in internet of things-based software engineering education Amneh Shaban; Arar Al Tawil
International Journal of Evaluation and Research in Education (IJERE) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v15i3.39041

Abstract

This study examines the effectiveness of gamified formative assessment in undergraduate internet of things (IoT) education, focusing on how content complexity and question format influence student performance. A quasi-experimental comparative design was employed, administering two gamified quizzes to 75 undergraduate students enrolled in two IoT-related courses DevOps for IoT (n=41) and human computer interaction in IoT (n=34) during spring 2025. The gamified platform incorporated visual feedback, progress indicators, and interactive components. Results revealed statistically significant differences in student performance between the two quiz conditions, with human–computer interface (HCI) students substantially outperforming DevOps students. Question-level analysis further indicated that fill-in-the-blank formats impaired performance more than multiple-choice formats, and a pronounced ceiling effect was observed in the HCI assessment. These findings suggest that gamification effectiveness is contingent on alignment between content complexity, question format, and students’ prior knowledge. Educators are advised to calibrate assessment difficulty and question types carefully when designing gamified learning experiences in technical education.
Deep learning-based prognostic modeling of brain tumors Laiali Almazaydeh; Arar Al Tawil
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3792-3804

Abstract

Headline accuracy is not enough to move a deep-learning brain tumor magnetic resonance imaging (MRI) classifier into the clinic. A model also needs repeated-split validation, trustworthy probabilities, a statistical sanity check, and a small enough footprint for hospital hardware. This study cover all four in one framework. Five convolutional neural network (CNN) backbones (visual geometry group (VGG)-16, residual network (ResNet)-50V2, MobileNetV2, EfficientNetB0, and dense convolutional network (DenseNet)-121) are trained on 4,600 public brain MRI scans (2,513 tumor, 2,087 healthy) under a shared two-phase transfer-learning recipe. The usual single split is replaced by stratified 5-fold cross-validation (CV), with paired McNemar and DeLong tests and 1,000 bootstrap resamples. Calibration is judged by expected calibration error (ECE), Brier, reliability diagrams, and temperature scaling. VGG16 wins mean accuracy (98.72 ± 0.42%); ResNet50V2 has the tightest area under the curve (AUC) (0.9986 ± 0.0006); the two are statistically tied. MobileNetV2 is the best-calibrated model out of the box (ECE = 0.0021) with only 2.59 M parameters, making it the most deployable. A four-level confidence-based risk score turns calibrated probabilities into triage tags. This study also flag a measured 3.23% format-duplicate leakage in the public dataset and presents the framework as a radiologist co-pilot, not a prognostic model.
Co-Authors Amneh Shaban Laiali Almazaydeh