LO1: To know the main concepts related to Learning Psychology.
LO2: To understand the main psychological processes associated with academic success as well as learning difficulties.
LO3: To analyze the teaching-learning process in the light of theoretical proposals from Learning Psychology.
LO4: To explore the school context as a primary field of intervention for Learning Psychology.

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.

LO1. Differentiate between direct and indirect teaching methods
LO2. Develop learning objectives based on content and desired outcomes
LO3. Establish learning goals and outcomes from overarching questions
LO4. Identify evidence of learning
LO5. Implement active and blended learning strategies
LO6. Apply classroom observation methods
LO7. Categorize types of feedback
LO8. Create and adapt teaching strategies considering diverse individual and collective student needs

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.

At the end of this course unit, students should be able to:
LO1. Define requirements for a technological project using a user-centred design approach.
LO2. Plan, manage, and execute the project using an agile project management methodology based on sprints.
LO3. Work effectively and efficiently in an interdisciplinary team, being able to resolve conflicts and define the various roles within the group, including leadership roles.
LO4. Develop the technological project according to user requirements, using hardware kits and low-code or no-code software tools.
LO5. Apply standards for the preparation of technical reports.
LO6. Prepare the demonstration of the technological project’s functionalities.

S1. Introduction, course overview and assessment. Demos of previous year’s projects and new project ideas.
S2. Hardware and software kits available in the laboratory.
S3. Condensed Design Sprint methodology: Map, Sketch and Decide in 140 minutes.
S4. AEIOU interviews, empathy maps, personas.
S5. Use scenarios, definition of user requirements and product vision.
S6. Team management, conflict resolution, and leadership.
S7. Project management: Agile vs Waterfall methodologies. Introduction to the PMBook.
S8. Agile SCRUM methodology: 3 roles, 5 ceremonies, 3 artefacts, and 4 bi-weekly sprints.
S9. Product backlog, functionalities, user stories, prioritisation using MoSCoW, cost estimation with story points.
S10. Tools supporting SCRUM (JIRA).
S11. Low-code/no-code tools for prototype development (Power Apps).
S12. Hardware simulator.
S13. Production of a technical report and project demonstration.

The course is assessed continuously throughout the semester, with no final exam. Weighting is as follows:
• Individual assessment: 2 mini-tests – 20%
• Group project assessment – 80%, based on deliverables submitted via Moodle:
 ◦ D1 – User Requirements Report and Product Vision – 20%
 ◦ D2–D5 – Interim automated reports generated by JIRA at the end of Sprints #1 to #4 – 20%
 ◦ D6 – Final Technical Report – 15%
 ◦ D7 – Presentation, prototype demo, and project discussion – 25%
Students pass the course if they achieve a final weighted mark equal to or greater than 9.5 out of 20.

Students must be able to:
LO1. Describe the role of the mnemonic system in multimedia learning;
LO2. Describe the assumptions of the Cognitive Theory of Multimedia Learning (dual channel, limited capacity and active processing);
LO3.Describe and apply the 5 processes of the Cognitive Load Theory of Multimedia Learning;
LO4. Interpret the way in which human beings process information from images, narrated and printed words;
LO5. Define cognitive load and distinguish the different types;
LO6. Know strategies to efficiently manage cognitive load;
LO7. Plan a multimedia project starting from the definition of objectives, resources and scheduling and task management;
LO8. Develop prototypes detailing navigation schemes and content;
LO9. Test and validate a multimedia product.

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.

By the end of this course unit, the student should be able to:
LO1: Apply fundamental programming concepts.
LO2: Create procedures and functions with parameters.
LO3: Understanding the syntax of the Python programming language.
LO4: Develop programming solutions for problems of intermediate complexity.
LO5: Explain, execute and debug code fragments developed in Python.
LO6: Interpret the results obtained from executing code developed in Python.
LO7: Develop programming projects.

