On the domain-specificity of mindsets: The relationship between aptitude beliefs and programming practice

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Deliberate practice is important in many areas of learning, including that of learning to program computers. However, beliefs about the nature of personal traits, known as mindsets, can have a profound impact on such practice. Previous research has shown that those with a fixed mindset believe their traits cannot change; they tend to reduce their level of practice when they encounter difficulty. In contrast, those with the growth mindset believe their traits are flexible; they tend to maintain regular practice despite the level of difficulty. However, focusing on mindset as a single construct focused on intelligence may not be appropriate in the field of computer programming. Exploring this notion, a self-belief survey was distributed to undergraduate software engineering students. It revealed that beliefs about intelligence and programming aptitude formed two distinct constructs. Furthermore, the mindset for programming aptitude had greater utility in predicting software development practice, and a follow-up survey showed that it became more fixed throughout instruction. Thus, educators should consider the role of programming-specific beliefs in the design and evaluation of introductory courses in software engineering. In particular, they need to situate and contextualize the growth messages that motivate students who experience early setbacks

The THREAT-ARREST Cyber-Security Training Platform

Cyber security is always a main concern for critical infrastructures and nation-wide safety and sustainability. Thus, advanced cyber ranges and security training is becoming imperative for the involved organizations. This paper presets a cyber security training platform, called THREAT-ARREST. The various platform modules can analyze an organization’s system, identify the most critical threats, and tailor a training program to its personnel needs. Then, different training programmes are created based on the trainee types (i.e. administrator, simple operator, etc.), providing several teaching procedures and accomplishing diverse learning goals. One of the main novelties of THREAT-ARREST is the modelling of these programmes along with the runtime monitoring, management, and evaluation operations. The platform is generic. Nevertheless, its applicability in a smart energy case study is detailed

On the limits of engine analysis for cheating detection in chess

The integrity of online games has important economic consequences for both the gaming industry and players of all levels, from professionals to amateurs. Where there is a high likelihood of cheating, there is a loss of trust and players will be reluctant to participate — particularly if this is likely to cost them money. Chess is a game that has been established online for around 25 years and is played over the Internet commercially. In that environment, where players are not physically present “over the board” (OTB), chess is one of the most easily exploitable games by those who wish to cheat, because of the widespread availability of very strong chess-playing programs. Allegations of cheating even in OTB games have increased significantly in recent years, and even led to recent changes in the laws of the game that potentially impinge upon players’ privacy. In this work, we examine some of the difficulties inherent in identifying the covert use of chess-playing programs purely from an analysis of the moves of a game. Our approach is to deeply examine a large collection of games where there is confidence that cheating has not taken place, and analyse those that could be easily misclassified. We conclude that there is a serious risk of finding numerous “false positives” and that, in general, it is unsafe to use just the moves of a single game as prima facie evidence of cheating. We also demonstrate that it is impossible to compute definitive values of the figures currently employed to measure similarity to a chess-engine for a particular game, as values inevitably vary at different depths and, even under identical conditions, when multi-threading evaluation is used

Novelty detection system for a social robot

This Thesis presents a system that allows a social human-interactive robot to be able to actively learn novel stimuli presented to it, expanding its knowledge base. The system detects unknown patterns and decides when those patterns are worth being learned by the robot. The system architecture leans on novelty detection algorithms, that serve to implement two steps: rst ltering noise entries; and then evaluating if the entries that have passed the noise lter are known by the existing model, or on the contrary, they are novel entries that are worth learning. When a novel entry is identi ed, the system activates the learning process to update the model with this new data. The novelty detection system is evaluated in the pose learning domain. The dataset is composed by 28 users that teach 3 di erent poses to the system. In the experiments, we compare the performance of four di erent novelty detection algorithms for this task. We rst evaluate the noise lter by analyzing how many entries from the same pose have to be shown to the robot to pass the lter. The second step is tested training the system with one of the poses, then evaluating if the algorithms are able to detect test entries from other poses as novel. A third experiment tests our system for detecting in-class novelties. The results show that the performances vary between the novelty detection algorithms. The best performance is achieved by GMM, with a 86% F score for detecting new poses and a 80% F score to detect variations within poses. This novelty detection system opens the door for robotic systems to be able to act as active learners, making their own decisions about when it is worth to learn from new stimuli. Additionally, to the extent of our knowledge, there is no reference on Novelty Detection for pose recognition in a Human-Robot interactive application, so this work is a novelty itself.Ingeniería Electrónica Industrial y Automátic

Case studies of personalized learning

Deliverable 4.1, Literature review of personalised learning and the Cloud, started with an evaluation and synthesis of the definitions of personalized learning, followed by an analysis of how this is implemented in a method (e-learning vs. i-learning, m-learning and u-learning), learning approach and the appropriate didactic process, based on adapted didactic theories. From this research a list of criteria was created needed to implement personalised learning onto the learner of the future. This list of criteria is the basis for the analysis of all case studies investigated. – as well to the learning process as the learning place. In total 60 case studies (all 59 case studies mentioned in D6.4 Education on the Cloud 2015 + one extra) were analysed. The case studies were compared with the list of criteria, and a score was calculated. As a result, the best examples could be retained. On average most case studies were good on: taking different learning methods into account, interactivity and accessibility and usability of learning materials for everyone. All had a real formal education content, thus aiming at the core-curriculum, valuing previous knowledge, competences, life and work skills, also informal. Also the availability of an instructor / tutor or other network of peers, experts and teachers to guide and support the learning is common. On the other hand, most case studies lack diagnostics tests as well at the start (diagnostic entry test), during the personalized learning trajectory and at the end (assessment at the end). Also most do not include non-formal and informal learning aspects. And the ownership of personalized learning is not in the hands of the learner. Five of the 60 case studies can as a result be considered as very good examples of real personalized learning

The Finnish matriculation examination in biology from 1921 to 1969 – trends in knowledge content and educational form

The history and evolution of science assessment remains poorly known, especially in the context of the exam question contents. Here we analyze the Finnish matriculation examination in biology from the 1920s to 1960s to understand how the exam has evolved in both its knowledge content and educational form. Each question was classified according to its topic in biology, and its cognitive level by Bloom’s taxonomy. Overall, the exam progressed from a rather dichotomous test of botany and zoology to a modern exam covering biology from biochemistry to environmental science, reflecting the development of biology as a scientific discipline. The contribution of genetics increased steadily, while ecology witnessed a decline and a renaissance during the same time period. The biological profile of the questions was established by the 1950s. The educational standard and cognitive demand of the questions was always high and established by the 1940s.Peer reviewe

I'm sorry to say, but your understanding of image processing fundamentals is absolutely wrong

The ongoing discussion whether modern vision systems have to be viewed as visually-enabled cognitive systems or cognitively-enabled vision systems is groundless, because perceptual and cognitive faculties of vision are separate components of human (and consequently, artificial) information processing system modeling.Comment: To be published as chapter 5 in "Frontiers in Brain, Vision and AI", I-TECH Publisher, Viena, 200