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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.

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.

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.

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.

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.