CP1 - Introduction to AI and Generative AI:
* Theoretical exposition on the historical context, evolution and important concepts about Artificial Intelligence (AI) and Generative AI
CP2 - Prompt Engineering:
* Explanation of good practices for interacting with generative language models
CP3 - Generative AI Tools:
* Exploration of multiple Generative AI tools, based on text, images and videos
CP4 - Formation of argumentative content:
* Development of creative solutions using argumentation practices and Generative AI tools
CP5 - Rules for scientific writing:
* Application of citation and referencing standards (APA standards) in academic writing

Assessment throughout the semester includes the following activities:
1. Individual activities (50%): will include assessment elements related to prompt engineering, the responsible use of Generative AI, and also oral defense in the group presentation.
2. Group activities (50%) [ students are organized into groups of up to 5 members, formed randomly]: will include the creation, revision, editing, and validation of AI-generated content, as well as the appropriate, strategic, and responsible use of generative AI tools and techniques, throughout the group work during classes, culminating in a presentation and peer review session in the final class.

General Considerations:
Given the theoretical-practical and applied nature of the UC, the assessment takes place in the form of assessment throughout the semester, with assessment by exam not being foreseen. Assessment in the 2nd season and special season will have an alternative assessment method, so any students wishing to carry out the assessment in these seasons must first contact the teacher to be informed of the assessment procedure.

For assessment purposes throughout the semester, attendance and participation in at least 80% of classes and having more than 7 points in each of the assessments is mandatory. Approval requires a minimum final grade of 10 points and compliance with the attendance and participation requirement mentioned above.
The group will have to ensure that there is at least one computer for each group to allow activities to be carried out in the classroom.

The curricular unit coordinator may determine the holding of a complementary oral discussion regarding any assessment element/instrument provided for in this Curricular Unit Sheet, whenever this proves necessary to confirm the acquisition of the knowledge and skills assessed, or clarify discrepancies between different assessment elements.

The complementary oral discussion does not constitute an autonomous assessment instrument, does not have its own weight in the final classification of the curricular unit and is intended exclusively for validating the classification of the respective assessment element/instrument. The classification previously assigned to this element/instrument is provisional in nature until the complementary oral discussion is carried out, and may be confirmed or revised depending on the validation carried out and the applicable evaluation criteria.

Mandatory

Ribeiro, A. & Rosa, A. (2024). Descobrindo o potencial do CHATGPT em sala de aula: guia para professores e alunos. Atlantic Books.
Cotton, D. R., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in education and teaching international, 61(2), 228-239.
d’Alte, P., & d’Alte, L. (2023). Para uma avaliação do ChatGPT como ferramenta auxiliar de escrita de textos acadêmicos. Revista Bibliomar, São Luís, 22(1), 122-138. DOI: 10.18764/2526-6160v22n1.2023.6.
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences, 103, 102274.

Optional

Cowen, T., & Tabarrok, A. T. (2023). How to learn and teach economics with large language models, including GPT. GMU Working Paper in Economics No. 23-18. DOI: 10.2139/ssrn.4391863
Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023). ChatGPT and a new academic reality: Artificial Intelligence‐written research papers and the ethics of the large language models in scholarly publishing.

PC1. Memory concepts:
* work
* long term
* interaction between MT and MLP
* learning (cognitivist perspective)
PC2. Concepts of:
* Communication
* Multimedia
PC3. Multimedia Learning Theory
PC4. Multimedia Learning Process
PC5. Principles of multimedia learning
PC6. Cognitive Load Theory
PC7. Multimedia presentations
PC8. Educational videos according to Multimedia Learning Theory
PC9. Design, production and evaluation of multimedia projects in an educational context

Students decide by the 2nd class the method of assessment for the 1st period, which can be: assessment throughout the semester or final exam. In the assessment modality throughout the semester, a minimum attendance of no less than 2/3 of classes is required.
In the assessment modality throughout the semester (1st season) students carry out:
- several individual tasks (20%) with a minimum grade of 8.5
- an individual written test (20%)
- a final group project (60%)
In the final exam modality (available for the 1st season, 2nd season and special season), the final exam corresponds to 100% of the final grade.

Mandatory

Brouner, N. (2022). Using video to develop teaching. New York: Routledge.
Mayer, R. E. & Fiorella, L. (2022). The Cambridge Handbook of Multimedia Learning (3rd ed.). New York: Cambridge University Press.
Mayer, R. E. (2021). Multimedia Learning (3rd ed). New York: Cambridge University Press.
Miranda, G.L, Rafael, M., Melo, M., Costa, J.M., & Pontes, T.B. (2021). 4C-ID model and cognitive approaches to instructional design and technology: emerging research and opportunities. Hershey PA, USA. IGI-Global.

Optional

S1. Introduction to Design Thinking

S2. Stage 1: Empathize

S3. Stage 2: Define the Problem

S4. Stages 3 and 4: Ideate and Prototype

S5. Stage 5: Test and Feedback

S6. Critical Reflection (2 hours)

The course unit adopts an active, project-based learning approach and is assessed through continuous assessment throughout the semester:

Class participation (20%): Assesses students' attendance, engagement, and contribution to classroom discussions and learning activities.
Individual project (40%): Students will develop an individual project applying the Design Thinking process to address a specific problem. Assessment will consider the application of the Design Thinking stages, the quality of the proposed solutions, and the level of creativity demonstrated.
Group project (40%): Students will work in teams to develop a collaborative project applying the Design Thinking process to address a real-world challenge. Assessment will be based on the application of the Design Thinking stages, the quality of the proposed solutions, and the effectiveness of teamwork and collaboration.

For all submitted assignments, the use of artificial intelligence (AI) tools must be transparent, explicitly declared, and compliant with Iscte's academic integrity regulations, through a signed declaration attached to the submitted work. Failure to provide this declaration or to disclose the use of AI technologies constitutes an academic misconduct offence under the Iscte Student Disciplinary Regulations.

Given the theoretical-practical and applied nature of the course unit, assessment is conducted exclusively through continuous assessment during the semester, and no final examination is offered. The final assessment of written assignments may include an individual oral examination to clarify or validate the submitted work.

Assessment during the resit period and the special assessment period will follow an alternative assessment format. Students wishing to be assessed during these periods must contact the course lecturer in advance to obtain information about the applicable assessment procedures.

For continuous assessment, students are required to attend and actively participate in at least 80% of the scheduled classes.

Mandatory

Brown, T. (2008). Design Thinking. Harvard Business Review, 86(6), 84–92.
Lewrick, M., Link, P., & Leifer, L. (2018). The design thinking playbook: Mindful digital transformation of teams, products, services, businesses and ecosystems. John Wiley & Sons.
Lockwood, T. (2010). Design Thinking: Integrating Innovation, Customer Experience and Brand Value. Allworth Press.
Stewart S.C (2011) “Interpreting Design Thinking”. In: https://www.sciencedirect.com/journal/design-studies/vol/32/issue/6

Optional

Brown, T., & Katz, B. (2011). Change by design. Journal of product innovation management, 28(3), 381-383.
Brown, T., Katz, B. M. Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation. HarperBusiness, 2009.
Liedtka, J. (2018). Why Design Thinking Works. Harvard Business Review, 96(5), 72–79.
Gharajedaghi, J. (2011). Systems thinking: Managing chaos and complexity. A platform for designing business architecture. Google Book in: https://books.google.com/books?hl=en&lr=&id=b0g9AUVo2uUC&oi=fnd&pg=PP1&dq=design+thinking&ots=CEZe0uczco&sig=RrEdhJZuk3Tw8nyULGdi3I4MHlQ

CP1: Introduction to Learning Psychology
CP2: Concepts of Learning Psychology and Object of Study
CP3: Main Theories of Learning Psychology:
(Behaviorism, Cognitivism, Humanism, Socio-constructivism, and Connectivism)
CP4: Conceptions of Memory in Learning
CP5: Applications of Memory in Educational Practices
CP6: Concept of Motivation
CP7: Motivational Guidance of Students for Learning
CP8: Strategies to Promote Motivation for Learning
CP9: Learning Strategies in the School Context
CP10: Learning Difficulties
CP11: Learning Psychology and Bullying

Evaluation throughout the period:

Individual class participation - weighted at 10%, requiring a minimum attendance of at least 2/3 of the classes.
Completion of 4 mini-tests (one at the end of each module), each representing 10% of the final grade and requiring a minimum grade of 7.5 out of 20 for each mini-test.
Completion of a group project, representing 50% of the final grade, with a minimum grade of 8 out of 20.

The average of the assessments must be equal to or greater than 9.5 out of 20.

Evaluation through an exam (available in Period 1 by student choice, Period 2, and Special Period) - weighted at 100%, minimum grade of 9.5 out of 20 or higher.

Note: The instructor reserves the right to conduct a supplementary oral examination whenever deemed necessary to clarify questions related to the submitted assessment components.

