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

Implicit Theories and Self-efficacy in an Introductory Programming Course

Contribution: This study examined student effort and performance in an introductory programming course with respect to student-held implicit theories and self-efficacy. Background: Implicit theories and self-efficacy shed a light into understanding academic success, which must be considered when developing effective learning strategies for programming. Research Questions: Are implicit theories of intelligence and programming, and programming-efficacy related to each other and student success in programming? Is it possible to predict student course performance using a subset of these constructs? Methodology: Two consecutive surveys (N=100 and N=81) were administered to non-CS engineering students in I\c{s}{\i}k University. Findings: Implicit theories and self-beliefs are interrelated and correlated with effort, performance, and previous failures in the course and students explain failure in programming course with "programming-aptitude is fixed" theory, and also that programming is a difficult task for themselves.Comment: Programming Education. 8 page

A gentle transition from Java programming to Web Services using XML-RPC

Exposing students to leading edge vocational areas of relevance such as Web Services can be difficult. We show a lightweight approach by embedding a key component of Web Services within a Level 3 BSc module in Distributed Computing. We present a ready to use collection of lecture slides and student activities based on XML-RPC. In addition we show that this material addresses the central topics in the context of web services as identified by Draganova (2003)

A Nation Deceived: How Schools Hold Back America's Brightest Students, Volume II

Provides a comprehensive review of research on the academic acceleration of gifted students

Employment Program Components: Considerations for Modifying Programming for People Experiencing Homelessness

Employment programs typically include assessment, job search assistance, work readiness training, occupational training, job placement, and retention services. Research and experience suggest that programs can best meet individual needs, strengths, and interests by modifying these elements to ensure flexible services that support each individual's personal and developmental needs as they transition to employment. This best practice brief highlights a menu of enhancements and examples that programs may consider in providing supportive, flexible services for individuals experiencing or at risk of homelessness

Intelligent tutoring systems as tools for investigating individual differences in learning

The ultimate goal of this research is to build an improved model-based selection and classification system for the United States Air Force. Researchers are developing innovative approaches to ability testing. The Learning Abilities Measurement Program (LAMP) examines individual differences in learning abilities, seeking answers to the questions of why some people learn more and better than others and whether there are basic cognitive processes applicable across tasks and domains that are predictive of successful performance (or whether there are more complex problem solving behaviors involved)

Rose garden promises of intelligent tutoring systems: Blossom or thorn

Intelligent tutoring systems (ITS) have been in existence for over a decade. However, few controlled evaluation studies have been conducted comparing the effectiveness of these systems to more traditional instruction methods. Two main promises of ITSs are examined: (1) Engender more effective and efficient learning in relation to traditional formats; and (2) Reduce the range of learning outcome measures where a majority of individuals are elevated to high performance levels. Bloom (1984) has referred to these as the two sigma problem; to achieve two standard deviation improvements with tutoring over traditional instruction methods. Four ITSs are discussed in relation to the two promises. These tutors have undergone systematic, controlled evaluations: (1) The LISP tutor (Anderson Farrell and Sauers, 1984); (2) Smithtown (Shute and Glaser, in press); (3) Sherlock (Lesgold, Lajoie, Bunzo and Eggan, 1990); and (4) The Pascal ITS (Bonar, Cunningham, Beatty and Well, 1988). Results show that these four tutors do accelerate learning with no degradation in final outcome. Suggestions for improvements to the design and evaluation of ITSs are discussed

Recursive Percentage based Hybrid Pattern Training for Supervised Learning

Supervised learning algorithms, often used to find the I/O relationship in data, have the tendency to be trapped in local optima as opposed to the desirable global optima. In this paper, we discuss the RPHP learning algorithm. The algorithm uses Real Coded Genetic Algorithm based global and local searches to find a set of pseudo global optimal solutions. Each pseudo global optimum is a local optimal solution from the point of view of all the patterns but globally optimal from the point of view of a subset of patterns. Together with RPHP, a Kth nearest neighbor algorithm is used as a second level pattern distributor to solve a test pattern. We also show theoretically the condition under which finding several pseudo global optimal solutions requires a shorter training time than finding a single global optimal solution. As the difficulty of curve fitting problems is easily estimated, we verify the capability of the RPHP algorithm against them and compare the RPHP algorithm with three counterparts to show the benefits of hybrid learning and active recursive subset selection. The RPHP shows a clear superiority in performance. We conclude our paper by identifying possible loopholes in the RPHP algorithm and proposing possible solutions