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This paper presents findings on participants of a massive open online course named "Educational Robotics for all" developed under Open edX platform. The document describes the organization and structure of the MOOC and some of its preliminary results. As an example, Module 2 about Gender and STEAM is presented and discussed.

Tales abound in popular magazines and academic publications of technologically savvy adolescents with near-magical powers. Whether celebratory or despairing, however, claims and assumptions about these roaming packs of digitally-nimble young people are often based on flawed – or nonexistent – research. Concurrently, educators may find themselves pursued by shadowy specters of what “everybody knows” about teaching, learning, students, and all things brain-ish. “But, does it really matter,” you might ask, “if a student believes that humans have a shorter attention span than a goldfish?” Short answer: Yes, it does matter, indeed. This presentation will propose a longer answer to that question by unearthing the sources of these legends, shining the light of day on the true nature of such creatures, and offering strategies for combating the pernicious effects of their influence. (Torches and pitchforks optional.)

The pedagogy of science, technology, engineering, arts, and mathematics (STEAM) can be easily developed by using robotics and computational thinking tools. Also, inclusion and integration of diverse groups of students can be promoted using these tools. Today we can find many tools for teaching robotics. This kind of tools allow us to promote innovation and motivation of students. In this way, students will be able to work during the learning process in an innovative and motivating way. Since it is increasingly common to find robots in our daily lives, it is important to integrate robots into education as well. There are already cooking robots, autonomous cars, vacuum cleaner robots in houses and gardens, or prostheses. This paper describes a course focused on a combination of teaching methodologies, educational robotics tools, and a student learning management methodology, all within an inclusive framework to strengthen the presence of women and other under-represented groups in engineering.

The Laboratory of Instrumentation for Measurement started its activities in September of 2000 and initially was intended to support the Instrumentation for Measurement course of the Mechanical Engineering Degree of Faculty of Engineering, University of Porto, Portugal. Since that time its multidisciplinary team has promoted and enlarged the scope of responsibility and interests. During these 18 years, the lab has been involved in experimental engineering education, mainly in the field of mechanical engineering but also in other engineering disciplines and fields such as medicine, nutrition, rehabilitation, sports, and multimedia, for example. This intense involvement attracted and helped to support students for master’s theses and final projects, Ph.D. students, and younger learners within non-formal learning programs, all focused on experimental tasks, project-based learning, and mentoring and coaching approaches. The lab has also been involved in informal learning initiatives and has supported regular R&D activities of a research group (funded by the National Science Foundation), while maintaining high quality standards for its students. This work describes the work of the lab and examines results, not in an analytical perspective, but based on real education-related outputs.

This article proposes a set of guidelines to inform the development of high quality laboratory assignments for implementation with virtual- or augmented-reality applications or remotely located equipment. The suggested criteria are based on validated instructional design practices and accreditation standards for engineering degree programs, and can be used to evaluate the design of laboratory instruction, as well. An example assignment is used to illustrate how the specific components of the proposed guidelines might be operationalized to design a new assignment or to improve an existing one. The authors anticipate multiple iterations of these guidelines and welcome constructive feedback for their improvement.

Engineering education is facing a changing world in which how one thinks is becoming more important than what one thinks; that is, our course content is important but constantly changing and we need to help students learn how to think about that content. Today’s students have grown accustomed to immediate rewards, multi-channel stimuli, and rapid-fire communications. As a result, they are often impatient and suffer a lack of focus. When reflection is called for in the learning process - a time consuming practice - students may find it difficult to overcome the conflict between their typically speedy management of priorities and the focused, time-intensive thinking required to acquire a strong foundation of declarative knowledge. Therefore, the exploration of tools to facilitate the formation of deep knowledge structures is essential. One instructional strategy that shows promise is the use of concept mapping, a learning activity that requires students to explain their understanding of important ideas and the relationships among those ideas. This paper describes a pilot project to integrate concept mapping into a Mechanical Engineering Course and the preliminary results of that project. This project has been established within the Working Group of “Tools for Developing High Order Thinking Skills”, of the Portuguese Society for Engineering Education, in which the first author is the leader and the other two co-authors, are working group members.

This book constitutes the refereed proceedings of the 7th International Conference on Computer Supported Education, CSEDU 2015, held in Lisbon, Portugal, in May 2015. The 34 revised full papers presented together with an invited talk were carefully reviewed and selected from 196 submissions. The papers address topics such as information technologies supporting learning; learning/teaching methodologies and assessment; social context and learning environments; domain applications and case studies; and ubiquitous learning.