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):
A1: Learning Tasks with teacher validation (30%)
Five learning tasks will be completed throughout the semester.
The A1 grade corresponds to the average of the grades for the five tasks. To pass A1, the student must meet one of the following requirements:
- obtain at least 7 points in each of the five tasks
or
- obtain a minimum average of 8 points across the five tasks.

A2: Mandatory Group Project (3) with theoretical-practical discussion (70%)
Minimum grade of 9.5 points.
Late submissions will result in penalties.

Remediation:
Students who do not achieve the minimum overall grade may complete an individual Practical Project (100%) with oral discussion.
If a student misses an exam due to absence, or does not achieve the minimum grade of 7 points, they may take a make-up exam at the end of the semester.

Attendance:
A minimum attendance of 2/3 of classes is required.

"At the end of the course, students are expected to:
LO1: Acquire the essential concepts of geometry and statistics that support the application of educational digital technologies.
LO2: Develop logical reasoning skills and clarity of mathematical language and scientific statistics in the educational context.
LO3: Apply knowledge through numerical and graphical representations to facilitate the understanding of abstract concepts and their application in real-life situations.
LO4: Solve geometric and statistical problems, and other practical activities, on topics related to education.
LO5: Use geometric and statistical tools to interpret phenomena and make decisions."

"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%)."

LO1: Identify data types and the main steps in the data analysis process.
LO2: Organize and prepare data in spreadsheets.
LO3: Understand and use the main concepts of descriptive statistics, choosing appropriate measures and graphical representations to describe the data.
LO4: Explain what a data model is and understand some predictive data modeling techniques.
LO5: Create effective visualizations with pivot tables and graphs.
LO6: Interpret results and present conclusions clearly and reasonedly.

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.

LO1: Understand the main theories, concepts, and issues related to Work, Organizations, and Technology;
LO2: Understand the main processes of the digital transition directly related to the world of work and its organizations;
LO3: Analyze the multiple social, economic, and political implications brought by the digital transition;
LO4: Explore cases, strategies, and application methods to understand the real impacts of the digital transition on professions, companies, and organizations.

PC1. Is work different today than it was in the past?
PC2. What rights and duties in the world of work?
PC3. How has theory looked at technology?
PC4. What digital technologies are changing work?
PC5. What future for work?
PC6. Is artificial intelligence really that intelligent?
PC7. Where does precariousness begin and end?
PC8. Who is to blame when the machine makes a mistake?
PC9. Do digital technologies change the relationship between unions and companies?
PC10. What digital transformation in Portugal?

Continuous assessment throughout the semester:
Each student will conduct a Flipped Classroom session, which represents 20% of the final grade.

Individual work accounting for 35% of the final grade.

Group work accounting for a total of 35% of the final grade (10% for the group presentation and 25% for the written work).

Attendance and participation in classes represent 10% of the final grade. A minimum attendance of no less than 2/3 of the classes is required.

Each assessment element must have a minimum grade of 8. The final average of the various elements must be equal to or greater than 9.5.

Examination evaluation (1st Period if chosen by the student, 2nd Period, and Special Period): in-person exam representing 100% of the final grade with a minimum grade of 9.5.

OA1 - Understand basic concepts of composition, structure and construction of graphic elements.
OA2 - Understand the differences between vector image and bitmap image.
OA3 - Mastering basic rules of graphic communication.
OA4 - Mastering techniques and tools for producing vector graphics.
OA5 - Mastering techniques and tools for bitmap image production.
OA6 - Be able to create, process or manipulate a graphic image.
OA7 - Master text formatting techniques.
OA8 - Be able to combine image and text in order to create a graphic element.
OA9 - Mastering techniques for constructing a graphic narrative.
OA10 - Know and apply different formats when exporting graphic elements.
OA11 - Mastering basic concepts of web design and publishing images online.

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.

LO1. Acquiring knowledge about the fundamentals and stages of the Design Thinking process
LO2. Develop skills such as critical thinking, collaboration, empathy and creativity.
LO3. To apply Design Thinking in problem solving in several areas, promoting innovation and continuous improvement.