Mandatory

Diana Dias, Psicologia da Aprendizagem: Paradigmas, Motivação e Dificuldades., 2018, Dias, D. (2018). Psicologia da Aprendizagem: Paradigmas, Motivação e Dificuldades. Lisboa: Edições Sílabo.,
Gleitman, H., Fridlund, A. & Reisberg, D. (2003). Psicologia. Lisboa: Fundação Calouste Gulbenkian.
Klein, S. B. (2012). Learning: Principles and applications (6th Ed.). London: Sage
Miranda, G. & Bahia, S. (Org.) (2005). Psicologia da Educação: Temas de desenvolvimento, aprendizagem e ensino. Lisboa: Relógio D'Água Editores.

Optional

CP1: Introduction to data analysis – types, sources, and data lifecycle.
CP2: Organizing and cleaning data in Excel – formats, filters, validation. Basic and advanced Excel functions – statistics, logic, search, and reference.
CP3: Basics of Descriptive Statistics: Types of variables. Frequency tables and graphical representations. Measures of central tendency, dispersion, skewness, and kurtosis. Exploratory data analysis.
CP4: Basics of predictive data modeling: Simple and multiple linear regression, logistic regression.
CP5: Data visualization – graphs, conditional formatting, simple dashboards.

Assessment throughout the semester includes:

- Practical data analysis project completed in a group (maximum 3 students), including a report (15%), with mandatory midterm submission (15%) and oral defense with individual grading (20%). Minimum grade of 8 points.
- Individual exam (30%) to assess theoretical knowledge.
- In-class exercises and weekly assignments (20%).

The use of AI tools (e.g., ChatGPT) is permitted for technical support and must be clearly referenced with the prompts used.

The Final Exam is a written, individual, closed-book exam covering all material.

Those who do not opt ​​for assessment throughout the semester take the final exam in Period 1.

Those who did not successfully complete the assessment throughout the semester, or the exam in Period 1, with a score greater than or equal to 9.5 (out of 20), take the final exam in Period 2.

Mandatory

Wayne L. Winston, Microsoft Excel Data Analysis and Business Modeling (Office 2021 and Microsoft 365), 7th Edition (2021)

Elizabeth Reis, Estatística Descritiva, 7ª Edição, Edições Silabo (2008)

Cole Nussbaumer Knaflic, 'Storytelling with data: a data visualization guide for business professionals', John Wiley & Sons, Inc., 2015.

Optional

Será disponibilizada consoante os interesses dos estudantes e atualizações tecnológicas relevantes.

PC1. Integrated development environments. Introduction to programming: Logical sequence and instructions, Data input and output.
PC2. Constants, variables and data types. Logical, arithmetic and relational operations.
PC3. Control structures.
PC4. Lists and Lists of Lists
PC5. Procedures and functions. References and parameters.
PC6. Objects and object classes.
PC7. File Manipulation.

This course follows a semester-long project-based assessment model due to its predominantly practical nature and does not include a final exam.

Students are assessed based on the following components (A1 + A2 + A3):
A1: 3 Individual Learning Tasks with teacher validation [15% - 5% each]
Three learning tasks will be completed throughout the semester.
The A1 grade corresponds to the average of the grades from the 3 tasks. Minimum grade of 8 out of 20.

A2: ePortfolio [15%]. Challenges will be proposed throughout the semester, including activities to be carried out outside the classroom context (participation in workshops, seminars, or training). The A2 grade corresponds to the average of the grades calculated based on the number of challenges proposed. No minimum grade.

A3: Group Project (3) and individual theoretical-practical discussion [70%]
The A3 grade corresponds to a percentage applied to the project grade, which directly results from the performance demonstrated in the individual theoretical-practical discussion. Minimum grade of 9.5 out of 20.

Attendance:
Attendance is not mandatory.

Mandatory

Portela, F., & Pereira, T. (2023). Introdução à Algoritmia e Programação com Python. FCA. ISBN: 9789727229314.

Sobral, S. R. (2024). Introdução à Programação Usando Python (2.ª ed.). Edições Sílabo. ISBN: 9789895613878.

Downey, A. B. (2024). Think Python: How to Think Like a Computer Scientist (3rd ed.). O'Reilly Media. ISBN: 9781098155438.

Python Software Foundation. Python 3 Documentation. Disponível em: https://docs.python.org/3/

Optional

Menezes, N. N. C. (2019). Introdução à Programação com Python: Algoritmos e Lógica de Programação para Iniciantes. Novatec Editora. ISBN: 9788575227183.

Matthes, E. (2023). Python Crash Course (3rd ed.). No Starch Press. ISBN: 9781718502703.

Martins, J. P. (2015). Programação em Python: Introdução à Programação com Múltiplos Paradigmas. IST Press. ISBN: 9789898481474.

Reitz, K., & Schlusser, T. (2016). The Hitchhiker's Guide to Python: Best Practices for Development. O'Reilly Media. ISBN: 9781491933176.
Disponível em: https://docs.python-guide.org/

Beazley, D., & Jones, B. K. (2013). Python Cookbook: Recipes for Mastering Python (3rd ed.). O'Reilly Media. ISBN: 9781449340377.

PC1. Preparing for a presentation.
PC2. Non-verbal communication techniques.
PC3. Voice and body communication, audience involvement. PC4. Presentation practice and feedback. The learning objectives will be achieved through practical and reflective activities, supported by the active and participatory teaching method which emphasizes experiential learning. Classes will consist of activities such as: Theatrical experiences and group discussions; Practical activities; Presentations and exhibitions of autonomous work; Individual reflection.

The assessment of the Public Presentations with Theatrical Techniques course aims to gauge the development of students' skills in essential aspects of public presentations. The assessment structure includes activities covering different aspects of the experiential learning process involving both theatrical techniques and specific communication techniques.
Assessment throughout the semester includes activities covering different aspects of the process of preparing a public presentation, including group and individual work activities:
Group activities (50%) [students are challenged to perform in groups of up to 5 elements, made up randomly according to each activity proposal].
1-Practical Presentations: Students will be assessed on the basis of their public presentations throughout the semester:
Description: each group receives a presentation proposal and must identify the elements of the activity and act in accordance with the objective.
The results of their work are presented in class to their colleagues (Time/group: presentation - 5 to 10 min.; reflection - 5 min.). Assessment (oral): based on active participation, organization of ideas and objectivity in communication, vocal and body expression, the use of theatrical techniques and performance. Presentations may be individual or group, depending on the proposed activities.
Individual activities (50%)
1-Exercises and Written Assignments (Autonomous Work):
Description: In addition to the practical presentations, students will be asked to carry out exercises and written tasks related to the content covered in each class. These activities include reflecting on techniques learned, creating a vision board, analyzing academic objectives, student self-assessment throughout the semester, answering theoretical questions and writing presentation scripts.
Assessment: (Oral component and written content), organization, content, correct use of the structure and procedures of the autonomous work proposed in each class, ability to answer questions posed by colleagues and the teacher. Communication skills and the quality of written work will be assessed, with a focus on clarity of presentation. These activities will help to gauge conceptual understanding of the content taught.
There will be no assessment by final exam, and approval will be determined by the weighted average of the assessments throughout the semester.
General considerations: in the assessment, students will be given feedback on their performance in each activity.
To complete the course in this mode, the student must attend 80% of the classes. The student must have more than 7 (seven) points in each of the assessments to be able to remain in evaluation in the course of the semester.

Mandatory

Prieto, G. (2014). Falar em Público - Arte e Técnica da Oratória. Escolar Editora.

Optional

Anderson, C. (2016). TED Talks: o guia oficial do TED para falar em público. Editora Intrinseca.
Luiz, P. (2019). Manual de Exercícios Criativos e Teatrais. Showtime. Rodrigues, A. (2022). A Natureza da Atividade Comunicativa. LisbonPress.

S1: Points and vectors in the plane and in space
S2: Distances between points and from a point to a line. Plane sections and spherical surfaces. Measurements
S3: Vectors and operations. Inner product. Properties of vectors. Parallelism and perpendicularity of vectors
S4: Principal vector and equation of the line
S5: Vector product. Vector normal to a plane and equations of the plane
S6: Quantitative and qualitative variables in statistics. Data grouped into classes
S7: Relative and absolute frequencies. Measures of location. Measures of dispersion
S8: Sampling: basics
S9: Correlation coefficients and regression lines

Pass with a mark of not less than 10 (scale 1-20) in one of the following ways:
- Assessment throughout the semester: An assessment test (30%) carried out throughout the semester + weekly exercises (10%) + final test (TF) carried out on the date of the 1st term (60%).
The first assessment test has a minimum mark of 7.0.
The final test (FT) has a minimum mark of 7.0.

In order for the student to opt for assessment throughout the semester, a minimum attendance of no less than 2/3 of the classes is required.

or

- Assessment by Exam (100%).

Notes:
1) A complementary oral assessment may be conducted after any evaluation moment to validate the final grade.
2) For this Course Unit, students must consult the Iscte Academic Code of Conduct, available on the institutional platforms, to ensure compliance with the established ethical and behavioral standards.

