To address the difficulties associated with computer programming, this article first looks at some reasons why students, especially engineering students, find programming such a daunting prospect, and it proposes a programming learning tool managed by a Deterministic Finite Automaton (DFA). The DFA machine used a graphical environment provided by Simulink to teach the FORmula TRANslator (FORTRAN) programming language to science students.The proposed programming learning tool and the traditional method of teaching were compared and evaluated. The results of evaluation indicated that there was an improvement in learning effectiveness of the proposed learning tool.
Aim/Purpose: The study examined types of errors made by novice programmers in different Java concepts with students of different ability levels in programming as well as the perceived causes of such errors.
Background: To improve code writing and debugging skills, efforts have been made to taxonomize programming errors and their causes. However, most of the studies employed omnibus approaches, i.e. without consideration of different programing concepts and ability levels of the trainee programmers. Such concepts and ability specific errors identification and classifications are needed to advance appropriate intervention strategy.
Methodology: A sequential exploratory mixed method design was adopted. The sample was an intact class of 124 Computer Science and Engineering undergraduate students grouped into three achievement levels based on first semester performance in a Java programming course. The submitted codes in the course of second semester exercises were analyzed for possible errors, categorized and grouped across achievement level. The resulting data were analyzed using descriptive statistics as well as Pearson product correlation coefficient. Qualitative analyses through interviews and focused group discussion (FGD) were also employed to identify reasons for the committed errors.
Contribution:The study provides a useful concept-based and achievement level specific error log for the teaching of Java programming for beginners.
Findings: The results identified 598 errors with Missing symbols (33%) and Invalid symbols (12%) constituting the highest and least committed errors respec-tively. Method and Classes concept houses the highest number of errors (36%) followed by Other Object Concepts (34%), Decision Making (29%), and Looping (10%). Similar error types were found across ability levels. A significant relationship was found between missing symbols and each of Invalid symbols and Inappropriate Naming. Errors made in Methods and Classes were also found to significantly predict that of Other Object concepts.
Recommendations for Practitioners: To promote better classroom practice in the teaching of Java programming, findings for the study suggests instructions to students should be based on achievement level. In addition to this, learning Java programming should be done with an unintelligent editor.
Recommendations for Researchers: Research could examine logic or semantic errors among novice programmers as the errors analyzed in this study focus mainly on syntactic ones.
Impact on Society: The digital age is code-driven, thus error analysis in programming instruction will enhance programming ability, which will ultimately transform novice programmers into experts, particularly in developing countries where most of the software in use is imported.
Future Research: Researchers could look beyond novice or beginner programmers as codes written by intermediate or even advanced programmers are still not often completely error free.
Cloud computing is a new model for delivering new applications and services. Its adoption is gaining ground because most of the services provided by the cloud are of low cost and readily available for use. Despite many promises by the cloud service providers, users remain much concerned about the general risk associated with the adoption of the cloud. The availability of many cloud service providers on one hand promotes competition in the cloud market and gives end users more freedom to choose the best cloud provider however it became a tedious and time consuming task for potential cloud users to evaluate and compare the available cloud offerings in the market. Hence, discovering a reliable service is a daunting task. This research proposed a trustworthy model for reliable cloud service discovery.
Rapid industrialization has contributed immensely to the discharge of heavy metals into receiving water bodies untreated. The quantity of heavy metals prediction in industrial wastewater is very essential before treatment so that the quantity is precisely removed. This article formulates, simulate and evaluate a predictive model that mimics electrochemical treatment of lead and cadmium ions present in paint industrial wastewater using artificial neural network. The predictive model was formulated using Fuzzy Logic toolbox in MATLAB and the simulation was done in the environment. The prediction of the model was evaluated by comparing the predicted quantity of lead ions and cadmium ions with the result of the experimental work in the laboratory. The article concludes that the developed prediction model demonstrated very high prediction accuracy in predicting the percentage of lead and cadmium ions present in paints wastewater.
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