Research Interests
Research Interests
Fitsum Deriba's research interests lie at the intersection of Human-Computer Interaction (HCI) and User Experience (UX) with a strong emphasis on Web Accessibility and Inclusive Design, further enriched by an interest in the potential of AI in Education and Smart Learning environments. His methodological toolkit, encompassing both qualitative and quantitative approaches, including design-based research and various analytical techniques, positions him to investigate the design, development, and evaluation of accessible and inclusive digital learning technologies.
Specifically, Fitsum Deriba's interest in HCI and UX focuses on creating user-centered interfaces and experiences, particularly within educational contexts. This includes exploring the design and development of learning applications, such as programming apps, virtual reality (VR), and virtual labs (V-Labs), tailored to diverse learners. His commitment to Web Accessibility and Inclusive Design ensures that these digital resources adhere to universal design principles and accessibility standards, making them usable by individuals with varying abilities. He aims to evaluate the accessibility of existing and newly developed learning technologies, ensuring compliance with guidelines and leveraging assistive technologies.
Furthermore, Fitsum Deriba is keen to explore the integration of Artificial Intelligence within educational settings to create smart learning environments. This includes investigating AI-powered tools for personalized learning, intelligent tutoring systems, conversational agents, and virtual/augmented reality enhanced learning experiences. His interest extends to the ethical considerations surrounding AI in education and the development of effective frameworks and curricula for AI-driven learning. His methodological expertise in analyzing user interactions and learning data through educational data mining and sequence mining will be invaluable in understanding the impact and effectiveness of these accessible and AI-enhanced learning technologies.
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Research IDs
Google Scholar: https://scholar.google.com/citations?user=vCP674EAAAAJ
Web of Science : NKQ-4070-2025
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<<Open for Grant collaboration, MSc/BSc supervision, and Teaching opportunities >>
Possible/interested research areas to advice MSc/BSc student:
Human-Computer Interaction and User Experience : User Interface (UI) Design, User Experience (UX) Design, Usability Testing, Accessibility, Interaction Design; e.g. Design and Development of Learning app (Programming app, VR, V-Labs) for diverse learners.
Keywords: Software Design, User Interface Design, User-Centered Design, Intelligent User Interface, User Experience, Interaction Design, Voice Assistant, Conversational Agent, Virtual Assistance, Chatbot, Personal Digital Assistant, Visual Impairment, Assistive Technology, Usability Engineering, Intelligent Agent, Multimodal Interaction, Voice Input, User Experience Evaluation, User Acceptance, User Modeling, Web User Interface, Ergonomics, Teaching Practice, Interaction Techniques, Curriculum Design, Requirement Engineering, Software Engineering Education, Software requirement, Design practices, Software Developer, Technology Design, Game Design, Software Development,User Interfaces, Disability Studies, Accessible Technology, Inclusive Design, Software Engineering, Sensation of Hearing, software Design, Global South, Disability Services, Learning Resource, Best Practice, Learning Situation, Usability Engineering,Usability Study, Usability Problem, usability test, usability questionnaire, user experience evaluation, cognitive walk-through, usability model, software testing, usability design, usability inspection, usability measure, usability expert, user interface evaluation, contextual design, severity rating, usability attribute, inspection method, evaluation system, usability feature, usability principle. With User Satisfaction, User Preference, Behavioral Interaction, User Engagement, User Interaction
Web Accessibility and Inclusive Design: Evaluating (auditing) accessibility of Websites, Software, mobile applications
Keywords: Web Accessibility, Accessibility Standard, Digital Accessibility, Accessibility Requirement, Automated Tools, Screen reader, World Wide Web Consortium, Success Criterion, Alternative Text, Accessibility Feature, Universal Design, Accessibility Compliance, Rich Internet Application, Design Guideline, Americans With Disability Act, Heuristic Evaluation, Web design, Conformance Level, Universal Access, Authoring Tools, Cognitive Disability, Digital Content, Usability Evaluation. In E-learning, Government Service, Web Development, e-commerce, Mobile Application.
AI Education and Smart Learning: AI in Education (framework, tools, curriculum, theory), AI Ethics, Expert Systems, AI-based instruction and tutoring-Intelligent tutoring, Conversational agents and chatbots, automatic grading and feedback, predictive analysis for assessments, Virtual and augmented reality on enhanced learning.