S1. Introduction to Design Thinking and Stage 1: Empathy (3h)
S2. Steps 2 and 3: Problem Definition and Ideation (3h)
S3. Step 4: Prototyping (3h)
S4. Step 5: Testing and application of Design Thinking in different areas (3h)

Semester-long Assessment Mode:

• Class participation (20%): Evaluates students' presence, involvement, and contribution in class discussions and activities.

• Individual work (40%): Students will develop an individual project applying Design Thinking to solve a specific problem. They will be evaluated on the application of the stages of Design Thinking, the quality of the proposed solutions, and creativity.

• Group work (40%): Students will form groups to develop a joint project, applying Design Thinking to solve a real challenge. Evaluation will be based on the application of the steps of Design Thinking, the quality of the solutions, and collaboration among group members.

To complete the course in the Semester-long Assessment mode, the student must attend at least 75% of the classes and must not score less than 7 marks in any of the assessment components. The strong focus on learning through practical and project activities means that this course does not include a final assessment mode.

OA1 - To be trained in the ethical and responsible use of Generative Artificial Intelligence (AI) tools
OA2 - To acquire critical analysis skills on the results produced by Generative AI tools
OA3 - To be able to identify and develop creative solutions in solving ethically and socially complex problems with Generative AI
OA4 - To be able to apply Generative AI tools in the preparation of academic work, in particular in the application of academic writing and in the use of normative citation and referencing procedures.

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

The Semester-Long Assessment includes the following activities:
1. Individual Activities (50%)
1.1 Prompt Simulations with AI Tools in an Academic Context (20%):
* Description: The student must create a clear/justified, well-structured prompt, according to the script proposed by the instructor in class.
* Assessment: (submit in Moodle), communication and teamwork skills based on the quality of the prompt simulations performed.
1.2 Oral Defense - Group Presentation - 5 min. Discussion - 5 min. (30%):
* Description: Each student must present their contributions to the work completed to the class.
* Assessment: After the student's presentation, there will be a question-and-answer session.
2. Group Activities (50%) [students are organized into groups of up to 5 students, randomly selected], which include:
* Group presentations, reviews, edits, and validations of AI-generated content. The assessment (to be submitted in Moodle) includes gathering relevant information, assessing the clarity and innovative nature of the use of structured prompts.
* Development of strategies for reviewing, editing, and validating AI-generated content. Students will be asked to critically evaluate and reflect on the ethical challenges of integrating AI into an academic environment. The assessment (to be submitted in Moodle) will consist of correcting the work based on the accuracy and quality of the reviews and edits, as well as student participation in providing feedback to their peers.
* Final Project Presentation Simulations, where groups choose a topic and create a fictitious project following the structure of a technical report or scientific text. They present their project in class (5 min.) and discuss the topic (5 min.). The assessment (to be submitted in Moodle) will consider the organization, content, correct use of the structure, and procedures of the academic work.

General Considerations:
Feedback on student performance in each activity will be provided during the Semester Assessment.
To be assessed throughout the semester, students must attend 80% of classes and achieve a score of at least 7 points in each assessment.
If there are questions about participation in the activities, the instructor may request an oral discussion.
The group must ensure that at least one computer is available for each group to allow for classroom activities.
There will be no final exam assessment; passing will be determined by the weighted average of the assessments throughout the semester. Assessments in the second and special assessment periods will have an alternative assessment method, so any students wishing to take the assessment in these assessment periods should contact their instructor in advance to learn about the assessment procedure.

LO1. Develop specific oral communication skills for public presentations.
LO2. Know and identify strategies for effective use of the vocal apparatus.
LO3. Identify and improve body expression. LO4. Learn performance techniques.
The learning objectives will be achieved through practical and reflective activities, supported by an active and participatory teaching method that emphasizes experiential learning. The knowledge acquired involves both theatrical theory and specific oral communication techniques. Students will learn about the fundamentals of vocal expression, character interpretation and improvisation, adapting this knowledge to the context of public performances.

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.