This study examined possible relationships between self-assessment scores provided by students for a laboratory exam compared with the scores assigned by the instructor. Six years' worth of paired scores were analyzed, along with other variables such as time in the degree program, sex, test performance, final marks, and whether the student had enrolled in the course more than once. Statistical analysis found that while students were relatively accurate in their assessments, high-performing students were notably more likely to underestimate their performance, while students who did less well typically over-estimated theirs. Accuracy varied significantly between the mid-range performers and both the high and low performers, with those students who scored near the mean giving themselves scores that closely matched those assigned by the instructor. Factors that appear to have little influence on self-assessment accuracy included sex, time in the degree program, and multiple enrollments in the course. Further studies may examine these factors more closely, as well as stratifying the results of the entire set of records for clarification of possible relationships.

Why do some programs of study build lab work into the curriculum? Although courses across a wide variety of disciplines traditionally include lab work, there is little consistency in how those labs (even within the same discipline) are configured and even less conversation about the value of lab work to student learning. Generally speaking, labs are typically intended to help students develop procedural skills (manipulate tools, for example), explore the processes of scientific experimentation, and link theory to practice [1,2]. While most educators would agree that these goals — procedural skills, experimentation, and theoretical understanding — are listed from least-to-most important, the unfortunate truth is that they are also listed in order of easiest-to-most-difficult for both teaching and learning. This paper will describe how the learning activities and the technological configurations of labs can be re-thought to address challenges for learning and for access.

William G. Bowen. Higher Education in the Digital Age. Princeton: Princeton University Press, 2013. ISBN 9780691159300. $26.95.This slim volume comprises William Bowen’s 2012 Tanner Lectures on Human Values at Stanford University, and includes responses from John Hennessy, President of Stanford; Howard Gardner, a Harvard University psychologist; Andrew Delbanco, Director of American Studies at Columbia University; and Daphne Koller, Stanford professor of computer science and co-founder of Coursera. Bowen presents two lectures, the first addressing “the cost disease” facing higher education, and the second discussing the prognosis that technology, specifically online education, can cure that disease, entering among other debates the efficacy of massive open online courses (MOOCs).

Engineering education is facing a changing world in which how one thinks is becoming more important than what one thinks; that is, our course content is important but constantly changing and we need to help students learn how to think about that content. One instructional strategy that shows promise is the use of concept mapping, a learning activity that requires students to explain their understanding of important ideas and the relationships among those ideas. This paper describes a pilot project to integrate concept mapping into a Mechanical Engineering Course and the preliminary results of that project.

The importance of engagement for learning, specifically related to online coursework, is discussed in this paper. The cognitive basis for engagement and instructional strategies for integrating it into coursework are described, as well as the challenges that instructors face in creating robust learning environments. The roles of teacher and learner must also evolve to accommodate these new models, with increased student responsibility and accountability. Finally, practical examples are given to demonstrate strategies for enhanced engagement in online coursework.

The teaching of writing has never been more important than in this “age of information.” Teachers of composition realize this and have begun to avail themselves of technological innovations to facilitate the writing process and prepare their students to be effective communicators. One of these innovations is the word processor. This article describes a representative sample of recent research on the uses of word processing in composition instruction and discusses some of the issues surrounding its adoption as a writing tool. Questions for future research developed from the analysis of prior studies are also discussed.

International audience; Individuals including adults would mostly need to keep their knowledge and skill updated in order to maintain or advance in their job. Computational Thinking (CT) is considered as one of the 21st century skills that adults must learn or develop. There exists some challenges in promoting adults’ CT, including lack of suitable methods that take into account individual’s needs and motivation. Educational games, adaptive educational games in particular, are one prospective solution to these challenges. This research presents a study investigating effects of an adaptive educational game, called AutoThinking, on adults gain of CT skills and conceptual knowledge. The game was used by adults in a Master class at the Université de Lille in France. Pre- and Post- tests results analysis has shown that the game helped the adults to acquire knowledge on the Computational Thinking: 92% of adults have answered correct at least 4 questions out of 7 in the post-test versus only 34% of learners in the pre-test.

International audience; While setting up a Massive Open Online Course for Lifelong Learners, the choice of the most adequate Learner Model for this most current contextis paramount: not all Learner Models are created equal, despite their overalladded value to facilitate the learner’s follow-up, course content personalizationand trainers/teachers’ practices in various Learning Environments. This systematic review of literature defines, compares, and highlights eight features of interest of Learner Models for Massive Open Online Courses from a LifelongLearning perspective. It discerns 17 of the most-current, existing Learner Models out of 442 search results. It concludes on the four most adequate, and current Learner Models in this context. In addition, we study how they handle thelearning experience personalization. This work is primarily dedicated to MOOCdesigners/providers, pedagogical engineers and researchers who meet difficulties to model and evaluate MOOC’s learners using Learning Analytics.

