Image Retrieval Systems (IRSs) are applications that allow one to retrieve images saved at any location on a network. Most IRSs make use of reverse lookup to find images stored on the network based on image properties such as size, filename, title, color, texture, shape, and description. This paper provides a technique for obtaining full image document given that the user has some portions of the document under search. To demonstrate the reliability of the proposed technique, we designed a system to implement the algorithm. A combination of Optical Character Recognition (OCR) engine and an improved textmatching algorithm was used in the system implementation. The Tesseract OCR engine and Levenshtein Algorithm was integrated to perform the image search. The extracted text is compared to the text stored in the database. For example, a query result is returned when a significant ratio of 0.15 and above is obtained. The results showed a 100% successful retrieval of the appropriate file base on the match even when partial query images were submitted.

Many factors can be attributed to the high rate of failure in computer programming courses. This paper studies the background of students, their fields of study and learning approaches applied to the study of programming courses. It is worth considering as a major factor and necessary to research into the causes of failure of students in programming courses from the learner perspective. Programming courses form part of the core concentration areas for students especially studying Information Technology (IT) and Computer Science as well as those other fields of study sandwiched with IT in an undergraduate degree programs.

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