Overview of available semester/master projects
The CHILI lab is inventing learning technologies that exploit recent advances in human-computer interaction (e.g. eye tracking, augmented reality, …) and in human-robot interaction. We are working on several educational platforms described below. Each platforms offers possibilities for semester and master projects. While semester projects are often limited to development, master projects usually include an empirical study with learners, supervised by our team. The platforms are:
- NEW ! We have funding for supporting master theses in the field of learning technologies in Fall 2019 and Spring 2020. In 2017, EPFL has launched the Swiss EdTech Collider which now gathers 77 start-ups in this field. Some of them will be interested to host master theses. You will be supervised by Prof. Dillenbourg or his team members but you will located in a start-up (different cities in CH). Contact: pierre.dillenbourg (at) epfl.ch
- A variety of projects in LEARNING ANALYTICS, i.e. data sciences applied to education are offered by my lab (contact: jennifer.olsen (at) epfl.ch) as well by the Center for Digital Education. See their project list here.
- REALTO is a social platform for vocational education. Apprentices collect pictures at the workplace and upload them on their class flow, where several picture annotation tools and augmented reality tools are available. Current projects concern these tools as well as a dashboard for teachers. Contact: catharine.oertel (at) epfl.ch
- CELLULO is a small robot for education. It moves by itself and can be moved by pupils. The hardware is ready and projects concern the software environments as well as designing and experimenting with new learning activities. Contact: wafa.johal (at) epfl.ch
- CO-WRITER is a project in which kids who face writing difficulties are offered to teach Nao how to write. Nao is a small humanoid robot available on the market. The projects concerns smoothening the interaction between the robot and young children. Contact: wafa.johal (at) epfl.ch
Some of these projects are described below, but since research is moving on permanently, we always have new opportunities. You can always contact the names above or pierre.dillenbourg (at) epfl.ch if you are interested in advancing digital education.
Learning analytics involves applying techniques in data science for optimizing and understanding learning. One dataset is from Tegami, a test developed to diagnose dysgraphia in children. Our goal is not only to automate this diagnosis, but also propose remediations so children can learn to write better.
[Semester] Learning to explain the predictions of neural networks
The predictive powers of convolution neural networks in recent years have been achieved or surpassed human level performance. However, interpretability or lack thereof limits the adoption of these methods in decision critical domains. This project aims to address this issue in the context of diagnosing dysgraphia. A popular solution is to apply saliency maps (eg. guided backprop and its variants). However, given domain knowledge of this condition, can we have a more descriptive explanation, by incorporating a prior on some features we would like the explanations to have, or learning an unsupervised representation of descriptors.
Prerequisites: Deep learning, Pytorch, image processing
Contact: teresa.yeo [at] epfl.ch, thibault.asselborn [at] epfl.ch
[Semester] Gaining insights from the predictions of neural networks
Deep learning applied to medical diagnosis, in some cases, achieved or surpassed that of experts. Their decision making process differs vastly from a human’s in a way that can reveal new connections and associations. This project aims to explore these associations in the context of diagnosing dysgraphia. One approach would be to visualize the embeddings following the convolution layers from predicting factors such as grade, gender and so on.
Prerequisites: Deep learning, Pytorch, image processing
Contact: teresa.yeo [at] epfl.ch, thibault.asselborn [at] epfl.ch
REALTO is a social platform for vocational education. Apprentices collect pictures (and videos) at the workplace and upload them on their class flow. Supervisors and teachers have the possibility to provide feedback on the students private flow and peers have the possibility to comment on other students pictures and videos. Over 2000 apprentices from a wide variety of disciplines such as florists, carpenters, fashion designers are currently registered on REALTO.
Following are the list of available projects and their descriptions. In case of interest, please send an email to the contact person. In your email, please include your CV and a short description of your specific interests.
[Master] The Stack Overflow Annual Survey: A look into the future?
Description: Stack Overflow is a well-known Q&A website for developers, where users may ask questions and give answers about a wide variety of programming issues. Stack Overflow also runs an annual developer survey, where the participants answer questions about the programming languages and frameworks they use and the methods they have used to learn new languages. This annual survey happens in January and has been receiving over 60,000 responses since 2017, creating a rich dataset of at least 2-3 years by the time this project begins! Of particular interest is the fact that developers may indicate their transitions between different languages and platforms, i.e. that they used language X last year, but are going to use language Y next year. This allows us to calculate transition probabilities between different topics (languages, frameworks) for people in different development fields (backend development, frontend development, etc.).