This research deals with how to foster the attitude of the mathematician facing a problem. In this regard, we identified some fundamental steps, from the initial understanding to its solution, and some key attitudes, such as critical thinking and insight. The steps have been translated into the phases of a digital interactive storytelling in mathematics (DIST-M), while the attitudes have been embodied as characters/roles within the story. The whole didactic design is based on collaborative scripts, and evolves according to the interactions between the characters and the stimuli coming from the expert. In the paper we briefly report the design, it’s implementation using ICT tools,a taste of the analysis conducted and the results arising from a DIST-M trial involving 26 first-year high school students.

International audience; Knowledge acquisition is a crucial problem for the design of Intelligent Tutoring Systems (ITSs). To overcome this problem, authoring tools have been proposed. Over two dozen of authoring tools have been built since the earliest days of ITS, but each of them focuses on a particular kind of ITSs such as constraint-based tutors or model-tracing tutors. In the context of the AMBRE project, we are interested in ITSs teaching problem-solving methods. Such ITSs enable learners to acquire a specific method in problem-solving. Despite of the variety of existing authoring tools, these tools do not meet our needs either because approach adopted do not match to AMBRE principle or because they do not allow to represent all knowledge needed to design an AMBRE ITS. We propose AMBRE-KB, an authoring tool to help authors to elicit knowledge needed for the design of AMBRE ITSs. This tool supports the acquisition of knowledge to be taught, and the description of problems to be solved. We present the authoring process and illustrate it using French verb conjugation domain. A preliminary evaluation shows that AMBRE-KB is successful in producing domains models but more thorough evaluation is planned.

With its 5,006,202 articles, 49 millions of registered people and on average 800 new articles per day, Wikipedia provides a knowledge base for teachers and instructional designers to build didactic materials. As a matter of fact, teachers often consult this encyclopaedia to arrange, integrate or enrich their courses. Moreover, with the exponential growth of the Internet, didactic materials are freely available and usable by teachers, instructional designers and students from Learning Objects Repositories such as Mertlot or Ariadne and others. On the other hand, designing and delivering a new course is a crucial task for teachers, who have to face two main problems: building or retrieving and sequencing learning materials. Retrieving or building learning materials requires a great effort and is time-consuming, while sequencing requires an accurate didactic project. In this paper we present a sequencing engine of learning materials, embedded in the Wiki Course Builder system, a system capable of retrieving and sequencing Wikipedia web pages, taking into account both the teacher model based on the Grasha teaching styles and on a social didactic approach. The main goal is to support teachers building on-the-fly courses, i.e., building courses quickly, with a few clicks of the mouse. An important feature of the system is represented by its ability to allow teachers to interact with the recommended learning path through a graph-based interface where they can directly modify the proposed learning path, adding or deleting Wikipedia pages. A first questionnaire has been submitted to a sample of teachers with encouraging results.

Selecting and sequencing a set of Learning Objects (LOs) to build a course may turn out to be quite a challenging task. In this paper we focus on such an aspect, related to the verification and respect of the relationships of pedagogical dependence existing between two LOs added to a course (meaning that if a given LO has another one as "pre-requisite", then any sequencing of the LOs in the course will need to have the latter LO taken by the learners before of the former). In our approach the sequencing of LOs in the course can still be managed by the instructor, basing on her/his taste and preferences, yet s/he can also be helped by a set of suggestions, related to the pre-requisite relationships existing among the LOs selected for the course. Such suggestions (such relationships, in effect) can be computed automatically and provide the instructor with significant help and guidance. We show a light-weight formalization of the LO, and how it can be "represented" by a set of WikiPedia Pages ("topics"); then we show how such set of topics, together with a set of relevant hypotheses we previously defined, can help establish the dependence relationship existing between two LOs. In this endeavor we exploit the classification in categories available for the WikiPedia topics, and obtain interesting results for our framework, in terms of precision and recall of the dependence relationships.

International audience; In a learning situation, gamification is a way to motivate learners and enhance their participation to learning activities by adding game elements. But it still pays little attention to the individual differences among learners’ preferences as players. This paper presents a generic and adaptive gamification system that can be plugged on various learning environments. The game elements can be automatically adapted, based on an analysis of the interaction traces. The architecture of the proposed system is based on the separation between the control of the pedagogical elements and the control of the game elements. To support the adaptivity of the game elements, we refer to a user model based on a list of player types. The user model is initialized thanks to the traces of interactions, and finally used to predict which game feature will be relevant for the user.