Interactive courseware to support blended learning

Covid-19 has been a game-changer in engineering education at the higher education level. Even beyond the pandemic, blended learning is there to stay. The design, execution, and delivery of blended learning can be supported by a plethora of fastdeveloping educational technology. In this paper we share the experience of the evolution of one engineering course "Uncertainty in Artificial Intelligence" from a rather traditional design strongly relying on face-to-face interaction to a fully blended technology-supported course. In particular, we share the experience of how an interactive courseware platform called "Nextbook", which allows students and teacher to directly interact on the course material, supported the design, implementation, and delivery. Student experiences measured using a questionnaire are supplemented with teacher experiences to present the following "lessons learnt": A well-chosen platform can help students find clear structure in a mix of types of material, and social annotation features make it possible to connect discussion and questions and answers directly to the course material. Further efforts are needed for engaging students to actively use the features of interactive courseware platforms

Interactive courseware to connect discussion to course material: So what?

Presentation delivered at OERxDomains Conference, 21st-22nd April, 2021 (online)

The Doctoral Symposium in Engineering Education Research at SEFI 2023

The 7th SEFI Doctoral Symposium in Engineering Education Research, held at the campus of Technological University Dublin on Sunday, September 10th, preceded the SEFI 2023 Annual Conference. In all, 37 Ph.D. researchers attended, which is a record number for this event. They came to share and further probe their Ph.D. research topics and plans of study and to strengthen and extend their professional networks. During this full and intense day, 27 established scholars provided the Ph.D. researchers with personal feedback and ideas regarding their research. The highlight, according to the Ph.D. student participants, was the warm and enthusiastic reception they received from the well-established seniors of the global engineering education research community. Although SEFI is a European organization, the Ph.D. researchers and senior advisers who attended travelled to Ireland for this event from Africa, Australia, and South and North America, and from all over Europe

Domain Specific Language for Geometric Relations between Rigid Bodies targeted to robotic applications

This paper presents a DSL for geometric relations between rigid bodies such as relative position, orientation, pose, linear velocity, angular velocity, and twist. The DSL is the formal model of the recently proposed semantics for the standardization of geometric relations between rigid bodies, referred to as `geometric semantics'. This semantics explicitly states the coordinate-invariant properties and operations, and, more importantly, all the choices that are made in coordinate representations of these geometric relations. This results in a set of concrete suggestions for standardizing terminology and notation, allowing programmers to write fully unambiguous software interfaces, including automatic checks for semantic correctness of all geometric operations on rigid-body coordinate representations. The DSL is implemented in two different ways: an external DSL in Xcore and an internal DSL in Prolog. Besides defining a grammar and operations, the DSL also implements constraints. In the Xcore model, the Object Constraint Language language is used, while in the Prolog model, the constraint are natively modelled in Prolog. This paper discusses the implemented DSL and the tools developed on top of this DSL. In particular an editor, checking the semantic constraints and providing semantic meaningful errors during editing is proposed.Comment: Presented at DSLRob 2012 (arXiv:cs/1302.5082

Town of Lamoine Maine 2001 Annual Report

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Learning analytics for co-creation and interactive courseware