Mandatory

Strang, G., (2007) Computational Science and Engineering, Wellesley-Cambridge Press.
Goldstein, L. (2011). Matemática Aplicada - Economia. Administração e Contabilidade, (12a edição) Editora Bookman.
Reis, E., Andrade, R., Calapez, T. e Melo, P. (2015). Estatística Aplicada, vol.1 (6a Ed.), Edições Silabo.
Krishnan, V. (2015). Probability and Random Processes, Wiley.
Hanselman, D., Littlefield B. and MathWorks Inc. (1997). The Student Edition of MATLAB, 5th Version, Prentice-Hall.
Silvestre, A. L. (2007). Análise de Dados e Estatística Descritiva. Lisboa: Escolar Editora.
Curto, J. D. (2021). Estatística com R: Aprenda Fazendo, ISBN-13979-8531511492.

Optional

Campos Ferreira, J. (2018). Introdução à Análise Matemática, Fundação Calouste Gulbenkian.

C1. Fundamentals of Project Management: concepts, characteristics, operations vs. projects
C2. Project Life Cycles and Methodologies (Waterfall, Agile, Hybrid)
C3. Planning and Control Dimensions: scope, time, cost, resources, risk, quality, communication
C4. PM² Methodology: structure, artefacts, stakeholders
C5. Applied Project: planning, execution, monitoring, closure, and benefits evaluation

Assessment is continuous and based on a combination of group and individual evaluation components.
A minimum attendance of two-thirds of the classes is required.
Assessment Components:
1. Project Management Dossier (50%) – Group Assessment
A structured document covering all phases of the project life cycle, based on the PM² methodology, including:
o Project Initiation
o Planning
o Execution and Control
o Closure
2. Final Project Presentation (20%) – Group Assessment
3. Individual Oral Discussion (20%) – Individual Assessment
4. Individual Written Reflection (10%) – Individual Assessment

Mandatory Requirements:
• Minimum grade of 9.5/20 in all assessment components
• Submission of all group project deliverables
This course unit does not include a final exam.

Mandatory

European Commission. (2024). PM² Guide - Open Edition 3.5 (ISBN: 978-92-76-60378-4). Publications Office of the European Union. https://op.europa.eu/pt/publication-detail/-/publication/97cc2f12-c648-11ee-95d9-01aa75ed71a1
Kerzner, H. (2022). Project management: A systems approach to planning, scheduling, and controlling (13th ed.). John Wiley & Sons.
Project Management Institute. (2021). A guide to the project management body of knowledge (PMBOK® guide) (7th ed.). Project Management Institute.
Rigby, D. K., Elk, S., & Berez, S. (2020). Doing agile right: Transformation without chaos. Harvard Business Review Press.
Schwaber, K. (2004). Agile project management with Scrum. Microsoft Press.

Optional

European Commission. (n.d.). PM² Project Management Methodology – Resources. European Commission. https://pm2.europa.eu/pm2-resources_en
European Commission. (n.d.). PM² Essentials: Project Management Methodology. EU Academy. https://academy.europa.eu/courses/pm-c1-pm-essentials-project-management-methodology
European Commission. (n.d.). PM² Agile Essentials. EU Academy. https://academy.europa.eu/courses/pm-a1-pm-agile-essentials
International Organization for Standardization. (2020). ISO 21502:2020—Project, programme and portfolio management: Guidance on project management. ISO.

CP1. Composition, structure and construction of the graphic form.

CP2. Vector Drawing:
- Principles and concepts;
- Drawing tools and techniques;
- Text formatting. Principles of composition and typographic rigor;
- Construction and use of grids;
- Use of vector elements.

CP3. Bitmap Painting:
- Basic notions and concepts (pixel);
- Image size, resolution and compression;
- Image Modes - Use and mastery of color channels;
- Tools and techniques for painting and drawing;
- Image creation, manipulation and treatment;
- Creation of synthetic and simulation images;
- Text composition (?Bitmap Text? vs ?Vector Text?).

CP4. Creation of static Digital Educational Resources:
- Composition of the graphic narrative in an educational and training context: relationship between concept, image, text and support;
- Graphic elements in educational interventions.

Assessment throughout the semester, in accordance with the General Regulations for the Assessment of Knowledge and Skills of ISCTE. Assessment will be continuous, with students being assessed in different dimensions, according to their involvement in the work production processes, the interest shown in the various subjects and their capacity to produce the various contents that must be requested throughout the UC. The final grade results from the weighting: 80% projects + 10% involvement in activities + 10% progression.
A minimum attendance of no less than 2/3 of the classes is required.

Mandatory

Wood, B. (2020). Adobe Illustrator: Classroom In A Book. Adobe.
Wilson, D.; Lourekas, P.; Schwartz, R. (2016). Learn Adobe Illustrator For Graphic Design And Illustration. Usa: Peachpit Press.
Lawton, R. (2016). Teach Yourself Photoshop. Bath, Uk: Future.
Graver, A.; Jura, B. (2012). Best Practices For Graphic Designers: Grids And Page Layouts. Beverly, Ma: Rockport Publishers.
Faulkner, A.; Chavez, C.; Wood, B. (2017). Learning Graphic Design And Illustration. Ny, Ny: Pearson Education.
Faulkner, A.; Chavez, C. (2020). Adobe Photoshop: Classroom In A Book. Adobe.
Elmansy, R. (2013). Illustrator Foundations: The Art Of Vector Graphics And Design In Illustrator. Ny, Ny: Focal Press.
Bailey, E. (2015) Photoshop: 20 Photo Editing Techniques Every Photoshop Beginner Should Know. Edward Bailey, 2015.
Armstrong, H. (2009). Graphic Design Theory: Readings From The Field. Ny, Ny: Princeton Architectural Press.

Optional

Hembree, R. (2011). The Complete Graphic Designer: A Guide To Understanding Graphics And Visual Communication. Beverly, Ma: Rockport Publishers.
Poulin, R. (2011). The Language Of Graphic Design: An Illustrated Handbook For Understanding Fundamental Design Principles. Beverly, Ma: Rockport Publishers.
Pender, K. (1998). Digital Colour In Graphic Design. Woburn, Ma: Focal Press.
Bierut, M. (2015). How To Use Graphic Design To Sell Things, Explain Things, Make Things Look Better, Make People Laugh, Make People Cry, And (Every Once In A While) Change The World. London, Uk: Thames And Hudson.

S1. Introduction to direct and indirect teaching methods
S2. Learning objectives
S3. Learning goals and outcomes
S4. Evidence of learning
S5. Active learning
S6. Articulation of teaching strategies
S7. Classroom observation methods
S8. Feedback
S9. Organization of the teaching-learning process

Season 1: Until the second class, the student chooses whether to be assessed throughout the semester or examination. A minimum attendance of no less than 3/4 of classes is required.
Assessment throughout the semester: Tasks (5%), with a minimum grade of 9.5, Case Study Report (60%) with a minimum grade of 9.5, and Test (35%) with a minimum grade of 8.5.
Examination Assessment: Exam (100%) with a minimum grade of 9.5.
Season 2: Exam (100%) with a minimum grade of 9.5.
Special Season: Exam (100%) with a minimum grade of 9.5.

Mandatory

Cosme, A., Lima, L., Ferreira, D., & Ferreira, N. (2021). Metodologias, métodos e situações de aprendizagem: Propostas e estratégias de ação. Porto: Porto Editora.
Belland, B. (2017). Instructional Scaffolding in STEM Education. Strategies and Efficacy Evidence. New York:Springer
Hattie, J. (2017). Aprendizagem visível para professores: como maximizar o impacto da aprendizagem. Penso Editora.
Hattie, J. (2023). Visible learning: The sequel: A synthesis of over 2,100 meta-analyses relating to achievement. Taylor & Francis.

Optional

Van Merrienboer, J. J., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational psychology review, 17, 147-177.

1. Transformations of work and employment: from the industrial society to the information and knowledge society
2. The challenge of studying work, organisations and technology
3. Rights and duties in the world of work
4. Social theories of technology: impacts, conflicts and assessments
5. Development, innovation and social inequalities
6. The future of work: prospective scenarios and dimensions of analysis
7. Responsibility and ethics in today’s workplace and organisations
8. Precarity in the digital age: concept, forms and practices of precarity
9. Trade unions and companies: participation, representation and collective bargaining
10. Digital transformation in Portugal: advancements and public policies

Assessment throughout the semester:
– Attendance and participation in classes, accounting for 10% of the final mark. A minimum attendance of ⅔ of the classes is required;
– Flipped classroom session, accounting for 20% of the final mark;
– Group assignment, accounting for a total of 35% of the final mark (15% for the written assignment and 20% for the discussion of the assignment);
– Individual written examination, accounting for 35% of the final mark.
Each assessment component has a minimum mark of 8. The final average of the various components must be 9.5 or above.

Exam-based assessment: individual written examination, accounting for 100% of the final mark, with a minimum mark of 9.5.