OA1 - Mastering basic script production techniques.
OA2 - Having the hability to produce a storyboard.
OA3 - Know how to organize information with a view to audiovisual production (photography and video).
OA4 - Master basic image capture techniques.
OA5 - Master several techniques and tools to produce animation and motion design.
OA6 - Being able to produce animated sequences and visual narratives.
OA7 - Develop basic notions of sound capture.
OA8 - Master video editing techniques.
OA9 - Know how to export and publish animated content in different formats and media.

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.

LO 1 Explain what databases and information systems are, characterising them in terms of both technology and their importance to organisations.
LO 2. formally represent information requirements by drawing up conceptual data models.
LO 3 Explain the Relational Model and data normalisation, highlighting their advantages and the situations in which they should be applied.
LO 4 Design relational databases that respond to requirements specified by conceptual data models.
LO 5. build and programme a relational database using the SQL language.
LO 6. manipulate data - i.e. insert, query, alter and delete - using the SQL language.
LO 7. Explain what database administration consists of, why it is necessary and how its most essential tasks are carried out.

S1. Introduction to Information Systems and their role in organisations.
S2. Introduction to Information Systems Analysis with UML: Introduction, requirements analysis, data models, schemas and UML diagrams.
S3. Database Design. Relational Model: relationships, attributes, primary keys, foreign keys, integrity rules, normalisation and optimisations.
S4. SQL Language. Tables, relational algebra, simple queries, subqueries, operators (SELECT, Insert, delete, update), views, indexes, triggers, stored procedures and transactions.
S5. Administration and Security in Database Management Systems (DBMS).

Assessment throughout the semester:

- 3 tests to be taken during the semester (70%). Minimum grade of 8 points for each tests.
- 1 modelling and implementation project (in groups of up to 3 people) (30%). The minimum project grade is 10 points. Completion of the project is mandatory for approval. The project is assessed in groups with individual oral discussion.

Assessment by exam:
- One exam, weighted 100%

The minimum passing grade for the course unit is 10 out of 20.
Attendance at 2/3 of the scheduled classes is mandatory for approval.

The Training Management course will adopt a student-centered teaching method through project-based learning, combined with individual task-based learning. In the group work, higher-level skills are promoted, such as analyzing, creating, and evaluating a digital training project. By the end of the course, students will be able to:
LO1: Characterize different training types, modalities, and products
LO2: Identify qualification systems and competency frameworks
LO3: Conduct a training needs assessment
LO4: Design a training program based on the needs assessment of an organization or market
LO5: Develop technical and pedagogical tools for training facilitation
LO6: Produce digital content for training
LO7: Plan, execute, and evaluate training

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.

At the end of this UC, the student should be able to:
LG.1. Present the image of the product/service in a website
OA.2. Present the image of the product/service in social networks
OA.3. Describe functionalities of the product/service
OA.4. Describe phases of the development plan
OA.5. Develop a prototype
OA.6. Test the prototype in laboratory
OA.7. Correct the product/service according to tests
OA.8. Optimize the product/service considering economic, social, and environmental aspects
OA.9. Adjust the business plan after development and tests, including commercialization and image
OA.10. Define product/service management and maintenance plan

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.

At the end of this course, the student should be able to:
LO1. Understand cybersecurity in its different perspectives
LO2. Understand the main security challenges and threats that organisations and users have to face;
LO3. Introduce the legal, ethical and strategic context of information security
LO4. Identify and manage information security risk;
LO5. Know and apply appropriate security technologies for risk mitigation;
LO6. Know mechanisms for the management and maintenance of information security environments.

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:
- Carrying out a set of group projects and activities (60%) throughout the semester.
- Two individual tests (40%) [minimum mark of 6 for each test].
Attendance at a minimum number of classes is not compulsory for the assessment throughout the semester.
Assessment by exam:
For students who opt for this process or for those who fail the periodic assessment process, with 3 seasons under the terms of the RGACC.