Presentation delivered at the Carnet Users Conference, 27th October, 2021.The educational landscape is evolving quickly, in tandem with society and in response to the challenges presented by today’s workplace (see, e.g. Germaine et al., 2016; Chalkiadaki, 2018). With these shifting expectations for teaching come new requirements for the tools that support the learning process. Educational technology (edtech) can be part of the answer to many of today’s challenges in higher education (Smith and Traxler, 2022). Technology is an enabling force that could unlock unseen potential in the digitally-literate student population. Yet, even with the increasing adoption of digital tools, and the global pandemic that forced teaching online across the world (Traxler et al., 2020), the promise of a significant increase in study performance or effectiveness remains to be realised. The authors have been working on a European Erasmus+ funded project, entitled “Co-created Interactive Courseware” (CIC), which planned to take a holistic, end-to-end, student-centric approach in improving student experiences and optimising study performance, by uniquely combining three solutions: (1) a social learning environment where students can help each other learn and track their own progress; (2) a fully automated publishing flow where authors can publish their existing (static) courseware and thus create interactive, co-creation-enabled textbooks with zero technical overhead; and (3) a learning analytics engine offering the educator insights into the full learning trajectory of their students. The project aims to establish the pedagogical backdrop against which these newly available tools could be implemented in future courses, and to create a methodology around co-creation and, crucially for this presentation, learning analytics that could be applied across multiple educational contexts. Unfortunately, learning platforms have thus far failed to unlock the potential benefits of co-creative learning. Too often, technical solutions are either limited in flexibility and thus are applicable only to narrowly defined types of content. These come with a significant technical burden for the educator in terms of initial setup, configuration, management, and ease-of-use, especially for those lacking in digital literacy, they lack the necessary controls, leaving little room for creativity, inspiration and research. Difficult to use solutions are often a time sink, diminishing the promise of such technology, especially for large groups of students. One immediate advantage of the successful implementation of co-creation or interactive software is that it creates valuable data traces that can be used to measure aspects of learning behaviour in real-time, at an individual level, with minimal overhead. Students can be rewarded for positive interactions in various ways. Positive interactions can include signalling a mistake in the course text, fixing an error, adding a link to an external resource, rewriting parts of the text, or adding new content altogether. For this, students can be rewarded directly with grades, or indirectly through gamification with temporary badges, permanent trophies, an increased ranking relative to their fellow students, unlocked features, or just the inherent pleasure of enabling one another's learning through the display of their own aptitude in the area of study. By measuring how students learn and interact with each other and their courseware, the student, the teacher, and higher education institution (HEI) can get a deeper, more objective and data-driven insight into the study process. This insight can be used to optimise the learning process and to tailor student support to the individual student. Also, for learning behaviour that is associated with lower chances of success, the teacher, teaching assistant, or student support services could receive advance warnings; or the system could implement automated, anonymous remedial measures. In short, this project aims to have a positive impact in how students and teachers interact with each other, how learning resources are consumed, and how these resources are continuously improved. This presentation looks in depth into the analysis of the data that can be generated in co-creative interactive learning contexts

Small data as a conversation starter for learning analytics: Exam results dashboard for first-year students in higher education

Purpose - The purpose of this paper is to draw attention to the potential of “small data” to complement research in learning analytics (LA) and to share some of the insights learned from this approach. Design/methodology/approach - This study demonstrates an approach inspired by design science research, making a dashboard available to n=1,905 students in 11 study programs (used by n=887) to learn how it is being used and to gather student feedback. Findings - Students react positively to the LA dashboard, but usage and feedback differ depending on study success. Research limitations/implications - More research is needed to explore the expectations of a high-performing student with regards to LA dashboards. Originality/value - This publication demonstrates how a small data approach to LA contributes to building a better understanding

Generalizing predictive models of admission test success based on online interactions

This article belongs to the Special Issue Sustainability of Learning AnalyticsTo start medical or dentistry studies in Flanders, prospective students need to pass a central admission test. A blended program with four Small Private Online Courses (SPOCs) was designed to support those students. The logs from the platform provide an opportunity to delve into the learners' interactions and to develop predictive models to forecast success in the test. Moreover, the use of different courses allows analyzing how models can generalize across courses. This article has the following objectives: (1) to develop and analyze predictive models to forecast who will pass the admission test, (2) to discover which variables have more effect on success in different courses, (3) to analyze to what extent models can be generalized to other courses and subsequent cohorts, and (4) to discuss the conditions to achieve generalizability. The results show that the average grade in SPOC exercises using only first attempts is the best predictor and that it is possible to transfer predictive models with enough reliability when some context-related conditions are met. The best performance is achieved when transferring within the same cohort to other SPOCs in a similar context. The performance is still acceptable in a consecutive edition of a course. These findings support the sustainability of predictive models.This work was partially funded by the LALA project (grant no. 586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP). The LALA project has been funded with support from the European Commission. In addition, this work has been partially funded by FEDER/Ministerio de Ciencia, Innovación y Universidades—Agencia Estatal de Investigación/project Smartlet (TIN2017-85179-C3-1-R) and by the Madrid Regional Government through the project e-Madrid-CM (S2018/TCS-4307). The latter is also cofinanced by the Structural Funds (FSE and FEDER). It has also been supported by the Spanish Ministry of Science, Innovation, and Universities, under an FPU fellowship (FPU016/00526