Mandatory

Agrawal, A., Gans, J, & Goldfarb, A. (2018). Prediction machines. Harvard Business Review Press.
Autor, D. (2022). The labour market impacts of technological change. Working Paper 30074, NBER Working Paper Series.
Benanav, A. (2020). Automation and the future of work. Verso.
Braun, J., Archer, M., Reichberg, G., & Sorondo, M. (2021). Robotics, AI and humanity. Springer.
Crawford, C. (2021). The atlas of AI. Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
Edgell, S., & Granter, E. (2020). The sociology of work: Continuity and change in paid and unpaid work. Sage Publications.
Grunwald, A. (2018). Technology assessment in practice and theory. Routledge.
Organização Internacional do Trabalho (2020). As plataformas digitais e o futuro do trabalho. International Labour Office.
Wilkinson, A., & Barry, M. (Eds.) (2021). The future of work and employment. Edward Elgar.
Zuboff, S. (2019). The age of surveillance capitalism. Public Affairs.

Optional

Casilli, A. (2021). Waiting for robots: The ever-elusive myth of automation and the global exploitation of digital labor. Sociologias 57, 112-133.
Cerejeira, J., & Lopes, H. (2023). Economia do trabalho: Mercados e instituições. Edições Almedina.
Estanque, E., Costa, H. A., Fonseca, D., & Santos, A. (2018). Digitalização e precariedade laboral: Novos desafios para o sindicalismo e os movimentos sociais no contexto português. Século XXI, 8(2), 589-621.
Eurofound - European Foundation for the Improvement of Living and Working Conditions (2020). Game-changing technologies: Transforming production and employment in Europe. Publications Office of the European Union.
Hoff, S. S., Langheinrich, M., Hoff, J., Wendehorst, C., Pfeffer, J., Fuchs, T., & Grunwald, A. (2026), The power of 10: New rules for the digital world. Communications of the Association for Computing Machinery, 69(7), 46-59.
International Labour Organisation (2019). Work for a brighter future – Global Commission on the Future of Work. International Labour Office.
Lee, H., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, Article 1121, 1-22.
Organisation for Economic Co-operation and Development (2019). How’s life in the digital age? OECD Publishing.
Standing, G. (2014), The precariat – The new dangerous class. Bloomsbury.
Wajcman, J., & Dodd, N. (2017). The sociology of speed: Digital, organizational, and social temporalities. Oxford University Press.
World Economic Forum (2025). Future of jobs report 2025. World Economic Forum.

CP1. Introduction to animation and motion design techniques and technologies.
CP2. Basic principles and concepts of animation
- Timeline
- FPS
- Keyframes
- Animation curves
- Modeling and rigging (3D animation)
CP3. Creating animated sequences
CP4. visual narratives
CP5. vector files
CP6. Pre-compositions
CP7. masks
CP8. Motion Tracking
CP9. rendering
CP10. Basic principles of sound capture, video editing and export

Assessment throughout the semester, in accordance with the General Regulations for the Assessment of Knowledge and Skills of Iscte. Assessment will be continuous, with students being assessed in different dimensions, according to their involvement in the work production processes, the interest shown in the various subjects and their capacity to produce the various contents that must be requested throughout the UC. The final grade results from the weighting: 80% projects + 10% involvement in activities + 10% progression.
A minimum attendance of no less than 2/3 of the classes is required.

Mandatory

Williams, R. (2012). The animator's survival kit: a manual of methods, principles and formulas for classical, computer, games, stop motion and internet animators. Macmillan.
Roberts, S. (2011). Introduction to Animation Working Practice, Waltham, MA, Focal Press
Meyer, C. & Meyer, T. (2010). Creating Motion Graphics with After Effects: Essential and Advanced Techniques, Version CS5.
Meyer, C. & Meyer, T. (2016). After Effects Apprentice: Real World Skills for the Aspiring Motion Graphic Artist, NY, Routledge
Love, C. (2018). Video Ideas, London, Penguin Random House
King, R. (2019). 3D Animation for the Raw Beginner Using Autodesk Maya, Boca Raton, FL, CRC Press
Green, T. & Labrecque, J. (2017). Beginning Adobe Animate, NY, Springer
Drate, S., Robbins, D., Salavetz, S., e Salavetz, J. (2006). Motion by Design. Laurence King Publishing.
Blain, J. (2016). The Complete Guide to Blender Graphics, Boca Raton, FL, CRC Press

Optional

Woolman, M. (2004). Motion Design, Moving Graphics for Television, MusicVídeo, Cinema, and Digital Interfaces, London, Rotovision
Küsters, C. & King, E. (2003). Restart: New Systems in Graphic Design, Hong Kong, Thames & Hudson.
Harvey, B. (2008). How to Make Your Own Video or Short Film, Oxford, How To Books

I. Introduction to Entrepreneurship;
II. Generation and discussion of business ideas;
III. Value Proposition Design;
IV. Business Ideas Communication;
V. Business Models Creation;
VI. Business Plans Generation;
VII. Minimum viable product (products, processes and services) test and evaluation;
VIII. Scalability analysis;
IX. Internationalization and commercialization;
X. Funding sources

Periodic grading system: - Group project: first presentation: 30%; second presentation: 30%; final report: 40%.

Mandatory

A. Osterwalder, Y. Pigneur / John Wiley & Sons, Value Proposition Design: How to Create Products and Services Customers Want, 2014, ·, ·
A. Osterwalder, Y. Pigneur / John Wiley & Sons, Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers., 2010, ·, ·
P. Burns / Palgrave Macmillan, Entrepreneurship and Small Business, 2016, ·, ·
S. Mariotti, C. Glackin / Global Edition. Pearson; Dorf. R., Byers, T. Nelson, A. (2014). Technology Ventures: From Idea to Enterprise. McGraw-Hill Education, Entrepreneurship: Starting and Operating A Small Business, 2015, ·, ·

Optional

S1 Introduction. The history of the Web. Programming languages for the Web; W3C standards.
S2 World Wide Web Architecture. Screen marking with HyperText Markup Language (HTML).
S3 Client-side programming. Structure description (HTML), style sheets (CSS) and dynamic updating of the graphical interface. Input validation; Introduction to client-side security.
S4 Server-side programming. Distribution of static content, dynamic generation of content and MVC design pattern. Services and communication between services. Introduction to server-side security.
S5 Data persistence. Integration with Database Management Systems ((SQL and NoSQL).
S6 Service-oriented web architectures. Web Services and Microservices. Middleware models for the Web. Containerization.

This course follows a semester-long project-based assessment model due to its predominantly practical nature and does not include a final exam.

Students are assessed based on the following components (A1 + A2):
A1: 3 individual multiple-choice mini-tests [30% - 10% each]. The A1 grade corresponds to the average of the 3 mini-test grades. Minimum grade of 8 out of 20.

A2: Group Project (4) and individual theoretical-practical discussion [70%]. The A2 grade corresponds to a percentage applied to the project grade, directly resulting from the performance demonstrated in the individual theoretical-practical discussion. Minimum grade of 9.5 out of 20.

Attendance:
Attendance is not mandatory.

Mandatory

Dean, J. (2025). Web programming with HTML5, CSS, and JavaScript. Jones & Bartlett Learning, 2nd Edition. ISBN: ‎ 9781284302967.

Percival, H., & Gregory, B. (2020). Architecture Patterns with Python: Enabling Test-Driven Development, Domain-Driven Design, and Event-Driven Microservices. O'Reilly Media. ISBN: 9781492052203

Richardson, L., & Amundsen, M. (2013). RESTful Web APIs. O'Reilly Media. ISBN: 9781449358068.

Mozilla Foundation. (n.d.). MDN Web Docs. https://developer.mozilla.org/

Ramírez, S. (n.d.). FastAPI documentation. https://fastapi.tiangolo.com/

Docker Inc. (n.d.). Docker documentation. https://docs.docker.com/

Optional

Fielding, R. T. (2000). Architectural styles and the design of network-based software architectures (Doctoral dissertation, University of California, Irvine). https://ics.uci.edu/~fielding/pubs/dissertation/top.htm

Haverbeke, M. (20). Eloquent JavaScript: A Modern Introduction to Programming (4th ed.). No Starch Press. ISBN: 9781593279509. Available at https://eloquentjavascript.net/

Martin, R. C. (2025). Clean Code: A Handbook of Agile Software Craftsmanship. Prentice Hall. 2nd Edition. ISBN: 9780135398579.