In this project, our aim is to investigate this dataset in parallel with the Stack Overflow questions and answers and also Stack Overflow Jobs (the job advertisement platform on Stack Overflow). We aim to answer (some or all of) the following questions:
Are the topical transition probabilities obtained from the developer survey predictive of how prevalent questions of that topic will be during the year? Where is the prediction closest to reality and where is it furthest from reality?
Do the probabilities obtained from the annual survey correspond to job advertisements?
What you need to know:
Mathematical knowledge: Basic probability and Markov Chains
Programming knowledge: Python and Jupyter notebooks
Data scientific knowledge: Experience with general descriptive statistics, experience with handling large datasets (Apache Spark experience is not necessary, but it is welcome) and having taken the course “Applied Data Analysis” (CS-401) is a bonus
Contact: ramtin.yazdanian [at] epfl.ch
[Master] Designing a virtual reality learning environment with Unity
Description: The practical experience of the students in their apprenticeship can be quite limited. One of the ideas of Realto is to provide a digital space for the learners to “expand their experience.” The goal of this project is to design a 3d virtual learning environment where the learners can gain some additional experience that would support their vocational training. Some examples can be: an interactive 3d garden for gardeners or a church to be decorated for florists. The project will involve working with Unity and VR headset.
Prerequisites: experience in following topics or interest in learning: Unity, C#, VR
Contact: kevin.kim (at) epfl.ch
[Master] Object detection using deep learning
Description: On REALTO, apprentices upload pictures taken from their workplaces and share them in the digital space. In order to make a better use of the uploaded data, it is important to have some semantic understanding about the image. Recent advancement of deep learning algorithms has improved the performance on the problem of image-based object detection. The goal of this project is to implement, train and test state-of-the-art deep learning algorithms to recognize objects from images. We are currently working with a dataset of flower bouquets (for florists).
Prerequisites: experience in following topics or interest in learning: machine learning, deep learning, image processing, python
Contact: kevin.kim (at) epfl.ch, catharine.oertel (at) epfl.ch
The CoWriter Project aims at exploring how a robot can help children with the acquisition of handwriting, with an original approach: the children are the teachers who help the robot to better write! This paradigm, known as learning by teaching, has several powerful effects: it boosts the children’ self-esteem (which is especially important for children with handwriting difficulties), it get them to practise hand-wrtiing without even noticing, and engage them into a particular interaction with the robot called the Protégé effect: because they unconsciously feel that they are somehow responsible if the robot does not succeed in improving its writing skills, they commit to the interaction, and make particular efforts to figure out what is difficult for the robot, thus developing their metacognitive skills and reflecting on their own errors.
[Semester] CoPainter: Drawing Activity with Nao Robot
The CoWriter project aims to develop an Child-Robot-Interaction based on the learning by teaching paradigm: we ask a child to teach handwriting to a Nao robot. The educational/therapeutic success of such an interaction is mainly based on the engagement of children in the interaction. For the moment we have 3 activities but our idea is to implement broad types of activities in order to switch activities according to the child’s learning level and attentional state.
The aim of this semester project is to develop a drawing activity involving the Nao robot, that will be tested later on with children. During this project you will be in charge of the software development of the activity controlling the Nao robot. The student will also develop the tablet application allowing drawing.
Using the same principle as the QuickDraw online game, the robot and the child will play a pictionary-like game. The goal will be to collect drawing data and adpat the difficulty of the game with the ability of the child.
The activity should be ROS based and be integrated to the whole CoWriter project. The student will:
- Explore the QuickDraw dataset to generate different levels of drawing difficulty.
- Develop an interactive scenario with Nao using an android tablet.
- Integrate the activity within the CoWriter project.
Prerequisites: Experience in the following skills or interest in learning Machine Learning, Python, Android, OpenCV, ROS, git.
Contact: wafa.johal (at) epfl.ch
In the Cellulo Project, we are aiming to design and build the pencils of the future’s classroom, in the form of robots. We imagine these as swarm robots, each of them very simple and affordable, that reside on large paper sheets that contain the learning activities. Our vision is that these be ubiquitous, namely a natural part of the classroom ecosystem, as to shift the focus from the robot to the activity. With Cellulo you can actually grab and move a planet to see what happens to its orbit, or vibrate a molecule with your hands to see how it behaves. Cellulo makes tangible what is intangible in learning.
