Instructional goals
The course Digital Tools and Undertaking Research aims to equip students with both theoretical understanding and practical skills to effectively and critically use digital tools in academic research. Adopting a hands-on approach, the course introduces students to the digital research process, from identifying reliable sources and managing data, to using digital tools for analysis, organization, and communication of research outcomes. The course fosters the responsible use of digital tools in research practice, encouraging students to develop a methodologically sound and ethically aware approach to digital scholarship.
Prerequisites
none
Intended learning outcomes
By the end of the course, students will be able to: Identify and access key academic databases and open data sources. Use digital tools (such as Excel, Word, qualitative analysis software, and presentation tools) to plan, conduct, and present research projects. Critically assess the role and impact of digital technologies in knowledge production. Collaborate effectively in interdisciplinary research settings by leveraging digital platforms for content co-creation and sharing. Apply topic modelling techniques to analyze large volumes of textual data and extract key themes. Leverage generative AI tools (e.g., ChatGPT, Claude) to support literature synthesis, idea generation, and interpretation of research outputs responsibly.
Course Contents
Collecting data performing query on specific digital platform. Using Google Form for collecting data. Analyzing a dataset using MS Excel. Using MS Word for describing analysis results. Exploring data visualization and presentation tools for organizing and communicating research. Understanding the role of AI tools in supporting academic work. Applying basic topic modelling techniques to identify latent themes in textual data and support qualitative analysis.
Reference Books
Slides and other materials will be provided during the course using the official e-learning platform
Teaching Methods
The course combines theoretical explanation of concepts and digital procedures with practical, hands-on classroom exercises. Each session includes live demonstrations, guided activities, and individual or group exercises.
Assessment Method
Test based on Multiple choice
Thesis assignment criteria
Serious and motivation interest to study the topics of the course. Individual colloquium.
Week 1
Introduction to the course, overview of digital tools for research, and guided exploration of academic databases and open-access sources.
Week 2
Introduction to Microsoft Excel: Cells, rows, columns, worksheets. Data entry, formatting, formulas, and basic functions. Introduction to references and data organization.
Week 3
Data analysis in Excel: Using functions like IF, SUMIF, and COUNTIF. Sorting, filtering, and working with relative, absolute, and mixed references. Exercises with structured datasets.
Week 4
Pivot tables and charts in Excel: creating and customizing pivot tables. Layout design, summary functions, and "Show Values As". Introduction to pivot charts and practical examples.
Week 5
Advanced visualization and exercises with Excel: editing and formatting charts. Using sparklines, conditional formatting, and filters. Importing data from CSV files. Exercises on stacked bar charts, drill-down analysis, and merging datasets with lookup functions.
Week 6
Introduction to Microsoft Word: basic functions, formatting tools, and structure of academic documents and reports.
Week 7
Using Microsoft Word to develop and refine research documents, focusing on clarity, structure, and effective presentation of content.
Week 8
Introduction to visual presentation tools such as PowerPoint and Canva, focusing on how to effectively communicate research ideas and findings. Advanced techniques for designing visually engaging, coherent, and impactful presentations tailored to academic and professional audiences.
Week 9
Creating and distributing surveys with Google Forms; collecting, organizing, and exporting research data from form responses.
Week 10
Data storytelling: designing meaningful and clear charts to interpret and communicate research results effectively.
Week 11
Introduction to topic modelling: using digital tools to identify latent patterns and themes within large sets of textual data.
Week 12
Using Generative AI tools to support research activities, with a critical focus on opportunities, limitations, and ethical implications.