PocketBase Authors. (n.d.). PocketBase Documentation. https://pocketbase.io/docs/

React Team. (n.d.). React Documentation. https://react.dev/

MariaDB Foundation. (n.d.). MariaDB Server Documentation. https://mariadb.com/docs/

MongoDB, Inc. (n.d.). MongoDB Documentation. https://www.mongodb.com/docs/

World Wide Web Consortium. (2004). Architecture of the World Wide Web, Volume One. W3C Recommendation. https://www.w3.org/TR/webarch/

S1: Training Concepts, Types and Modalities
S2: Digital Training: Tools and Platforms
S3: Qualification Systems and Competency Frameworks
S4: The Training Cycle
S5: Training Accreditation: Standards and Certification

A1: Individual tasks throughout the semester, following the criteria defined by the instructor, account for 30% of the final grade, with a minimum passing grade of 7.5.
A2: Assessment by project, following the General Regulation for the Assessment of Knowledge and Competencies at ISCTE, which includes a report that justifies all stages of the training cycle. The group Project carries a weight of 50% in the final grade, with a minimum passing grade of 7.5. Individual discussion of the project on the regular period counts for 20% of the final grade, with a minimum passing grade of 7.5.
Minimum attendance of no less than 3/4 of classes is required.

Second and Special Period: Individual project (60%) and following discussion (40%), with a minimum passing grade of 7.5 in each component.

The final average grade must be equal to or higher than 9.5.
There is no exam assessment in this course unit.

Mandatory

Affleck, M., People Learning and Development - Transforming people and organizations through learning, 2021, Kwantlen Polytechnic University, Surrey, BC, Canada., https://kpu.pressbooks.pub/peoplelearningdevelopment/
Dias, A. e Rocha, A., Referencial de Formação Pedagógica Contínua do Formador a Distância (e-Formador), 2018, IEFP e TecMinho, Lisboa, http://www.panoramaelearning.pt/wp-content/uploads/2020/10/Referencial-FPCFaD.pdf
Kirkpatrick, Donald L., Kirkpatrick, James D., Evaluating training programs: the four levels (3rd ed), 2006, San Francisco : Berrett-Koehler,
Muramatsu, B. and Ludgate, H. Authors and Contributors: Adams Becker, S., Caswell, T., Jensen, M., Ulrich, G., and Wray, E., Online Course Design Guide, 2014, Cambridge, Massachusetts: Massachusetts Institute of Technology, https://dltoolkit.mit.edu/online-course-design-guide

Optional

European Commission, Directorate-General for Education, Youth, Sport and Culture, Key competences for lifelong learning, 2019, Publications Office, https://data.europa.eu/doi/10.2766/569540
Vuorikari, R., Kluzer, S. and Punie, Y., DigComp 2.2: The Digital Competence Framework for Citizens, 2022, EUR 31006 EN, Publications Office of the European Union, Luxembourg, ISBN 978-92-76-48882-8, doi:10.2760/115376, JRC128415, https://publications.jrc.ec.europa.eu/repository/handle/JRC128415
Ministério do Trabalho, Solidariedade e Segurança Social, Guia da Certificação de Entidades Formadoras Sistema e Requisitos de Certificação, 2017, Direcção-Geral do Emprego e das Relações de Trabalho. Direcção de Serviços de Qualidade e Acreditação.
Learning and Development: A Comprehensive Guide, AIHR https://www.aihr.com/blog/learning-and-development/

S1: Introduction, Program, and Assessment. Computing and HCI: History, state-of-the-art, and applications
S2: User-centered design process.
S3: User and task analysis. Empathy map. Personas. User "as is" scenarios and journeys. User questions. User requirements.
S4: We, the Humans
S5: Principles and golden rules of interface design. Usability
S6: Visual design of screens (WWW, mobility)
S7: Ideation. Storyboards. Prioritization. Low-fidelity (Lo-Fi) and high-fidelity (Hi-Fi) prototypes of the solution
S8: Deliver a functioning solution. Heuristic evaluation with experts. User evaluation. Statistical analysis of evaluation data. Calculate metrics and iterate the design. Requirements of an MVP (Minimum Viable Product). Elevator Pitch to investors and users

Given its eminently practical and applied nature, this curricular unit follows a project-based model — Assessment Throughout the Semester — as provided for in the RGACC, Iscte’s General Regulations for the Assessment of Knowledge and Competences. It does not include assessment by final examination and comprises the following assessment components:

Mini-test 1 – 15%
Mini-test 2 – 15%
D1 Project Proposal Document – 4%
D2 User Requirements Document – 12%
D3 Version 1 Low-Fidelity Paper Prototype (Lo-Fi) – 10%
D4 Expert Evaluation Report on the Lo-Fi Prototype – 12%
D5 High-Fidelity Prototype (Hi-Fi) and Report – 10%
D6 User Evaluation Report on the Hi-Fi Prototype – 11%
D7 Oral Presentation of the Project – 11%

Mandatory minimum attendance in 2/3 of classes.

Use of artificial intelligence. The Guidelines for the Responsible Use of Artificial Intelligence in Academic Work (Scientific Council, 21 April 2026) apply, under the following specific arrangements: the use of artificial intelligence is permitted in project deliverables and must be declared in accordance with Section 4 of the Guidelines, including completion of the attached form; its use is prohibited in tests and practical assessments conducted in class, as well as in any repeat assessment of these components.
For any assessment component, the lecturer or curricular unit coordinator may, at any time during the semester, request an individual oral discussion to confirm the authorship of the work submitted and/or assess the knowledge and competences demonstrated by the student.
The assessment includes individual oral verification components in which students are required to explain and modify the work submitted.

To pass the curricular unit, students must achieve a minimum final mark of 10 out of 20, calculated as the weighted sum of all assessment components.

A student will fail this curricular unit if they do not pass the Assessment Throughout the Semester (ALS).

In the second Exam period, mini-tests with a score of ≤7.5 must be retaken. If the student fails the regular period (score <10), is eligible to retake the 2 mini-tests in the 2nd or special seasons (30% of the grade). It ismandatory to pass the group project (70% of the grade).

Mandatory

Brown, T (2009), Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation, HarperCollins, 2009, ISBN-13: 978-0062856623
Lewrick, M, Link, P., Leifer, L. (2020). The Design Thinking Toolbox, Wiley, ISBN 9781119629191
Shneiderman, B., Plaisant, C., Cohen, M., Jacobs, S., Elmqvist, N., Nicholas Diakopoulos, N. (2017). Designing the User Interface: Strategies for Effective Human-Computer Interaction (6th edition), Pearson, ISBN-13: 978-0134380384
Manuel J. Fonseca, Pedro Campos, Daniel Gonçalves (2017), Introdução ao Design de Interfaces, FCA, Portugal, 2017, 3ª Edição,
Norman, D. (2013). The Design of Everyday Things, Revised and Expanded Edition. MIT Press. ISBN: 9780262525671
Nielsen, J., Mack, R. (1994). Usability Inspection Methods 1st Edition. John Wiley & Sons.

Optional

Johnson, J. & Henderson, A. (2002). Conceptual models: begin by designing what to design. Interactions. 9, 1: 25-32. https://dl.acm.org/doi/10.1145/503355.503366
Joseph J. LaViola Jr., Ernst Kruijff, Ryan P. McMahan, Doug Bowman, Ivan P. Poupyrev (2017), 3D User Interfaces: Theory and Practice (2nd Edition), Addison-Wesley Professional, ISBN-10: 0134034325.
Yvonne Rogers, Helen Sharp, Jenny Preece (2011), Interaction Design: Beyond Human-Computer Interaction, 3rd edition, Wiley, ISBN-13: 978-0470665763
? Snyder, C. (2003). Paper Prototyping: the fast and easy way to design and refine user interfaces. Morgan Kaufmann Publishers.
The Basics of User Experience Design by Interaction Design Foundation, https://www.interaction-design.org/
Artigos:
Nielsen, J. (1994) Enhancing the explanatory power of usability heuristics. Proc. ACM CHI'94 Conf. (Boston, MA, April 24-28), pp. 152-158.
Rettig M. (1994), Prototyping for Tiny Fingers, Communications of The ACM, 1994

PC1. Theoretical foundations of the curriculum
- What is the curriculum;
- Dimensions associated with the curriculum;
- Curriculum theories;
- Agents and structures: from local to global.
PC2. Curriculum Development
- What is Curriculum Development?
- Curriculum management;
- Curriculum development models;
- Curricular innovation;
- Curricular Assessment (objectives and objects of assessment, internal/external/mixed, assessment methods and techniques).
PC3. Curricular Planning in Education and Training
- Basic and secondary education;
- Professional education;
- University education.

Students decide by the 2nd class the method of assessment for the 1st period, which can be: assessment throughout the semester or final exam. In the assessment modality throughout the semester, a minimum attendance of no less than 2/3 of classes is required.
In the assement throughout semester modality (1st season) students:
- 2 tests (40%), with a minimum grade of 8.5;
- carry out group research work during the semester (60%).
In the final exam modality (available for 1st and 2nd season and special season), the in-person final exam corresponds to 100% of the final course grade.