[Master/ Bachelor Semester] Cellulo: Pacman Game with Dynamic Workspace
In the Cellulo project, we are designing tangible robots to be used in games. Our robots operate on tabletop paper sheets and are used as game elements where they can be physical input devices and/or autonomous agents. Our hypothesis is that we can build tabletop games with these robots that can move and be moved, possibly at the same time. One of our current goals is to explore game design options that create engaging interactions between multiple players through these tangible robots and shareable game spaces. Our robots work on printed paper sheets that can be produced with up to ~1m width and with unlimited length, and so far we have used these large shared workspaces within our activities to promote scalable multi-user interaction. We have recently developed smaller workspaces that can be tiled dynamically during runtime, allowing the growing, shrinking and modification of the workspace and its shape by the players.
The goal of this project is to design a tangible game that uses this element at its core, together with the Cellulo robots. The resulting game will require the players to build the shape and the functionality of the workspace itself as the game progresses, on which their robots will move and be moved. A small user study will be conducted at the end to validate the player interaction with the developed game.
- Extend Pacman game to two players or more
- Extend Pacman with tiles to have a dynamic game space
Prerequisites: Experience in the following skills or interest in learning: Qt/QtQuick development, QML programming, git, game design,
Contact: arzu.guneysu (at) epfl.ch
[Master/ Bachelor Semester] Cellulo: Human Arm Activity Estimation During Pacman Game-Play
In the Cellulo project, we are designing gamified rehabilitation activities with tangible robots. The unique functionalities of Cellulo (i.e. haptic feedback and submillimeter precision localization) make it an interesting device for upper-arm rehabilitation. One of the designed games is tabletop Pacman game where the user performs upper limb activities while playing against ghost robots. This Pacman game has been tested with several healthy and impaired participants. Among them, the game-play data of 33 healthy participants and 10 impaired participants were recorded with a 2d and 3d camera. This semester project will investigate the construction of the skeleton data of the players during game-play and calculation of angles corresponding to shoulder, elbow and wrist motions of the player through video data. The final goal will be finding a correlation between end-effector (Pacman robot held by the user) position and the arm angles. Therefore, the game system will be able to make the estimations of performed arm activities through Pacman data.
The project will consist of:
- Extraction of upper skeleton data through 2d or 3d video data (by using open pose library: https://gitlab.iri.upc.edu/perception/openpose_ros ) and calculation arm angles.
- Investigating the relationship between Pacman pose data (end-effector position) and corresponding arm activities such as angle changes in elbow and shoulder
- Estimating the performed angle values of joints through Pacman pose data.
Prerequisites: Experience in the following skills or interest in learning Computer Vision, Python, C++, ROS, QtQuick, Data Analysis, git.
Contact: arzu.guneysu (at) epfl.ch , hala.khodr (at) epfl.ch
[Master/ Bachelor Semester] Charging station for Cellulo
In the Cellulo project, we are designing tangible swarm robots to be used in elementary school classrooms for learning activities. The goal of this project will be to create a charging station for the Cellulo robots to ease the mass charging and on-off control of a Cellulo Swarm.
The desired outcome would be a charger base connected to a power supply where a group of Cellulo robots can be placed to be charged and a switch to trigger the robots ON.
The student will :
- study the different alternatives for the most suitable contact design.
- modify the power circuitry accordingly.
- build a prototype
Prerequisites: Experience in the following skills or interest in learning: electronics design, mechanical design, prototyping
Contact: hala.khodr (at) epfl.ch
[Master/ Bachelor Semester] Modular Cellulo top:
In the Cellulo project, we are designing tangible swarm robots to be used in elementary school classrooms for learning activities. In its current version, the Cellulo top has illuminated touch buttons. In this project, we would like to have a modular top which will allow us to add/change peripherals, such as an illuminated touch buttons, a force sensor, a 9-axis IMU, a gripper, an ACM. The student will:
- Re-design the top to allow an easy modular change
- Design the electronics and printed circuit board necessary for the communication to the main microcontroller.
- Choose one example of a peripheral and Implement the firmware to demonstrate the idea
Prerequisites: Experience in the following skills or interest in learning. Embedded systems design, electronics design, mechanical design, prototyping
Contact: hala.khodr (at) epfl.ch , arzu.guneysu (at) epfl.ch