Bringing Computational Thinking to STEM Education

AbstractToday, as advanced technologies and cloud computing tools emerge, it is imperative that such innovations are sustained with knowledge and skill set among STEM educators and practitioners. In this paper, the author reports on a project, HBCU-UP II, that works on bringing Computational Thinking to Science, Technology, Engineering, and Mathematics (STEM) disciplines. A Computational-Thinking based strategy is adopted to enforce thinking computationally in STEM gate-keeping courses. The paper presents framework, implementation and outcomes. This on-going project contributes to efforts to establish computational thinking as a universally applicable attitude that is meshed within STEM conversations, education, and curricula. This paper will be particularly useful for researchers interested in Computational Thinking and its applications in STEM education, in particular and higher education in genera

Teaching Computational Thinking

Computational thinking is a fundamental skill for everyone, not just computer scientists. Computational thinking is the thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can be effectively carried out by an information processing agent. Teaching Computational Thinking introduces the fundamental principles of communicating computing to learners across all levels. The book delves into the philosophical and psychological foundations of computer science as a school subject as well as specific teaching methods, curriculum, tools, and research approaches in computing education. This book is intended as a guide and teaching companion for pre-service and in-service computer science teachers

Predicting and Improving Performance on Introductory Programming Courses (CS1)

This thesis describes a longitudinal study on factors which predict academic success in introductory programming at undergraduate level, including the development of these factors into a fully automated web based system (which predicts students who are at risk of not succeeding early in the introductory programming module) and interventions to address attrition rates on introductory programming courses (CS1). Numerous studies have developed models for predicting success in CS1, however there is little evidence on their ability to generalise or on their use beyond early investigations. In addition, they are seldom followed up with interventions, after struggling students have been identified. The approach overcomes this by providing a web-based real time system, with a prediction model at its core that has been longitudinally developed and revalidated, with recommendations for interventions which educators could implement to support struggling students that have been identified. This thesis makes five fundamental contributions. The first is a revalidation of a prediction model named PreSS. The second contribution is the development of a web-based, real time implementation of the PreSS model, named PreSS#. The third contribution is a large longitudinal, multi-variate, multi-institutional study identifying predictors of performance and analysing machine learning techniques (including deep learning and convolutional neural networks) to further develop the PreSS model. This resulted in a prediction model with approximately 71% accuracy, and over 80% sensitivity, using data from 11 institutions with a sample size of 692 students. The fourth contribution is a study on insights on gender differences in CS1; identifying psychological, background, and performance differences between male and female students to better inform the prediction model and the interventions. The final, fifth contribution, is the development of two interventions that can be implemented early in CS1, once identified by PreSS# to potentially improve student outcomes. The work described in this thesis builds substantially on earlier work, providing valid and reliable insights on gender differences, potential interventions to improve performance and an unsurpassed, generalizable prediction model, developed into a real time web-based system

Automatic Algorithm Recognition Based on Programming Schemas and Beacons - A Supervised Machine Learning Classification Approach

In this thesis, we present techniques to recognize basic algorithms covered in computer science education from source code. The techniques use various software metrics, language constructs and other characteristics of source code, as well as the concept of schemas and beacons from program comprehension models. Schemas are high level programming knowledge with detailed knowledge abstracted out. Beacons are statements that imply specific structures in a program. Moreover, roles of variables constitute an important part of the techniques. Roles are concepts that describe the behavior and usage of variables in a program. They have originally been introduced to help novices learn programming. We discuss two methods for algorithm recognition. The first one is a classification method based on a supervised machine learning technique. It uses the vectors of characteristics and beacons automatically computed from the algorithm implementations of a training set to learn what characteristics and beacons can best describe each algorithm. Based on these observed instance-class pairs, the system assigns a class to each new input algorithm implementation according to its characteristics and beacons. We use the C4.5 algorithm to generate a decision tree that performs the task. In the second method, the schema detection method, algorithms are defined as schemas that exist in the knowledge base of the system. To identify an algorithm, the method searches the source code to detect schemas that correspond to those predefined schemas. Moreover, we present a method that combines these two methods: it first applies the schema detection method to extract algorithmic schemas from the given program and then proceeds to the classification method applied to the schema parts only. This enhances the reliability of the classification method, as the characteristics and beacons are computed only from the algorithm implementation code, instead of the whole given program. We discuss several empirical studies conducted to evaluate the performance of the methods. Some results are as follows: evaluated by leave-one-out cross-validation, the estimated classification accuracy for sorting algorithms is 98,1%, for searching, heap, basic tree traversal and graph algorithms 97,3% and for the combined method (on sorting algorithms and their variations from real student submissions) 97,0%. For the schema detection method, the accuracy is 88,3% and 94,1%, respectively. In addition, we present a study for categorizing student-implemented sorting algorithms and their variations in order to find problematic solutions that would allow us to give feedback on them. We also explain how these variations can be automatically recognized