Mandatory

Chaplowe, S.G. & Cousins, J.B. (2017). Monitoring and Evaluation Training: A Systematic Approach. Thousand Oaks: Sage.
Duarte, P. (2021). Pensar o desenvolvimento curricular: uma reflexão centrada no ensino. Porto: Instituto Politécnico do Porto/Escola Superior de Educação. Acessível em https://recipp.ipp.pt/handle/10400.22/19104
Formosinho, J. & Pascal, C. (2016). Assessment and Evaluation for Transformation in Early Childhood. Londres: Routledge.
Roldão, M., & Almeida, S. (2018). Gestão Curricular – Para a autonomia das escolas e professores. Direção-Geral da Educação.

Optional

CP 1. Introduction to databases and information systems.

CP 2. Relational Model and data normalization.

CP 3. SQL language: data definition; data manipulation; programming routines and triggers.

CP 4. Data modeling with the UML language.

CP 5. Database design by converting UML to Relational Model;

CP 6. Transactions and competition.

CP 7. Introduction to database administration.

Assessment throughout the semester:

· 4 individual tests, each worth 20% of the student's final grade. Duration of each test: 1 hour. The last test is simultaneously with the 1st season exam.

· 20 individual worksheets carried out in the classroom, together worth 20% of the grade. There are 2 forms per class, one at the beginning and one at the end, except for the first and last classes.
There is no minimum grade. The student's grade is the weighted average of all assessments.

Assessment by exam:

· Individual written test, lasting 3 hours, worth 100% of the grade.

All tests are individual and to be carried out without external help, particularly without artificial intelligence. In case of suspicion of non-compliance with this rule, the student is called to take an oral test. In this case, if it appears that external help has been used, the test will be canceled and the incident will be reported to the Pedagogical Council.

Mandatory

Documentação da cadeira.
Ramos, P. “Desenhar Bases de Dados com UML, Conceitos e Exercícios Resolvidos”, Editora Sílabo, 2ª Edição, 2007.
Damas, L. “SQL - Structured Query Language”, FCA Editora de Informática, 14ª Edição, 2017.

Optional

Blaha, M.: "UML Database Modeling Workbook", Technics Publications, LLC, 2014.
Churcher, C., “Beginning Database Design: From Novice to Professional”, 2ª edição, Apress. 2012.
Ramakrishnan, R., Gehrke, J. “Database Management Systems”, 3ª edição, McGraw-Hill, 2003.

I. Development of the product/service image
II. Functionalities of the product/service
III. Development plan
IV. Development of the product/service (web/mobile or other)
V. Revision of the business plan
VI. Management and maintenance of the product/service
VII. Certification plan
VIII. Intellectual property, patents, and support documentation
IX. Main aspects for the creation of a startup - juridical, account, registry, contracts, social capital, obligations, taxes

Periodic grading system:
- Group project: first presentation: 30%; second presentation: 30%; final report: 40%. The presentations, demonstrations and Defence are in group.

Mandatory

A. Osterwalder, Y. Pigneur / John Wiley & Sons, Value Proposition Design: How to Create Products and Services Customers Want, 2014, ·, ·
A. Osterwalder, Y. Pigneur / John Wiley & Sons, Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers, 2010, ·, ·
P. Burns / Palgrave Macmillan, Entrepreneurship and Small Business, 2016, ·, ·
R. Dorf, T. Byers, A. Nelson / McGraw-Hill Education, Technology Ventures: From Idea to Enterprise., 2014, ·, ·
S. Mariotti, C. Glackin / Global Edition. Pearson, Entrepreneurship: Starting and Operating A Small Business, 2015, ·, ·

Optional

·

SC1. Introduction to Cybersecurity: main components; cybersecurity pillars; cybersecurity frameworks.
SC2. Information Security Planning and Legal and Ethical Framework
SC3. Principles of Information Security Governance and Risk Management
SC4. Introduction to Information Security Technology: access controls, firewalls, vpns, idps, cryptography and other techniques.
SC5. Physical Security: physical access control mechanisms, physical security planning, among others.
SC6. Information Security Implementation: information security project management; technical and non-technical aspects of information security implementation.
SC7. Personnel Security: personnel security considerations; personnel security practices.
SC8. Maintenance of Information Security.

Assessment throughout the semester:
- Completion of a set of group projects and activities (50%) throughout the semester
- Completion of a set of individual mini-tests throughout the semester (50%) [minimum grade of 8 for each test]
For assessment throughout the semester, minimum attendance of 70% of classes is required.
Exam assessment:
For students who choose this process or who fail the assessment process throughout the semester, with 3 periods in accordance with the RGACC.

Mandatory

Whitman, M., Mattord, H. (2021). Principles of Information Security. Course Technology.
Whitman, M., & Mattord, H. (2016). Management of information security. Nelson Education.
Andress, J. (2014). The Basics of Information Security: Understanding the Fundamentals of InfoSec in Theory and Practice. Syngress.
Kim, D., Solomon, M. (2016). Fundamentals of Information Systems Security. Jones & Bartlett Learning.

Optional

Conjunto de artigos, páginas web e textos que complementam a informação bibliográfica da unidade curricular, e que serão fornecidos pela equipa docente.

PC1. Characterization and selection of platforms for management and distribution of learning content
- Learning Management Systems (LMS)
- Content Management Systems (CMS)
- Virtual Learning Environments (VLE)
PC2. Employment of LMS platforms
- Installation
- Configuration
- Management
- Customization
- Content creation and management
PC3. Development of distante learning courses
- Pedagogical design
- Instructional design models
- Planning and development of activities and learning and evaluation resources
PC4. Production and assessment of e-content: context, content, functionality and relevance

A1: Individual tasks during the semestre, according to the criteria defined by the lecturer, contribute to 30% of the final grade, with a minimum grade of 7,5 points.
A2: Groupwork Project that contributes to 70% of the final grade, including a report that explains all project phases, according to the following steps:
a) Configuration and personalization of a LMS (35%);
b) Design and development of a course (35%);
c) Individual discussion in the regular period (30%).

A minimum grade of 7,5 points is required in each component (A1 and A2), and also in items a), b) and c) of the project. The total average to pass the CU needs to be equal to or more than 9,5 points.
Minimum attendance of no less than 3/4 of classes is required.

Second and Special Period: individual project (100%)

Mandatory

Kasim, N. N. M., & Khalid, F. (2016). Choosing the Right Learning Management System (LMS) for the Higher Education Institution Context: A Systematic Review. International Journal of Emerging Technologies in Learning, 11(6).
Dillenbourg, P. (2000). Virtual learning environments. Proceedings of EUN Conference 2000, Learning in the New Millennium: Building New Education Strategies for Schools. Workshop on Virtual Learning Environments. Geneva.
Nash, S. & Rice, W. (2018). Moodle 3 E-Learning Course Development - Fourth Edition. Birmingham: Packt Publishing
Bates, A. W. (2022). Teaching in a digital age: Guidelines for designing teaching and learning (3rd ed.). Tony Bates Associates Ltd. https://pressbooks.bccampus.ca/teachinginadigitalagev3m/
Clark, R. C., & Mayer, R. E. (2016). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. New Jersey: John Wiley & Sons.

Optional

Krouska, A., Troussas, C., & Virvou, M. (2017). Comparing LMS and CMS platforms supporting social e-learning in higher education. In 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-6). IEEE.
Malasri, S. (2000). The McGraw-Hill Handbook of Distance Learning. Journal of Professional Issues in Engineering Education and Practice, 126(1), 41-42.
Denmeade, N. (2015). Gamification with Moodle. Birmingham: Packt Publishing
Buchener, A. (2016). Moodle 3 Administration - Third Edition: An administrator's guide to confi guring, securing, customizing, and extending Moodle 3rd Edition. Birmingham: Packt Publishing
Nash, S. (2018). Moodle Course Design Best Practices - Second Edition. Birmingham: Packt Publishing
High Quality Online Courses: Subtitle: How to Improve Course Design & Delivery for your Post-Secondary Learners, by University of Waterloo; Queen's University; University of Toronto; and Conestoga College, CC-BY-NC-SA 4.0 https://ecampusontario.pressbooks.pub/hqoc

S1: Analysis and diagnosis of training and technological needs in business contexts.
S2: Development and implementation of digital educational solutions in response to business requirements.
S3: Agile project management methodologies (Scrum, Kanban) applied to educational technology projects.
S4: Professional communication strategies and presentation of results to stakeholders.
S5: Integration of emerging technologies (AI, augmented reality, data analysis) into business educational solutions.
S6: Impact assessment of educational technology projects.
S7: Technical documentation and professional report writing.

Project-based assessment, in accordance with Iscte’s General Regulations for the Assessment of Knowledge and Competencies, distributed as follows:
- Weekly monitoring (30%) – assesses engagement in class, attendance, weekly tasks, and the progress of the project.
- Final deliverables (40% – may include a report, article, poster, among other formats) – assesses clarity, methodological rigour, and the quality of the analysis developed.
- Project defence (30%) – assesses the ability to communicate and discuss the work carried out.
A minimum attendance of at least three-quarters of the classes and participation in scheduled meetings with partner companies is required. Each assessment component has a minimum passing grade of 8.5 out of 20. The final grade must be at least 9.5 out of 20. This course unit does not include an examination-based assessment.

