DOP8_Qpp: Model to pre-process educational data

This paper addresses problem of reuse of data to help data anlysis and enhace data quality. The article describes a process to take over data that come from educational context. This process to reuse TEL data contains: major tasks, identified data properties and a set of quality criteria to reach these data properties. The objective of the process is to evaluate if data are reusable to serve learning analytics. This process is integrated in an existing data life cycle DOP8. The model was elaborated from several works with set of data since 2012 and from interviews with data-scientists. Also, we have administrated an on-line survey with data-scientists to evaluate feasibility of our proposal

DOP8_Qpp: Model to pre-process educational data

This paper addresses problem of reuse of data to help data anlysis and enhace data quality. The article describes a process to take over data that come from educational context. This process to reuse TEL data contains: major tasks, identified data properties and a set of quality criteria to reach these data properties. The objective of the process is to evaluate if data are reusable to serve learning analytics. This process is integrated in an existing data life cycle DOP8. The model was elaborated from several works with set of data since 2012 and from interviews with data-scientists. Also, we have administrated an on-line survey with data-scientists to evaluate feasibility of our proposal

Review of Current Student-Monitoring Techniques used in eLearning-Focused recommender Systems and Learning analytics. The Experience API & LIME model Case Study

Recommender systems require input information in order to properly operate and deliver content or behaviour suggestions to end users. eLearning scenarios are no exception. Users are current students and recommendations can be built upon paths (both formal and informal), relationships, behaviours, friends, followers, actions, grades, tutor interaction, etc. A recommender system must somehow retrieve, categorize and work with all these details. There are several ways to do so: from raw and inelegant database access to more curated web APIs or even via HTML scrapping. New server-centric user-action logging and monitoring standard technologies have been presented in past years by several groups, organizations and standard bodies. The Experience API (xAPI), detailed in this article, is one of these. In the first part of this paper we analyse current learner-monitoring techniques as an initialization phase for eLearning recommender systems. We next review standardization efforts in this area; finally, we focus on xAPI and the potential interaction with the LIME model, which will be also summarized below

The Big Five:Addressing Recurrent Multimodal Learning Data Challenges

The analysis of multimodal data in learning is a growing field of research, which has led to the development of different analytics solutions. However, there is no standardised approach to handle multimodal data. In this paper, we describe and outline a solution for five recurrent challenges in the analysis of multimodal data: the data collection, storing, annotation, processing and exploitation. For each of these challenges, we envision possible solutions. The prototypes for some of the proposed solutions will be discussed during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a two-day hands-on workshop in which the authors will open up the prototypes for trials, validation and feedback

Multimodal Challenge: Analytics Beyond User-computer Interaction Data

This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data