At the end of the learning unit, the student must be able to: LG.1. Understand entrepreneurship; LG.2. Create new innovative ideas, using ideation techniques and design thinking; LG.3. Create value propositions, business models, and business plans; LG.5. Develop, test and demonstrate technology-based products, processes and services; LG.6. Analyse business scalability; LG.7. Prepare internationalization and commercialization plans; LG.8. Search and analyse funding sources

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%.

By successfully completing this CU, the student will be able to:
LO1. Evaluate and select platforms for management and distribution of educational and training content;
LO2. Install, configure, customize and manage platforms for content management
LO3. Create learning content for distance training and learning courses
LO4. Plan and execute the distance learning courses design within the pedagogical and technical approach
LO5. Organise, construct and implement courses in content/learning management platforms

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%)

LO1: Understand the historical context of Computing and HCI (Human-Computer Interaction) and the principles of user-centred Design
LO2: Understand the fundamental perceptive and cognitive characteristics of human beings and their respective limitations that impact HCI design.
LO3: Create empathy with the user (needs, goals, current and desired tasks, problems). Requirements based on collected data.
LO4: Apply principles and 'golden rules' of HCI design and usability in practical cases
LO5: Apply techniques/rules of visual screen design (WWW and mobility). Create storyboards and low-fidelity (Lo-Fi) and high-fidelity (Hi-Fi) prototypes. Perform ideation and development of the Minimum Viable Product (and its Lo-Fi).
LO6: Design and apply heuristic evaluation with Lo-Fi experts, leading to a new iteration and Hi-Fi development.
LO7: Design experimental studies of the Hi-Fi with end-users and apply usability and task satisfaction metrics based on collected data.

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

Assessment throughout the semester, not including a Final Exam. Weights:
70% Group lab project work + presentation and discussion
30% Two multiple choice mini-tests
Mandatory minimum attendance in 2/3 of classes
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 take the exam in the 2nd or special seasons (30% of the grade). Is mandatory to pass the group project or an equivalent individual project (70%)

LO1 Frame and understand the main components of the World Wide Web;
LO2 Know and correctly apply the client programming model and the MVC paradigm;
LO3 Use and extend server technologies to develop web applications and services;
LO4 Integrate web applications and services with Database Management Systems;
LO5 Understand the life cycle pipeline of a web project;
LO6 Develop creativity, technological innovation, critical thinking;
LO7 Develop self-learning, peer review, teamwork, oral expression.

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
S6 Service-oriented web architectures. Web Services and Microservices. Middleware models for the Web. Containerization.

Course with Periodic Assessment, not by Final Exam.
Assessment weights:
- Lab project (in group between 2 and 4), with technical report, individual oral discussion (60%)
- 4 multiple response individual Mini-tests (40%)

A mark below 8 assigns (in average of mini-tests) the student to an exam in normal and/or the appeal period (40% of the mark) in a written test, with the completion and approval of the group project, or an individual project (with technical report and individual oral discussion) is mandatory (60%).

At the end of this course students should be able to:
LO1. Define curriculum and associated dimensions;
LO2. Frame narratives in different theories about the curriculum;
LO3. Understand the role of curriculum agents and structures;
LO4. Describe curriculum development models;
LO5. Apply the principles of curriculum development and curriculum management models;
LO6. Propose curricular innovation measures;
LO7. Plan curriculum evaluation strategies;
LO8. Analyze practical curriculum planning situations in different contexts;
LO9. Plan an investigation in the context of curriculum development;
LO10. Design and apply qualitative data collection instruments (semi-structured interviews).

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.

Upon completing this course, students should:
OA1: Recognise AI fundamentals, linking predicate logic and knowledge-based systems.
OA2: Build and interpret simple machine learning models, distinguishing classification from regression, and defining suitable evaluation metrics.
OA3: Apply deductive and inductive reasoning, understanding how knowledge representation and heuristics guide decision-making.
OA4: Critically analyse the suitability of AI approaches in specific contexts, considering data, performance, and ethical implications.
OA5: Implement basic Generative AI strategies, focusing on prompt engineering and innovative content generation.
OA6: Demonstrate autonomous research and experimentation skills, integrating AI concepts into collaborative projects.
OA7: Reflect on AI constraints and socio-economic impacts, adopting a responsible stance in methodology selection and application.