Mandatory

Brown, T. (2009). Change by design: How design thinking transforms organizations and inspires innovation. HarperCollins.
Lewrick, M., Link, P., & Leifer, L. (2020). The design thinking toolbox. Wiley.
Knapp, J., Zeratsky, J., & Kowitz, B. (2016). Sprint: How to solve big problems and test new ideas in just five days. Bantam Press.

Optional

Ries, E. (2017). The lean startup: How today's entrepreneurs use continuous innovation to create radically successful businesses (capítulos 3 e 4). Penguin Group.
Scrum Institute. (2020). The Kanban Framework (3ª ed.). Recuperado em fevereiro de 2023, de www.scrum-institute.org/contents/The_Kanban_Framework_by_International_Scrum_Institute.pdf
Rigby, D., Elk, S., & Berez, S. (2020). Doing agile right: Transformation without chaos. Harvard Business Review Press.
Sutherland, J., & Sutherland, J. J. (2014). Scrum: The art of doing twice the work in half the time. Crown Business.
Project Management Institute. (2017). A guide to the project management body of knowledge (PMBOK® guide) (6ª ed.). Project Management Institute.
Gwaldis, M. (2019). How to conduct a successful pilot: Fail fast, safe, and smart. Recuperado em fevereiro de 2023, de https://blog.shi.com/melissa-gwaldis/

S1: Main types of disability: visual, auditory, motor and cognitive
S1.1: Special Education Disabilities
S2: Universal design: principles and applications in learning.
S2.1: Inclusive design practices
S3: Functional challenges of assistive technology
S4: Accessibility applied to digital content: text, audio, complex images, videos, graphics and tables
S4.1: Document conversion, subtitling, audio description, accessible document design.
S5: Accessible and inclusive digital content for web in educational context: definition of usability goals and measures, content control, writing of customized accessible components

Due to the practical nature of the CU, and the work carried out in connection with a real context, there is only assessment throughout the semester, with no final exam.

At the beginning of the semester, which covers the main types of disabilities, special educational needs and universal and inclusive design, there will be a written test, weighted at 30%, to assess the initial theoretical content.

Subsequently, there will be a second assessment, based on the collection and processing of data from a visit made in the context of the CU to a school that is a benchmark in accessibility and inclusion, weighted at 20%.

Finally, the third assessment stage refers to the final project, in which students will have to develop an educational resource based on all the knowledge they have acquired during the semester, with a weighting of 50%.

Students cannot have a grade lower than 8.5 in any assessment element, and the final average must be higher than 9.5. A supplementary oral assessment may be carried out in addition to any assessment method or to validate the final grade.

A minimum attendance of no less than 2/3 of classes is required.

There is no exam at any time of the year; the final assessment takes place during the 1st semester on a continuous assessment basis throughout the semester.

Mandatory

Nielsen, J. (2006). Prioritizing Web Usability. New Riders Press
Halder, S., & Argyropoulos, V. (Eds.). (2019). Inclusion, equity and access for individuals with disabilities: Insights from educators across world. Springer.
Ghosh, S. C. (2017). Technology for Inclusion Special Education, Rehabilitation, for All. Linus Learning.
Gilbert, R. M. (2019). Designing with Accessibility in Mind. In Inclusive Design for a Digital World (pp. 1-20). Apress, Berkeley, CA.
Firth, A. (2019). Practical web inclusion and accessibility: A comprehensive guide to access needs. Apress.

Optional

World Health Organization. (2011). Relatório mundial sobre a de ciência (World report on disability 2011). World Health Organization. Tradução Lexicus Serviços Lingüísticos. - São Paulo : SEDPcD, 2012. 334 p. Disponível em: https://apps.who.int/iris/bitstream/handle/10665/70670/WHO_NMH_VIP_11.01_por.pdf?sequence=9
Ana Paula Lombardi (org.). (2019) Ergonomia e acessibilidade. Disponível em: https://atenaeditora.com.br/index.php/catalogo/ebook/ergonomia-e-acessibilidade

S1: Essential AI concepts: definition, historical evolution, and multidisciplinary relevance.
S2: Predicate logic and knowledge-based systems: representation, deduction, and applications.
S3: Reasoning methods and heuristics: informed and uninformed search, selecting suitable strategies.
S4: Introduction to machine learning: basic notions, classification and regression algorithms, evaluation metrics.
S5: Simple predictive models: design, implementation, and results analysis in real-world scenarios.
S6: Generative AI: foundations, prompt engineering, and creative applications.
S7: Critical approach to AI limitations, ethical challenges, and socio-economic implications.

The assessment of this curricular unit follows the model of Assessment Throughout the Semester (ALS), provided for in the RGACC, and includes several moments, organized into three assessment blocks (AB), aimed at measuring progress and consolidating knowledge. It is made up as follows:
AB1: Written Exam (40%)
Assesses the fundamental concepts of artificial intelligence, machine learning, and generative AI. The exam evaluates both theoretical understanding and the ability to apply the concepts in practice.

AB2: Final Project in AI (60%)
Conducted individually or in groups, this project involves the design, implementation, and evaluation of a prototype or case study based on the course content. It encourages integration of knowledge and critical reflection on the topics covered. Evaluation is divided into a written report (60%) and an oral presentation (40%), delivered during the final session.

Additional rules:
Each assessment block requires a minimum score of 8.5 points.
If necessary, an individual oral discussion may be conducted to confirm knowledge.
The final course grade is the weighted sum of both blocks, with a minimum of 10 points required to pass.

Mandatory

Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
→ Considerado a "bíblia" da IA, cobre desde fundamentos históricos até algoritmos modernos.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
→ Introdução acessível e aprofundada ao deep learning.

Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.
→ Livro clássico sobre fundamentos de aprendizagem de máquina.

Domingos, P. (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books.
→ Introdução mais popular, sem fórmulas, ideal para contexto geral.

Chollet, F. (2021). Deep Learning with Python (2nd ed.). Manning.
→ Didático, foca em exemplos práticos com Keras/TensorFlow.

Optional

Haenlein, M., Kaplan, A., Tan, C. T., Tan, B. C., & Zhang, P. (2019). Artificial Intelligence (AI) and Management Analytics. Journal of the Academy of Marketing Science, 48(3), 411–431.

Jordan, M. I., & Mitchell, T. M. (2015). Machine Learning: Trends, Perspectives, and Prospects. Science, 349(6245), 255–260.

Topol, E. (2019). High-performance Medicine: The Convergence of Human and Artificial Intelligence. Nature Medicine, 25, 44–56.

S1. The digital transformation as a new civilizational paradigm.
S2. The impact of digital technologies on the economy.
S3. The impacts of digital technologies on work.
S4. The impact of digital technologies on inequalities.
S5. The impacts of digital technologies on democracy.
S6. The impacts of digital technologies on art.
S7. The impacts of digital technologies on individual rights.
S8. The impacts of digital technologies on human relations.
S9. The impacts of digital technologies on the future of humanity.
S10. Responsible Artificial Intelligence.
S11. The impact of quantum computing on future technologies.
S12. The impact of digital technologies on geopolitics.

The assessment process includes the following elements:

A) Ongoing assessment throughout the semester
A1. Group debates on issues and problems related to each of the program contents. Each group will participate in three debates throughout the semester. The performance evaluation of each group per debate will account for 15% of each student's final grade within the group, resulting in a total of 3 x 15% = 45% of each student's final grade.
A2. Participation assessment accounting for 5% of each student's final grade.
A3. Final test covering part of the content from the group debates and part from the lectures given by the instructor, representing 50% of each student's final grade.
A minimum score of 9.5 out of 20 is required in each assessment and attendance at a minimum of 3/4 of the classes is mandatory.

B) Final exam assessment Individual written exam, representing 100% of the final grade.

Mandatory

Chalmers, D. (2022). Adventures in technophilosophy In Reality+ - Virtual Worlds and the problems of Philosophy (pp. xi-xviii). W. W. Norton & Company.
Chin, J., Lin, L. (2022). Dystopia on the Doorstep In Deep Utopia – Surveillence State – Inside China’s quest to launch a new era of social control (pp. 5–11). St. Martin’s Press.
Dignum, V. (2019). The ART of AI: Accountability, Responsibility, Transparency In Responsible Artificial Intelligence - How to Develop and Use AI in a Responsible Way (pp. 52–62). Springer.
Howard, P. N. (2020). The Science and Technology of Lie Machines In Lie Machines - How to Save Democracy from Troll Armies, Deceitful Robots, Junk News Operations, and Political Operatives (pp. 1-4; 6-7; 10-18). Yale University Press.
Kearns, M., Roth, A. (2020). Introduction to the Science of Ethical Algorithm Design In The Ethical Algorithm - The Science of Socially Aware Algorithm Design (pp. 1-4; 6-8; 18-21). Oxford University Press.

