AIRE Learning Sciences

Teachers: Stéphane Douady, Melissa McCartney (FIU, Florida), Saskia van der Vies (VUMC, Amsterdam), Johannes Jaeger, Eugenia Covernton

Duration: 1 week workshop

Description: The workshop intends to assemble free-spirited students, and researchers from broad scientific backgrounds to conceive creative projects at the interface with Life, Learning and Digital Sciences.This year will combine newcomer students of the FdV PhD program and 2nd year AIRE master program.The CIRP workshop attempts to provide the primary basis for collegiality and communication through dialogue and brainstorming on open questions in Life Sciences, Education Design, Entrepreneurship....

Objectives:

• Focus on an important question and define the means to approach it from different angles
• Zoom out (broad view) and zoom in (be precise and define the key points)
• Think and express your ideas more clearly
• Gain confidence in your ideas
• Discuss, accept or reject ideas
• Learn to take constructive scientific criticism
• Learn how to write a research proposal
• Discuss questions thoroughly
• Learn to interact with people from different backgrounds

Prerequisite: Research courses of the M1 or an experience in research.

Examples of work: Seminars, group work, icebreakers

Teachers: Franck Zenasni, Amélia Legavre, Raphaël Grasset, Macarena Paz Celume, Laurent Sovet, Thomas Constant

Duration: 45 hours

Description: This class is dedicated to the readings of research articles, their analysis and their use to ground debates.

Objectives:

• To read efficiently an article (take notes)
• Make links between articles (mini literature review every session)
• Elaborate critics about methodology, concepts and analysis in research.

Prerequisites: None

Examples of work: Synchronous discussion, oral presentation

Students of the Learning Science Master program have the opportunity to do 3 internships during their 2 years in the program:

ONE internship of 5 MONTHS on the first year (starting early-February) TWO internships of 4/5 MONTHS each on the second year (first one starting beginning of October, second one starting mid-February). These sequences are designed for the students to meet the diversity of actors and positions in the innovative education field (startups, research labs, schools, companies, universities, public services, associations, etc.). The idea is also to make them use and seize the tools and core concepts seen in the Master in order to empower themselves as EdTech experts, activists and stakeholders of the learning transition of our society.

Our students are generally advised to do their first internship in France, but as our promotions are from various horizons and backgrounds, we also accept and sometimes recommend internships abroad (mainly in the second year).

Students also have the possibility to choose to carry out a project instead of an internship. Mentored by the Challenge Hub team, the project will also be subject to pedagogical validation. It will have to be in line with a research perspective and be associated with a team of referring professionals who will allow a solid pedagogical follow-up.

Teachers: Sophie Pène, Gabrielle Fauste, Benoit Bonte (Millionroads)

Duration: 30 hours

Description: The goal of the Skills and Data course is to leverage students’ extra-curricular projects and initiatives to augment the Learning Planet Institute’s scientific training as a recognition of their self-directed learning experiences. The course supports a skill-based approach. It is meant to make visible and recognize what students really learn, ie. how they make the prescribed knowledge theirs and augment it, how the transformative process of learning occurs. They create a personalised learning path that reflects their singularity. The course supports them in making this process conscious and reflecting on the transformative process at play.To achieve this, students are supported in defining their short and long term goals and to document their activities.To do so, they need to develop new skills and implement a set of tools: an activity log that encourages a reflexive approach to their day to day learning experiences and how their daily activities relate directly with learning, and the building of new skills. They create the story of meaningful experiences that present their actions to the world and will illustrate their portfolio, showing their competency to the world.

Students will learn to define goals that reflect their vision of the future and aspirations, guided by a data-based approach to their orientation and future choices by using real career data from HumanRoads GPS. They develop a form of data literacy that informs their decision-making process at the crossroads of what they know, who they want to become, and what career and orientation choices others have done before them. They learn to reflect on their personal pathway in a holistic way, including their individual actions and wishes, but also real life data and facts that reflect the ecosystem they’re part of.

