Identifying cheating users in online courses

Máster Universitario en en Investigación e Innovación en Inteligencia Computacional y Sistemas InteractivosStudents interact with online courses mainly in two ways: by reviewing the course materials and by solving exercises. However, there are cases in which student behaviour differs and tends to become more focused on solving exercises without looking at course materials. This type of interaction could be an indicative of unethical behavior, such as students who collaborate by sharing answers with one another or fake accounts that are used by students to obtain the correct answers for exercises. In this work, we propose several metrics to identify these two types of cheating based on cooccurring events and measures of interaction with the course. From the pool of accounts in the course, the pairs of accounts that solve exercises very close in time are considered to be potential collaborating accounts. The proposed metrics are computed for these pairs of accounts and K-means clustering is used to separate pairs of real students who collaborate with respect to students who use fake accounts to harvest the correct answers to exercises. A generalization accuracy over 95% to classify these types of cheating is achieved by using a Support Vector Machine (SVM

A new framework for the evaluation of locomotive motion datasets through motion matching techniques

International audienceAnalyzing motion data is a critical step when building meaningful locomotive motion datasets. This can be done by labeling motion capture data and inspecting it, through a planned motion capture session or by carefully selecting locomotion clips from a public dataset. These analyses, however, have no clear definition of coverage, making it harder to diagnose when something goes wrong, such as a virtual character not being able to perform an action or not moving at a given speed. This issue is compounded by the large amount of information present in motion capture data, whichposes a challenge when trying to interpret it. This work provides a visualization and an optimization method to streamline the process of crafting locomotive motion datasets. It provides a more grounded approach towards locomotive motion analysis by calculating different quality metrics, such as: demarcating coverage in terms of both linear and angular speeds, frame use frequency in each animation clip, deviation from the planned path, number of transitions, number of used vs. unused animations and transition cost. By using these metrics as a comparison mean for different motion datasets, our approach is able to provide a less subjective alternative to the modification and analysis of motion datasets, while improving interpretability

Conventional Mirror Therapy versus Immersive Virtual Reality Mirror Therapy: The Perceived Usability after Stroke

Background. Stroke is a widespread and complex health issue, with many survivors requiring long-term rehabilitation due to upper-limb impairment. This study is aimed at comparing the perceived usability of two feedback-based stroke therapies: conventional mirror therapy (MT) and immersive virtual reality mirror therapy (VR). Methods. The study involved 45 participants, divided into three groups: the stroke survivors (n=15), stroke-free older adults (n=15), and young controls (n=15). Participants performed two tasks using both MT and VR in a semirandom sequence. Usability instruments (SUS and NASA-TLX) were applied at the end of the activities, along with two experience-related questions. Results. The results indicated that both MT and VR had similar levels of perceived usability, with MT being more adaptable and causing less overall discomfort. Conversely, VR increased the perception of task difficulty and prevented participants from diverting their attention from the mirror-based feedback. Conclusion. While VR was found to be less comfortable than MT, both systems exhibited similar perceived usability. The comfort levels of the goggles may play a crucial role in determining the usability of VR for upper limb rehabilitation after stroke