Self-efficacy theory was applied to the domain of computer programming to develop a thirty-two-item self-efficacy scale for computer programming, primed to the C++ programming language. To assess its construct validity, the scale was administered to 421 students enrolled in an introductory course in C++ programming at the beginning and the end of the course. The reliability of the scores was high. An exploratory factor analysis, with oblimin rotation, yielded a four-factor solution. There was a growth in self-efficacy between two administrations of the scale twelve weeks apart, particularly for students who initially had low self-efficacy. The computer programming self-efficacy of males and females did not differ substantially in practical terms.Self-efficacy is a key construct of social cognitive theory as it has developed since the 1970s. Self-efficacy is defined by Bandura as ". . . people's judgments of their capabilities to organize and execute courses of action required to attain designated types of performances" [ 1, p. 3911. Bandura argues that efficacy beliefs influence: 1) choice of activities, 2 ) level of effort expended, 3) persistence in the face of difficulties, and 4) performance [ 1,2]. An individual's efficacy beliefs are critical to self-motivation. A person with high self-efficacy is more likely to initially choose to undertake a challenging task and to apply greater effort to achieve it. A person with high self-efficacy is also more likely to initiate coping strategies in the face of difficulties and to persist when presented with competing demands. While intellectual ability and domain knowledge are major factors in achievement in an educational setting, self-efficacy also plays a strong role. Those with the same 367 0 1998, Baywocd Publishing Co., Inc.

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Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

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