Objectives:

• By the end of the course, students will be able to:
• Define 1-3 personal goals, short, mid and long-term, identify their resources and define strategies to reach them
• Implement a set of tools based on the daily activities that supports the recognition and documentation of developed skills, experiences and learning processes.
• Use storytelling to describe meaningful experiences: documented and data-based accounts of past projects and experiences that resulted in skills creation.
• Publish these experiences in a portfolio of their choice.
• Explore the trajectories of alumni and other professionals to inform future career choices.
• Develop a reflexive practice of analyzing their actions, learnings and objectives

Prerequisites: None

Examples of work:

• A project plan including a goal, waypoints, resources and plan to reach it
• Creation of an activity log
• Link skills with real activities
• Produce a personal vision based on personal values, past experiences, future aspirations and real career data
• Publish experiences in a portfolio

Teachers: Gabrielle Fauste, Florian Pittion-Rossillon

Duration: 30 hours

Description: The course objective is to equip students with a broad culture of ​entrepreneurship as a framework​ used to identify problems that can be turned into opportunities, and creatively generate ways of solving that problem with limited means. They will develop ​entrepreneurial skills​ that combine a range of technical, management and personal skills. These skills build upon an entrepreneur's first assets: who s/he is, whom and what s.he knows. They can apply to both the development of personal projects or to create a new business.

Entrepreneurial skills are considered today as essential skills all students need to develop. Even the students who wish to integrate the research field should be aware that it is more and more submitted to competition, as public funds tend to be more scarce. That's why senior heads of research look for resourceful contributors and contractors, which means people skilled in project mode and capable of understanding obvious business stakes. This is a key factor of the student's future employability, whatever the country and the field they will choose.

The Learning Planet Institute's promise is to turn students into a singular individual at the nexus of 3 realms: a social, scientist entrepreneur. Hence, the entrepreneurship course should provide them with the capability to start an initiative that requires classical ​entrepreneurial activities​ (funding, communication, planning, pitching, branding, etc..), while ​including the social dimension​ of any work and approaching the venture as scientists.

Based upon a simple and field-based definition of an ‘entrepreneur’, this course will provide tools that will help students find the keys to their own leadership, wherever they come from in terms of experience, studies and country.

Objectives:

• At the end of the course, students will be able to apply a standard framework for the design and the launch of any venture
• Identify the steps required to create a venture, commercial or personal, including a social dimension, leaning in their existing resources
• Identify the external resources and support needed to support the launch and development of their venture
• Approach entrepreneurship as scientists, both in the experimental approach they will deploy, an evidence-based foundation, or the domain of application (bio-engineering, ...)
• Apply project management skills to the planning and execution of their project
• Define success for their project in terms of social impact through a theory of change
• They will get a culture of innovation and entrepreneurship in the fields of science, social change, and technology: trends, innovative business models, ...
• They will gain an understanding of key entrepreneurial characteristics and competencies
• They will work on their own posture as creators and innovators, identifying the skills and capabilities they can rely on to support themselves in creating a venture.

Prerequisites: Basic practical knowledge of how to organize and run a project. Depending on the expertise of all students in project management, a refresher course might be organized for beginners.

Examples of works: Lectures, reverse classroom, workshop, case studies, challenges, discussion about documents (articles, video), etc.

Teachers: Amélia Legavre, Nicolas de Chanaud

Duration: 2*45 hours

Description: These courses will guide students during the process of achieving academic research, from the topic’s choice to the protocol construction, literature search, data analysis, writing and oral presentation. It will be applied to the individual Master Thesis project of each student.

The first semester presents some main methodologies in research, while guiding the students through their readings in order to stabilize a research question. The second semester helps them to build their protocole and data collection tools, analyse the data and present it following academic standards

Objectives:

• Know about the main methodologies in research
• Gain a scientific way to look at educational issues
• Be able to write a realistic protocole
• Gain some expertise on a specific topic
• Develop professional skills : collecting and analysing data, synthesis and writing.

Prerequisites: Think about a topic to address through the master thesis

Examples of work: Readings of scientific articles, exercices (analysis of given data, question on videos), collective discussions, writing research paper

Teachers: Franck Zenasni + invited speakers

Duration: Full time week

Description: This workshop aims to explore creativity in both theoretical and practical ways. For that purpose creativity will be examined through four specific facets that will be defined during the open discussions

It is a five days’ workshop which articulates (a) definition and description of scientific concepts related to creativity with (b) activities that help to experience and understand what is creativity and how we can improve and promote it.

Objectives:

• Know what is the scientific description of creativity
• Know the factors that promote or inhibit creativity
• Understand how we can “evaluate” creativity
• Know and be initiated to three specific creative techniques
• Develop ways of thinking that are resources for creation

Prerequisite: Think about your everyday creativity

Examples for work: Complete creative tasks, collectively share creative experiences, lectures

Differents guests at each session

One seminar each month

Description: As part of the Master program, students attend monthly Masterclasses, which are special lessons given by invited speakers. The goal is to have our students interact with high-level scientists from all over the world, who come to share not only their science, but also an overview of their career, with positive and negative moments. It is a moment of open interaction and inspiration. The talk is in the form of an interactive discussion about the interdisciplinary nature of the invited speaker’s research, with a perspective on their career.

