Important notice
The course guide is provisional.
The PDF version of the course guide may take a few days to become available in the DDD.
Education Research III: ITC Tools in the Research Process
Code: 43200Credits: 6
| Degree programme | Type | Course |
|---|---|---|
| Research in Education | OB | 1 |
Contact lecturer
- Name :
- Èlia Tena Gallego
- Email :
- elia.tena@uab.cat
Teaching staff
- Lurdes Martínez Mínguez
- Angelina Sanchez Marti
- Ingrid Noguera Fructuoso
- Carme Grimalt Alvaro
- Núria Gorgorio Sola
Group languages
You can consult this information at the end of the document.
Prerequisites
There is not any
Objectives
This module aims to introduce students to data analysis in educational research and to the use of ICT tools for this purpose. The module seeks to enable students to critically relate research objectives to the most appropriate qualitative and quantitative analysis strategies. It also addresses the interpretation of results and the preparation of research reports.
Learning outcomes
- CA06 (Adopt criteria of methodological quality for data analysis with ICT tools.) Adopt criteria of methodological quality for data analysis with ICT tools.
- KA06 (Understand how methodological paradigms and designs condition data types and analysis.) Understand how methodological paradigms and designs condition data types and analysis.
- SA05 (Analyse different types of data depending on their nature using ICT tools.) Analyse different types of data depending on their nature using ICT tools.
- SA06 (Report a research study by selecting the most appropriate format and level for the recipients (report, article, contribution to congresses, case study, poster, video, etc.).) Report a research study by selecting the most appropriate format and level for the recipients (report, article, contribution to congresses, case study, poster, video, etc.).
Contents
The main contents of this course are as follows:
- Analysis and interpretation of qualitative and quantitative data using ICT tools, specifically:
-Qualitative data analysis and interpretation with ATLAS.ti: data preparation, project creation, coding, categorisation and interpretation.
-Quantitative data analysis with Jamovi: data preparation, identification of variables and descriptive analysis.
- Scientific dissemination and communication: research portals and digital journals.
- Interpretation of results and preparation of research reports for communication purposes.
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Classroom practice: solving problems/cases/exercises. | 20 | 0.8 | |
| Analysis and discussion of articles and documentary sources | 20 | 0.8 | CA06, KA06, SA05, SA06 |
| Lectures by the teacher | 16 | 0.64 | CA06, KA06, SA05, SA06 |
| Reading articles and documentaries, case studies and information literacy | 94 | 3.76 | CA06, KA06, SA05, SA06 |
The module combines lectures, practical demonstrations using specialised software, the completion of applied exercises, independent work and progressive assessment activities. The sessions are organised around two main areas: qualitative information analysis and quantitative data analysis. They will also include reflections on the communication of results.
All of this involves a range of learning activities, listed below:
- Lectures / taught sessions.
- Presentation of experiences.
- Reading of articles and documentary sources.
- Practical demonstrations: solving problems, cases and exercises.
- Tutorials.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Attendance and participation in all sessions | 20% | 0 | 0 | CA06, KA06, SA05, SA06 |
| Report/individual work of MIII | 50% | 0 | 0 | CA06, SA05, SA06 |
| Activities during the development of the module and research evaluation MIII | 30% | 0 | 0 | CA06, KA06, SA06 |
CONTINUOUS ASSESSMENT:
The module will be assessed through the activities indicated. All assessment activities are individual.
The final mark will be the weighted average of the planned activities. In order for this criterion to be applied, students must obtain a minimum mark of 4 in all activities, both those completed during the module and the module report/final assignment.
The submission date for the activities carried out during the module will be agreed with the teaching staff of each group. The submission date for the individual report will be 31 May 2027. The mark for the final assignment may only be recovered if it has been submitted in the first call and if the specific improvement instructions provided for each student are followed. Teaching staff will provide assessment feedback within 20 days of submission.
Attendance is compulsory. In order to obtain a positive final assessment, students must have attended at least 80% of the classes. If a student does not meet the attendance requirement or fails to submit an assessment activity, they will be marked as Not Assessable.
Plagiarism or copying will result in a fail and will be reported to the degree coordination team. The use of generative artificial intelligence tools to replace the student’s own learning activity will result in a mark of zero for the course.
The procedure for reviewing assessment tasks will be carried out individually.
No synthesis assessment is offered.
In order to pass this course, students must demonstrate good general communicative competence, both orally and in writing, as well as a good command of the language or languages of instruction specified in the course guide. Therefore, linguistic accuracy, writing quality and formal aspects of presentation will be taken into account in all activities, both individual and group-based. Students must be able to express themselves fluently and accurately, and must demonstrate a high level of comprehension of academic texts. An activity may be returned unassessed or failed if the lecturer considers that it does not meet these requirements.