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.

LO1: Distinguish the different types of disabilities and associated characteristics
LO2: Identify Universal Design Principles and Inclusive Design Practices
LO3: Know the main functional challenges of assistive technologies
LO4: Apply accessibility practices to digital content
LO5: Develop accessible and inclusive digital educational resources, depending on the context

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.

LO1: Recognize Data Types
LO2: Tackle Data Challenges
LO3: Apply AI for Insight Extraction
LO4: Enhance Data Quality:
LO5: Implement AI Techniques
LO6: Navigate Ethical and Legal Aspects
LO7: Communicate Insights Effectively
LO8: Collaborate on AI Projects:
LO9: Integrate AI Tools and Libraries
LO10: Applications in the education sector

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.
Grades can only be improved by repeating the assessment process the following year.

LO1: Evaluate the needs and opportunities for improvement in the digitalization of educational processes in educational institutions, using appropriate assessment tools and techniques
LO2: Create a technological project that addresses real-world problems in the domain of Educational Digital Technologies, promoting a positive impact on pedagogical practices and the school community
LO3: Implement a project that integrates at least one technological area (programming, multimedia, artificial intelligence, cybersecurity, databases) and one educational area (teaching and learning methodologies and strategies, inclusion, accessibility, training management, curriculum)
LO4: Apply project management methodologies in real educational contexts, from creation to implementation and evaluation, ensuring the effectiveness and sustainability of the proposed solutions

S1: Techniques for identifying needs and opportunities in the school context
S2: Diagnostic and evaluation tools
S3: Objectives and results with educational and social impact
S4: Planning an application project in the school context
S5: Integration of technological and educational areas in educational projects
S6: Management of educational projects
S7: Dissemination of technological projects

Project evaluation, according to the General Regulations for Knowledge and Skills Assessment of Iscte, distributed as follows:
- Video dedicated to project presentation (context and functionalities) with a maximum duration of 2 minutes in a group - 5%
- Individual mini-test - 15%
- Preliminary report with group discussion - 10%
- Final report with individual and group discussion - 20%
- Project - 50%
A minimum attendance of no less than 3/4 of classes is required.
All components have a minimum grade of 8.5 out of 20.
The final average grade must be equal to or greater than 9.5 out of 20.
The course unit does not include evaluation by exam.

LO1: Explain the concepts and components of analytical information systems and Business Intelligence.
LO2: Model and transform data using Power BI.
LO3: Create interactive reports and dashboards tailored to different management needs.
LO4: Analyze and interpret data to support organizational decision-making.
LO5: Evaluate the quality of information and the effectiveness of analytical visualizations.

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.

LO1: Analyze needs and opportunities for improvement in training processes and skills development within real business contexts.
LO2: Implement an educational technology project that addresses concrete business challenges, integrating knowledge acquired throughout the course.
LO3: Apply agile project management methodologies in business environments, adapting to the specific needs and constraints of the context.
LO4: Develop innovative educational technology solutions that enhance efficiency, productivity, and continuous learning within organizations.
LO5: Communicate effectively with business stakeholders, presenting results and documenting project progress.
LO6: Work productively in multidisciplinary teams, demonstrating collaboration, negotiation, and problem-solving skills.

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.

After completing this UC, the student will be able to:

LO1. Identify the main themes and debates relating to the impact of digital technologies on contemporary societies;
LO2. Describe, explain and analyze these themes and debates in a reasoned manner;
LO3. Identify the implications of digital technological change in economic, social, cultural, environmental and scientific terms;
LO4. Predict some of the consequences and impacts on the social fabric resulting from the implementation of a digital technological solution;
LO5. Explore the boundaries between technological knowledge and knowledge of the social sciences;
LO6. Develop forms of interdisciplinary learning and critical thinking, debating with interlocutors from different scientific and social areas.

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.