Examples of guest speakers:

• Célia Hodent, Game UX consultant, septembre 2018
• Claude Terosier, Magic Makers CEO, novembre 2018
• Sylvain Laborde, Lecturer and research, Deutsche Sporthochschule Köln, octobre 2019
• Emmanuelle Volle, neurologist and neuroscientist, researcher in creativity at the Institut du cerveau et de la moelle épinière (ICM), novembre 2019

and also scientists from the Learning Planet Institute ecosystem and alumni of the Master AIRE

All second-year Master students and second-year PhD students are required to participate in the Thematic Workshops (TW).

What are the TW? It’s a chance to gain experience in organising a scientific event in the form of a conference, workshop or debate. Students will have the opportunity to build the whole program of the event from the ground up: invite speakers, decide topics, advertise the event, take care of the logistics…

Students will be divided into groups, according to the subject of their Phd project / internship project (LiSc and DiSc) / master thesis (LeSc). Each group will have to organise a conference/workshop/debate of half a day, which will be held during a week in February

Roles in a group In every group, each member will have a defined role. Students will have to fulfill the following roles:

• Scientific program: choose and invite speakers, prepare debates, prepare the outline of the program...
• Communication: creation of an account on social media, creation of a flyer and comm material to advertise the workshop, take care of inviting attendees and of their registration... catering
• Relay with coordination team: keep us updated on a regular basis on the advancements in the organization of your workshop.

Attendance For M2 and 2nd year PhD students, Thematic Workshops are a mandatory event. Given the variety of backgrounds, different topics will be covered all along the week. You are all required to attend 3 days of conferences (the day of your group’s event, plus two other days, of which at least one should be from another field).

Teachers: Nils Myzskowski (Assistant professor, PACE University, New York)

Duration: 30 hours

Description: This course reviews advanced statistical procedures for data analysis, as well as their applications using both user-friendly (Graphical User Interface -based) and advanced (syntax-based) statistical software.

This course builds on the previous course, which was focused on foundations and most common applications of Linear Models (LM), including simple and multiple linear regression models, group mean comparisons, one-sample tests and repeated measures mean comparisons. Based on this existing knowledge, this course tackles various extensions of linear models, such as Generalized Linear Models (GLM) – including applications like Logistic Regression and Poisson Regression – moderation and mediation models, multivariate linear models, latent variable models – including factor analysis models and structural equation models – time series analysis (TSA), advanced model selection methods – including stepwise selection, best subset selection, and regularization – cross-validation methods, and an introduction to predictive algorithms used in machine learning.

Throughout this course, students are instructed on when (for which research design, and to answer which question) and how (checking assumptions, interpreting results and reporting them appropriately) to use these statistical methods. Along the course, students are trained in using various statistical software, including R (a very popular statistical programming language) and JASP (an actively developed freeware statistical package built on R). They are also trained in connecting their understanding of statistics with “real-world” problems (analysing a dataset, communicating scientific results, understanding published research, etc.).

Objectives: At the end of the class, students shall have a functional understanding of the following analyses, along with the ability to perform adequately a number of popular statistical learning procedures, including:

• Logistic regression
• Poisson regression
• Generalized Linear Modeling
• Moderation analysis
• Mediation analysis
• Multivariate linear modeling
• Factor analysis
• Structural Equation Modeling
• Stepwise model selection
• Best subset model selection
• Cross-validation
• Regularization (Ridge regression, LASSO regression, Elastic Net Regression)

Students shall also be able to use related graphical methods (plots), report the results of these analyses, and test the assumptions of these statistical analyses appropriately. It is also expected that students are able to connect their understanding of data analysis with the understanding of scientific reports of various types (conference presentations, peer-reviewed publications, etc.).

Prerequisite: To have validate the Behavioral and Social Data Science I course.

Examples for work:

• Importing and processing dataset files of various forms using R and JASP
• Testing various hypotheses using data, involving different types of outcomes (notably numeric, binary, count and categorical)
• Making relevant plots to answer questions and verify assumptions based on data
• Interpreting the output of statistical software
• Troubleshoot issues with a data analysis procedure
• Suggest ideas on how to answer specific questions based on a dataset