Keywords: Curriculum Development, Teaching Machine, Professional Development, Virtual Reality, Immersive Virtual reality, Augmented Reality, Mixed Reality, Extended Reality, Mounted Display, Virtual Learning Environment, Imersive Technology, Metaverse, Immersive Environment, Virtual Laboratories, Imersive Experience, Virtual Reality Experience, Smart Environment, Personalized Learning. With Learning Experience, Learning Motivation, Self Efficacy, Learning Outcome, Student Engagement, Student Motivation, Learning Effect, Skill Training, User Acceptance, Influencing factor, User satisfaction, students' attitude, students' perception, Teacher Perception.
Learning Analytics and Educational Technology: Understanding and Predicting Learner Behavior (Engagement Analysis, Performance Prediction, Behavioral Pattern Analysis); Personalization and Improvement of Learning Experiences (Adaptive Learning, Personalized Feedback, Resource Recommendation); Early Intervention and Support (Risk Identification, Targeted Interventions, Instructor Support)
Keywords: Learning Analytics, Dashboards, Learning Management System, E-learning, Educational Data Mining, Learning Design, Predictive Analytics, Data Analytics, Learning Experience, Online Learning, Analytics Tools, Data Mining, Learning Systems, Learning Process, Blended Learning, Collaborative Learning, Technology Enhanced Learning, Distance Education, Data Protection, Data Privacy, Adaptive Learning, Students Success, Distance Learning, Learning Platforms, Digital Education, Massive Open Online Course, Analytical Model, Student Performance, Feedback (learning), Early Warning System, Intelligent Tutoring System, Online Discussion, Log Data, Predictive Model, Data Science, Formative Assessment, Online Teaching, Big Data Analytics. With Student Engagement, At-Risk Students, Student Behavior, Learning Outcome, Academic Performance, Student Retention, Students Interaction
Computational Thinking and Programming Education: CT education (pedagogy and curriculum-learning activities: plugged/Unplugged; age appropriate CT curricula: K-12, higher education; Teacher training and professional development for CT education); Assessment of CT; Tools and Environment for CT (Visual programming: scratch, robotics platform, data analysis tools).
Keywords: Thinking skill, Computer Programming, K-12 Education, Visual Programming, Programming Environment, Game based learning, Problem-solving skills, Programming languages, Programming course, School Curriculum, Project based learning, Computer Aided instruction, Critical thinking, Digital literature, Teacher training, Curriculum Development, Programming Experience, 21st Century Skill, Digital literacy. With Student Attitude, Gender Difference, ...
Bibliometrics and Research Analysis: analyzing the trend and pattern of of scientific literature
Keywords: Bibliometric Analysis, Scientometrics, Citation Analysis, Research Trends, Bibliometric study, Coauthorship, Biblioshiny, Network Analysis, Text Mining, Data Visualization, Authorship Analysis, Trend Analysis, Knowledge Mapping, Topic Modeling, Visual Analysis, Documentary Analysis, Citation Network, Content Analysis, Research Topic, Social Network Analysis, Text Mining, Knowledge Map, Thematic Analysis, Bibliographic Analysis, Information Analysis, Highly Cited Paper, Co-occurence Network, Bibliology.
Research methodologies: Usability Testing, Heuristic Evaluation, Sequence analysis, Statistical Analysis, Social Media Analytics, Bibliometric Analysis, Network Analysis, Social Network Analysis, Epistemic Network Analysis, Scientometrics, Citation Analysis, Literature Review, Systematic Review, Text Mining, Thematic Analysis, Survey Method, Factor Analysis, Case study, Multi-Level Modeling, Mixed Method, Discourse Analysis, Focus Group, Qualitative Method, Semi-structured Interview, Self-Deterministic Theory, Unified Theory, Structured Equation Modeling, Experimental Study.
Tools: Figma, Heatmaps, Click Maps, Qualitative feedback, Data Logging and Observations, WAVE, AXE, Google Lighthouse, JAWS, WebAIM Contrast Checker, Manual Checklist& Guideline Review, WCAG Conformance Level, Heuristic Lists, Spreadsheet Softwares, Collaborative Platforms (Google Docs, Disco), Tableau, Power BI, Python Libraries (e.g. Seaborn, Ploty), R, TraMiner (in R), Scratch, APP Inventor, Java/Python, Online Coding Platforms (Codecademcy, HackerRank), Databases (Web of Science, Scopus, Google Scholor, Dimentions, Lens.org, PubMed); Biblimetric analysis SW (Bibliometrix-Biblioshiny web app), Gephi, Text Analysis (RapidMiner, kH Coder, Orenge)
Possible analyses and Relevant aspect to my research works:
1) In Usability Testing
User Satisfaction in digital (or learning) technologies: Measured through questionnaires and qualitative feedback.