Optional

(Principal - continuação)
Kissinger, H. A., Schmidt, E., Huttenlocher, D (2021). Security and World Order In The Age of AI - And Our Human Future (pp. 157–167, 173-177). John Murray Publishers.
Parijs, P. V., Vanderborght, Y. (2017). Ethically Justifiable? Free Riding Versus Fair Shares In Basic Income - A Radical Proposal for a Free Society and a Sane Economy (pp. 99–103). Harvard University Press.
Pentland, A. (2014). From Ideas to Actions In Social Physics – How good ideas spread – The lessons from a new science (pp. 4–10). The Penguin Press.
Zuboff, S. (2021). O que é capitalismo de vigilância? In A Era do Capitalismo de Vigilância - A luta por um futuro humano na nova fronteira de poder (pp. 21–25). Intrínseca.

***
(Complementar)
Acemoglu, D.; Johnson, S. (2023). What Is Progress? In Power and progress: our thousand-year struggle over technology and prosperity (pp. 1 - 7). PublicAffairs.
Bostrom, N. (2024). The purpose problem revisited In Deep Utopia – Life and meaning in a solved world (pp. 121–124). Ideapress Publishing.
Castro, P. (2023). O Humanismo Digital do século XXI e a nova Filosofia da Inteligência Artificial In 88 Vozes sobre Inteligência Artificial - O que fica para o homem e o que fica para a máquina? (pp. 563 – 572). Oficina do Livro/ISCTE Executive Education.
Gunkel, D. J. (2012). Introduction to the Machine Question In The Machine Question - Critical Perspectives on AI, Robots, and Ethics (pp. 1-5). The MIT Press.
Innerarity, D. (2023). O sonho da máquina criativa. In Inteligência Artificial e Cultura – Do medo à descoberta (pp. 15 – 26). Colecção Ciência Aberta, Gradiva.
Jonas, H. (1985). Preface to the English version of the Imperative of Responsibility In The Imperative of Responsibility: In Search of an Ethics for the Technological Age. (pp. ix - xii). University of Chicago Press.
Nakazawa, H. (2019). Manifesto of Artificial Intelligence Art and Aesthetics In Artificial Intelligence Art and Aesthetics Exhibition - Archive Collection (p. 25). Artificial Intelligence Art and Aesthetics Research Group (AIAARG).
Patel, N. J. (2022, february 4). Reality or Fiction - Sexual Harassment in VR, The Proteus Effect and the phenomenology of Darth Vader — and other stories. Kabuni. https://medium.com/kabuni/fiction-vs-non-fiction-98aa0098f3b0
Pause Giant AI Experiments: An Open Letter. (22 March, 2023). Future of Life Institute. Obtido 26 de agosto de 2024, de https://futureoflife.org/open-letter/pause-giant-ai-experiments/

CP1: Introduction to Business Intelligence – concepts, architecture, and tools.
CP2: Data integration and transformation – Power Query, M, connections to multiple sources.
CP3: Data modeling – relational tables, DAX, measures, and KPIs.
CP4: Data visualization – creating reports, dashboards, storytelling with data.
CP5: Assessment and best practices in BI projects – data ethics, governance, information quality.

Assessment throughout the semester includes:

- Practical project for developing an analytical information system carried out in a group (maximum of 3 students), including a report (15%) with a mandatory midterm submission (15%) and an oral defense with an individual grade (20%). Minimum grade of 8 points.
- Individual exam (30%) to assess theoretical knowledge.
- Weekly in-class and independent assignments (20%).

The use of AI tools (e.g., ChatGPT) is permitted for technical support and must be clearly referenced with the prompts used.

The Final Exam is a written, individual, closed-book exam covering all the material.

Those who do not opt ​​for assessment throughout the semester take the final exam in Period 1.

Those who did not successfully complete the assessment throughout the semester, or the exam in Period 1, with a score greater than or equal to 9.5 (out of 20), take the final exam in Period 2.

Mandatory

Greg Deckler, Learn Microsoft Power BI: A comprehensive, beginner-friendly guide to real-world business intelligence (2025)

R. Kimball, M. Ross The Data Warehouse Toolkit - the definitive guide to dimensional modeling, 3rd Edition. John Wiley & Sons, USA, (2013)

Optional

Será disponibilizada consoante os interesses dos estudantes e atualizações tecnológicas relevantes.

S1: Analysis and diagnosis of training and technological needs in business contexts.
S2: Development and implementation of digital educational solutions in response to business requirements.
S3: Agile project management methodologies (Scrum, Kanban) applied to educational technology projects.
S4: Professional communication strategies and presentation of results to stakeholders.
S5: Integration of emerging technologies (AI, augmented reality, data analysis) into business educational solutions.
S6: Impact assessment of educational technology projects.
S7: Technical documentation and professional report writing.

Project-based assessment, in accordance with Iscte’s General Regulations for the Assessment of Knowledge and Competencies, distributed as follows:
Individual weekly logbook (documentation of progress and reflections) – 5%
Technical report (comprehensive project documentation) with discussion (individual and group) – 70%
Developed product/solution (technical quality and relevance to identified needs) – 25%
A minimum attendance of at least three-quarters of the classes and participation in scheduled meetings with partner companies is required. Each assessment component has a minimum passing grade of 8.5 out of 20. The final grade must be at least 9.5 out of 20. This course unit does not include an examination-based assessment.

Mandatory

Brown, T. (2009). Change by design: How design thinking transforms organizations and inspires innovation. HarperCollins.
Lewrick, M., Link, P., & Leifer, L. (2020). The design thinking toolbox. Wiley.
Knapp, J., Zeratsky, J., & Kowitz, B. (2016). Sprint: How to solve big problems and test new ideas in just five days. Bantam Press.

Optional

Ries, E. (2017). The lean startup: How today's entrepreneurs use continuous innovation to create radically successful businesses (capítulos 3 e 4). Penguin Group.
Scrum Institute. (2020). The Kanban Framework (3ª ed.). Recuperado em fevereiro de 2023, de www.scrum-institute.org/contents/The_Kanban_Framework_by_International_Scrum_Institute.pdf
Rigby, D., Elk, S., & Berez, S. (2020). Doing agile right: Transformation without chaos. Harvard Business Review Press.
Sutherland, J., & Sutherland, J. J. (2014). Scrum: The art of doing twice the work in half the time. Crown Business.
Project Management Institute. (2017). A guide to the project management body of knowledge (PMBOK® guide) (6ª ed.). Project Management Institute.
Gwaldis, M. (2019). How to conduct a successful pilot: Fail fast, safe, and smart. Recuperado em fevereiro de 2023, de https://blog.shi.com/melissa-gwaldis/

PC1: Introduction to Data
* Definition and key concepts
PC2: Data Challenges in education sector
PC3: AI Foundations for Knowledge Extraction
PC4: AI Techniques for Data Analysis
PC5: Ethical and Legal Considerations
PC6: Effective Communication of Findings
PC7: Collaborative AI Projects in Education
PC8: Tools and Libraries
PC9:AI applications in education
PC10: Project Presentation and Reflection

Assessment throughout the semester (only available in the first term):
3 assignments throughout the semester and a test at the end: the assignments are done in groups, one in class and the others independently. The class work represents 10% of the final grade, and the independent work 20% each, with a minimum grade of 9.5. The test represents 50% of the grade, with a minimum mark of 8. The average grade must be equal to or higher than 9.5 for each of the assignments.
The marks for the assignments may vary depending on individual performance in an oral discussion, which will take place (for the group) if the mark (of one of the members) between the test and the assignment differs by more than 3 marks.

A minimum attendance of no less than 2/3 of classes is required.

Given the practical nature of the course, there is no exam in the first term.

If the student did not have the minimum grade of 8 in the test, it is possible to recover the test in the second term.

Mandatory

Wes Mckinney, Python for Data Analysis, 2e: Data Wrangling with Pandas, NumPy, and IPython - Publisher: O'Reilly Media; Publication date: November 2017
Chris Albon, Machine Learning with Python Cookbook: Practical solutions from preprocessing to deep learning - Publisher: O'Reilly Media; Publication date: May 2018
Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. - Publisher: O'Reilly Media; Publication date: September 2019
Daniel Nelson, Data Visualization in Python - Publisher: ‎ Independently; Publication date: September 2020
O'Reilly, T. (2010). Open Government: Collaboration, Transparency, and Participation in Practice - Publisher: O'Reilly Media; Publication date: February 2010
OpenCV: Open Source Computer Vision Library. (https://opencv.org/)
TensorFlow: An Open Source Machine Learning Framework for Everyone. (https://www.tensorflow.org/)

Optional

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education. Boston: Center for Curriculum Redesign.
Furey, H., & Martin, F. (2019). AI education matters: A modular approach to AI ethics education. AI Matters, 4(4), 13-15.
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. Ieee Access, 8, 75264-75278.
Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial Intelligence trends in education: a narrative overview. Procedia Computer Science, 136, 16-24.
Nkambou, R., Mizoguchi, R., & Bourdeau, J. (Eds.). (2010). Advances in intelligent tutoring systems (Vol. 308). Springer Science & Business Media.