Are you willing to do your master or bachelor thesis or research internship under my supervision? that is possible. The ideal duration of the internship is from 3 to 6 months (7 to 15 ECTS). The target students are bachelor or master students in the field of computer science, knowledge engineering, data science, information technologies. Contract and monthly scholarships are available. The internship will all take place in English.

Please have a look at the list of open topics. Please reach out to me to discuss this further. 


The topics here described are:

  1. Using smartphones for Multimodal Learning Analytics
  2. Inspection Tool for Multimodal Recordings of learning experiences
  3. Multimodal chess-playing
  4. Exploratory Data Analysis with the Learning Pulse dataset

1) Using smartphones for Multimodal Learning Analytics

Smartphones have embedded multiple sensors such as: accelerometers, GPS, Microphones, Cameras, etc. For this project the task would be to develop an application that uses the data obtained by the sensors from smartphones in order to record Multimodal Learning experiences. Examples of learning experiences can be: dancing, gymnastics, martial arts, playing a musical instrument, public speaking, etc. The student can decide what type of learning task would like to make recordings from. The student also needs to do some recordings in order to test the developed application. For research purposes it would be better if the recordings are made using some experts and novices performing the learning tasks.

Expected results:

  • Development of a program able to extract data out of the sensors from a smartphone.
  • Development of a library class for smartphones that can connect to our multimodal recording tool (TCP and UDP socket connection).
  • Design the set-up for recording of the learning task
    • Which specific learning task will be recorded
    • Which characteristics of this learning task can and should be recorded
    • Which sensors of the smartphone are needed for the recording (We can provide other sensors, such as kinect, MYO armband, Leapmotion, etc, to improve the recording)
    • How should the user carry the smartphone while doing the recording
  • Creating a set of multimodal learning recordings.


2) Inspection Tool for Multimodal Recordings of learning experiences

Multimodal data is generally noisy and difficult to interpret and analyse. For this project the user will develop an application able to open multimodal recordings, where users can manually annotate specific sections of the recordings, save these sections in files that can be used for later analysis and/or for the use of machine learning. Finally the annotations and sections should be stored in a learning record store.

Expected results:

  • Development of ta tool with the following features:
    • Opening multimodal recordings
    • Plotting multimodal data
      • Recorded Values
      • Derivatives
      • Different frame rates
    • Playing videos and audios included in the recordings
    • The user should be able to select and tag sections of the recordings
      • The raw selected data should be saved in some type of file format (e.g. cvs), that can later be opened and used by softwares for statistical analysis (R, Excel) and machine learning libraries.
      • The tags together with the selections should also be stored in a learning record store.  

3) Multimodal chess-playing

The popular game of chess is an interesting learning scenario for investigating the true meaning of expertise in cognitive intense tasks. In artificial intelligence the game of chess is usually considered a search problem: find the optimal move taking into account the opponent’s reactions in all the possible configurations. Human however are not able of keeping track of all the combinatorial possibilities, and for this reason they adopt a search system which is much more based on heuristics or tactics. The scope of this multimodal application consists in untangling the strategy of the players by means of sensors data and multimodal learning analytics. The nature of this task is highly explorative. The multimodal application should be able to correlate the decisions taken by the players (i.e. a move in the chess board) within a particular state configuration of the game with the observed sensor data. The analysis can also look at different strategies adopted by different players and reason on the differences.

Expected results

  • Sensor capturing plays sessions of one sensor among Emotiv Insight EEG headset, Empatica E4 wristband or Eye tracking device
  • Correlate the sensor data with players’ moves an board configurations
  • Analysing recurrent patterns in the sensor data and peculiarities of each individual  player
  • Possible extension:
    • Compare two or more players
    • Scale to multiple integrated sensors

4) Exploratory Data Analysis with the Learning Pulse dataset

In spring 2016 at the Welten Institute, research centre of the Open University, took place  “Learning Pulse”, an exploratory study whose main aim was to discover whether physiological responses (their heart rate) and physical activity (step count) when associated with data about the learning activity (use of software applications) are predictive for learning performance.  Nine PhD students of the OU took part in the experiment wearing each of them a Fitbit HR tracker and having their computer activity tracked. In addition for each of them, also the geo-location and outdoor meteorological conditions were tracked. To monitor the level of learning performance, the participants, participate had to self report every hour during their working time their Main Activity and their perceived level of Productivity, Stress, Challenge and Abilities. The former two indicators can be combined to calculate the Flow, a famous construct in psychology which can be used as Learning performance indicator. The data collection in the experiment lasted five weeks and produced a dataset about 10,000 records. Each of those records represents a five minutes learning interval having ~430 attributes, the majority of which are sparse.

This Master thesis topic consists in performing Exploratory Data Analysis on the Learning Pulse data set in order to:

  • find meaningful patterns, interesting correlation and insights in collected data
  • find a structured approach to treat sparse data preserving the time dependency
  • learn statistical regression models which are able to predict at least one of the performance indicators
  • find interesting visualisations of the data for self-awareness and reflection

This topic requires familiarity with descriptive statistics, data analysis and preferably with machine learning. To accomplish these task you are required to use one data analysis tool such as R, SPSS, Python.

5) Internet of Things (IoT) devices for Learning

This internship will deal with the crafting of Internet of Things devices for educational settings With Raspberry Pi and sensors available in the lab (Myo, Leap motion, Fitbit)

6) Cloud applications for data analysis

Learn to set up scalable applications for data processing in Cloud Platforms

7) Analytics visualisations and user dashboards

Learn to use visualisation libraries like D3.js, etc. 

8) Augmented Reality feedback applications

Use Unity3D for Hololens to show data-driven feedback for learning