SINGLE ASSESSMENT:
Single assessment consists of submitting all assessment activities during the week following the last scheduled session of the module, on 31 May 2027. The same conditions regarding class attendance apply to this assessment modality: attendance is compulsory and, in order to obtain a positive final assessment, students must have attended at least 80% of the classes.
Bibliography
Main references:
- Albarracín, L., & Ärlebäck, J. B. (2025). Exploring the role of assumptions in mathematical modeling teacher training using Fermi problems. ZDM – Mathematics Education. https://doi.org/10.1007/s11858-025-01677-0
- Brunet-Biarnes, M. & Albarracín, L. (2024). Exploring the negotiation processes when developing a mathematical model to solve a Fermi problem in groups. Mathematics Education Research Journal, 36(1), 177-198.
- Cohen, L., Manion, I., Morrison, K. (2000). Research Methods in Education (5th edition). London and New York: Routledge, Falmer, pp 73-91.
- Field, A. (2019). Discovering statistics using SPSS (and sex and drugs and rock ‘n’ roll). SAGE Publications.
- Flick, U. (2014). La gestión de la calidad en Investigación Cualitativa. Morata.
- Gibbs, G. (2012). El análisis de datos cualitativos en investigación cualitativa. Morata.
- Hamilton, L., Elliott, D., Quick, A., Smith, S., & Choplin, V. (2023). Exploring the use of AI in qualitative analysis: A comparative study of guaranteed income data. International journal of qualitative methods, 22, 16094069231201504.
- Hernández-Sampieri, R., & Mendoza, C. (2018). Metodología de la Investigación. Las rutas cuantitativa, cualitativa y mixta. McGraw-Hill.
- Kalpokas, N., & Radivojevic, I. (2022). Bridging the gap between methodology and qualitative data analysis software: A practical guide for educators and qualitative researchers. Sociological Research Online, 27(2), 313-341.
- Kangiwa, B. I., Ladan, I. M., Nassarawa, H. S., Sabo, S. A., & Umar, M. A. (2024). Free and open-source software for data analysis: Leveraging the potentials of JASP, Jamovi and PSPP in Nigeria tertiary institutions. International Journal of Multidisciplinary Research in Science, Technology and Innovation, 3(1), 1-8.
- Lopezosa, C., & Codina, L. (2023). ChatGPT y programas CAQDAS para el análisis cualitativo de entrevistas: pasos para combinar la inteligencia artificial de OpenAI con ATLAS. ti, Nvivo y MAXQDA.
- Lopezosa, C., Codina, L., & Freixa, P. (2022). ATLAS. ti para entrevistas semiestructuradas: guía de uso para un análisis cualitativo eficaz. DigiDoc Research Group, 1-30.
- Martínez-Garrido, C., & Murillo-Torrecilla, F. J. (2012). Análisis de datos cuantitativos con SPSS en investigación socioeducativa. UAM.
- Pallant, J. (2020). SPSS Survival Manual: a step-by-step guide to data analysis using IBM SPSS. Routledge.
- Ravitch, S.M.& Mittenfelner, N. (2016). Qualitative Research: Bridging the Conceptual, Theoretical and Methodological. LA: Sage Publishing.
- Revuelta, F.I. & Sánchez, M.C. (2012). Programas de análisis cualitativo para la investigación en espacios virtuales de formación. http://campus.usal.es/~teoriaeducacion/rev_numero_04/n4_art_revuelta_sanchez.htm
- Silver, C., & Lewins, A. (2014). Using software in qualitative research: A step-by-step guide. Sage.
- Woods, M., Paulus, T., Atkins, D. P., & Macklin, R.(2016). Advancing qualitative research using qualitative data analysissoftware (QDAS)? Reviewing potential versus practice in published studies using ATLAS. ti and NVivo, 1994–2013. Social Science Computer Review, 34(5), 597-617.
WEBS
- http://www.atlasti.com/index.html
- http://www.eval.org/Resources/QDA.asp (Qualitative Software. American Evaluation. Association).
- http://www.refworks.com/
- http://biblio.universia.es/catalogos-recursos/bases-datos/
- http://biblio.universia.es/catalogos-recursos/metabuscadores/
- http://biblio.universia.es/catalogos-recursos/revistas-digitales/
- http://www.qsrinternational.com/other-languages_spanish.aspx
Software
Qualitative data analysis: ATLAS.ti
Quantitative data analysis: Jamovi
Course groups and languages
The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE
| Type of teaching | Group | Language | Semester | Shift |
|---|---|---|---|---|
| (PAULm) Classroom practices (master) | 1 | Catalan | second semester | afternoon |
| (PAULm) Classroom practices (master) | 2 | Catalan | second semester | afternoon |