Qualitative Feedback Analysis: Analyzing user comments and observations for insights.
2) In Evaluating (Auditing) Accessibility of Websites, Software, Mobile Applications
WCAG Conformance Level analysis: Determining the level of compliance with WCAG (A, AA, AAA).
Identification of Accessibility Barriers: Pinpointing issues like missing alternative text for images, insufficient color contrast, keyboard navigation traps, etc.
Examining the Compliance with Accessibility Standards & Legislation: Assessing adherence to laws like ADA (Americans with Disabilities Act) or Section 508.
Web or technology accessibility Audit: Assessing accessibility on digital technologies, mobile devices, considering touch interactions.
Accessibility Training: Educating teams on accessibility best practices.
Identification of Usability Problems: Finding specific instances where the interface violates established usability heuristics.
3) In Learning Analytics and Educational Technology
Engagement Analysis: Measuring student participation, time spent, and interaction with learning materials.
Performance Prediction: Using data to forecast student success or risk of failure.
Behavioral Pattern Analysis: Identifying common learning behaviors and sequences of actions.
Learning Path Analysis: Understanding how students navigate through learning resources.
Content Usage Analysis: Determining which learning materials are most frequently accessed and used.
Assessment Data Analysis: Evaluating student performance on quizzes, assignments, and exams.
Feedback Analysis (using NLP): Analyzing open-ended feedback to identify common themes and sentiment.
Social Learning Analytics: Examining interactions and collaborations within online learning environments.
Predictive Modeling: Developing models to predict outcomes like student retention or course completion.
Early Warning System Development: Identifying students who may need additional support.
Personalized Learning Recommendations: Suggesting resources or activities based on individual student data.
Adaptive Learning System Evaluation: Assessing the effectiveness of personalized learning pathways.
Developing Learning Analytics Dashboards: Creating visual representations of key learning indicators.
4) In Computational Thinking and Programming Education
Student Performance analysis in Programming Tasks: Evaluating the correctness, efficiency, and style of code.
Learners Engagement with Programming Activities: Measuring time spent, number of attempts, and participation.
Development of Computational Thinking Skills: Assessing abilities in problem decomposition, pattern recognition, abstraction, and algorithm design.
Evaluating the Impacts of Different Pedagogical Approaches: Comparing the effectiveness of plugged vs. unplugged activities, game-based learning, etc.
Analyzing Gender Differences in Programming Education: Examining participation and performance differences.
Curriculum design and Evaluation: Developing age-appropriate learning activities, Assessing the alignment of curricula with learning outcomes.
Examining Student Attitudes and Perceptions towards Computing: Measuring changes in interest and confidence.
Designing Effective Learning Environments for Programming: Creating supportive and engaging spaces for coding.
5) Bibliometrics and Research Analysis
Citation Analysis: Examining the number of citations a publication or author has received.
Co-citation Analysis: Identifying publications that are frequently cited together.
Co-authorship Analysis: Studying collaboration patterns among researchers.
Keyword Co-occurrence Analysis: Identifying frequently occurring keywords and their relationships.
Research Trend Analysis: Identifying emerging topics and shifts in research focus over time.
Journal Impact Assessment: Evaluating the influence of academic journals.
Author Impact Assessment: Measuring the influence and productivity of individual researchers.
Thematic Analysis (of keywords or abstracts): Identifying key research themes and their evolution.
Network Analysis (of citations, co-authorship, keywords): Visualizing relationships and identifying influential nodes.
Scientometric Mapping: Creating visual representations of the structure and evolution of scientific fields.
Literature Review Mapping: Systematically mapping the existing literature on a topic.
Data Extraction from Bibliographic Databases: Exporting data in formats suitable for analysis software.
Data Cleaning and Reprocessing: Standardizing author names, affiliations, and keywords.
Defining Search Strategies: Developing effective queries to retrieve relevant publications.
Understanding Bibliometric Indicators: Interpreting metrics like citation counts, h-index, impact factor, etc.
Visualizing Bibliometric Networks: Creating meaningful and informative visualizations.
Interpreting Bibliometric Findings: Drawing conclusions about research trends and impact.