OnCreate and the virtual teammate: an analysis of online creative processes and remote collaboration

This paper explores research undertaken by a consortium of 10 universities from across Europe as part of an EU Erasmus Strategic Partnership project called OnCreate. Recent research and experiences prove the importance of the design and implementation of online courses that are learner-centred, include collaboration and integrate rich use of media in authentic environments. The OnCreate project explores the specific challenges of creative processes in such environments. The first research phase comprises a comparative qualitative analysis of collaboration practices in design-related study programmes at the ten participating universities. A key outcome of this research was in identifying the shortcomings of the hierarchical role models of established Learning Management Systems (such as Moodle or Blackboard) and the tendency towards evolving 'mash-up' environments to support creative online collaboration

Old Meets New: Media in Education – Proceedings of the 61st International Council for Educational Media and the XIII International Symposium on Computers in Education (ICEM&SIIE'2011) Joint Conference

A conferência ICEM&SIIE'2011 foi organizada pela Universidade de Aveiro (Portugal) – membro do European Consortium of Innovative Universities – e pretendeu reunir investigadores, professores e outros profissionais, a nível nacional e internacional, em torno de um tema aglutinador que pretendeu despoletar e colocar a tónica da discussão na dualidade ―old/new‖, ou seja, os participantes foram convidados a discutir: - os media na educação em ambas as perspetivas, mais tradicionais ou modernas, com incidência numas ou noutras ou, ainda, numa perspetiva comparativa; - a conjugação, adaptação e adoção dos media consoante os contextos e objetivos de utilização; - o que os media implicam em termos de tecnologia, barreiras profissionais e /ou sociais; - a relação custo-benefício da utilização dos media em contexto de aprendizagem; - os media em função dos diversos contextos educativos e dos perfis de aprendizagem dos alunos. Para a conferência foram selecionados 76 artigos organizados em 15 sessões paralelas, 13 posters e 9 workshops. A conferência caracterizou-se pelo caráter internacional dos contributos, reunindo 38 artigos em português, 32 em língua inglesa e 6 em espanhol. Estas atas encontram-se organizadas de acordo com o programa da conferência. Em primeiro lugar incluem-se os artigos (full paper e short paper) por sessão, seguem-se os posters e, finalmente, o resumo relativo aos workshops.The ICEM&SIIE'2011 conference was organised by the University of Aveiro (Portugal) – a member of the European Consortium of Innovative Universities – and aimed at gathering researchers, teachers and other professionals, at national and international level, around a focal topic that might trigger and centre the discussion on the ―old/new‖ duality of media in education. Participants were invited to discuss: - old and new media in education, in isolation or comparatively; - how old and new media in education can be combined, adopted and adapted; - what old and new media in education imply in terms of technological, professional and social barriers; - what cost-benefit relationships old and new media in education entail; - how to compare old and new media in education given their particular educational contexts and the students' learning profiles. 76 papers were selected and organised in 15 paralel sessions, 13 posters and 9 workshops. The conference is characterized by the international character of contributions, gathering 38 papers in Portuguese, 32 in English and 6 in Spanish. These procedings are organised according to the programme of the conference. First we find the full and short papers, per session, then posters and finally the abstracts for the workshops