Graasp Desktop: Supporting Digital Education in Underconnected Schools in Africa

Over the past three years, the Go-Lab Goes Africa (GO-GA) initiative has focused on promoting and implementing digital education in Africa. GO-GA is a Horizon 2020 innovation action supported by the European Commission. One of the outcomes of this initiative is Graasp Desktop, an open-source, customizable, cross-platform desktop application aimed at delivering technology-enhanced learning to schools where internet connectivity is limited or inexistent. A mixed-method requirements elicitation process was conducted in three pilot countries—Benin, Kenya and Nigeria—comprising surveys with 109 teachers, as well as 14 school visits that included interviews with principals, teachers and personnel in charge of technology infrastructure. Key findings showed that (i) schools had unreliable internet connectivity, with approximately 27% of respondents reporting not having internet access at school and field visits revealing functional connections in only six schools; and (ii) students worked mostly on shared desktop devices, with 42% of respondents reporting that students worked in groups of five or more per device. Graasp Desktop’s design and software architecture are based on these findings, supporting multi-user offline use and dissemination of open educational resources. Conceived to be future-proof, Graasp Desktop seamlessly transitions between online and offline contexts. When connected, it allows teachers to explore, preview and download learning capsules created on Graasp’s authoring platform (graasp.eu). Learning capsules are educational resources comprising digital activities—such as virtual labs—that can be contextualized with text, images, and interactive content. Once downloaded, teachers can make use of these capsules and share them with their students without the need of an internet connection. Furthermore, Graasp Desktop can record learning analytics, allowing (i) students to reflect on their learning, (ii) teachers to visualize and get insights into the offline activity of both individual students and entire cohorts, and (iii) researchers to conduct studies with data obtained in underconnected schools.

A Bayesian Model of Individual Differences and Flexibility in Inductive Reasoning for Categorization of Examples

Pierre Dillenbourg, Louis Pierre Faucon, Jennifer Kaitlyn Olsen

Inductive reasoning is an important educational practice but can be difficult for teachers to support in the classroom due to the high level of preparation and classroom time needed to choose the teaching materials that challenge students' current views. Intelligent tutoring systems can potentially facilitate this work for teachers by supporting the automatic adaptation of examples based on a student model of the induction process. However, current models of inductive reasoning usually lack two main characteristics helpful to adaptive learning environments, individual differences of students and tracing of students' learning as they receive feedback. In this paper, we describe a model to predict and simulate inductive reasoning of students for a categorization task. Our approach uses a Bayesian model for describing the reasoning processes of students. This model allows us to predict students' choices in categorization questions by accounting for their feature biases. Using data gathered from 222 students categorizing three topics, we find that our model has a 75% accuracy, which is 10% greater than a baseline model. Our model is a contribution to learning analytics by enabling us to assign different bias profiles to individual students and tracking these profile changes over time through which we can gain a better understanding of students' learning processes. This model may be relevant for systematically analysing students' differences and evolution in inductive reasoning strategies while supporting the design of adaptive inductive learning environments.

ASSOC COMPUTING MACHINERY2020

Using Educational Data From Teaching And Learning To Inform Teachers’ Reflective Educational Design In Inquiry-Based STEM Education

Denis Gillet, Maria Jesus Rodriguez Triana

Science, Technology, Engineering and Mathematics (STEM) education is recognized as a top priority for school education worldwide and Inquiry-based teaching and learning is identified as one of the most dominant approaches. To effectively engage individual students in Inquiry tasks, appropriate guidance needs to be provided, usually by combining different digital tools such online labs, data analysis tools and modelling tools. This is a cumbersome task for teachers to perform manually since it involves (a) assessing during the education design, the type and level of tool-supported guidance to be provided to students and (b) potentially refining this level and types to meet the guidance needs of individual students based on educational data from the delivery of the educational design. Thus, in our research we target to investigate how to support this process with educational data analytics methods and tools from both the design and the delivery of educational designs, that inform teachers’ decision making for systematic reflection. To this end, the contribution of this paper is the design and evaluation of a novel “Teaching and Learning” Analytics method and supporting research prototype tool, extending the scope of purely learning analytics methods, to (a) analyze inquiry-based educational designs in terms of the tool-supported guidance they offer and (b) relate these analyses to students' educational data that are already being collected by existing learning analytics systems, so as to increase teachers’ awareness and understanding and scaffold their reflection. A two-layer evaluation methodology was adopted to evaluate both the capacity of our method to analyze educational designs in terms of appropriate guidance as well as to investigate whether the insights generated by the method offer statistically significant indicators that impact students' activity during the delivery of these educational designs. The results obtained, based on real-life educational data, argue that the proposed method and tool can support teachers to accurately analyse Inquiry-based educational designs and receive meaningful insights to improve and tailor students' learning experiences. The insights of this work aim to contribute in the research field of cognitive data analytics for teaching and learning, by investigating new ways to combine analyses of the educational design and the students’ activity, so as to inform teachers’ reflective decision making from a holistic perspective

2019

Special issue on: Learning Analytics (Editorial)

Maria Jesus Rodriguez Triana

With the general technological advances of the recent years, current learning environments amass an abundance of data. Albeit such data offer the chance of better understand the learning process, stakeholders – learners, teachers and institutions – often need additional support to make sense of it (Dyckhoff et al., 2013; Macfadyen and Dawson, 2012). The acknowledgement of these needs is at the heart of the recent emergence of Learning Analytics (LA), a research area that draws from multiple disciplines such as educational science, information and computer science, sociology, psychology, statistics and educational data mining (Buckingham Shum and Ferguson, 2012). This multidisciplinarity in LA has motivated the work done by Ferguson (2012), which provides a first review of the drivers, development and challenges of this novel and young research area. Our understanding of learning analytics is based on the definition from the Society for Learning Analytics (SoLAR – Society for Learning Analytics1) which specifies that “Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs”. Since 2011, the Horizon reports list Learning Analytics as a hot topic in higher education and indicate the importance of data for this field (Johnson et al., 2011). Learning analytics are able to provide a fresh view on understanding of teaching and learning by observing patterns of complex data (Johnson et al., 2012). Furthermore, it will influence the evolution of higher education in a great measure. Nowadays, learners have access to a huge amount of online information having themselves the possibility of being content creators and information sharers. Therefore the quantity of available information grows in an exponential way, once that each and every citizen can access and produce information. For these purposes, learners have at their disposal many online resources, including LMSs, VLEs, MOOCs and many other online tools that facilitate the learning process and the development of competences. Taking into account these online learning facilities and therefore the learners’ acquisition of knowledge, it is also easier to measure and analyse their experiences by using learning analytics tools. Different online courses and institutions provide dashboards with information about student experiences, flaws and successes. Although the investigation of behavioural specific data makes learning analytics complex, the time comes to utilise personalised learning environments adapted to students learning paths, skills, previous knowledge, competences and motivation.