Academic journal article Journal of Information Systems Education

Implementation of an Automated Grading System with an Adaptive Learning Component to Affect Student Feedback and Response Time

Academic journal article Journal of Information Systems Education

Implementation of an Automated Grading System with an Adaptive Learning Component to Affect Student Feedback and Response Time

Article excerpt

1. INTRODUCTION

The National Research Council (NRC) and National Science Foundation (NSF) have defined basic requirements that today's students need to "Be Fluent in Information Technology" (BeFIT) (National Research Council, 1999). These concepts revolve around increased IT skills, concepts, and capabilities of all citizens. Many universities, colleges and two-year institutions require computer literacy for STEM (Science, Technology, Engineering and Mathematics) majors as well as for business majors. Computer literacy centers primarily on the use of personal productivity software applications, such as word processors, spreadsheets, databases and presentation applications.

Many educational institutions offer computer literacy courses to students and assist the learning process by assigning a certain number of computer projects. According to various learning theories, providing meaningful and timely feedback on assignments has been identified as a key component of successful learning among students. However, it is very time consuming and sometimes impractical to provide extensive and qualified feedback on numerous computer projects. This research reports the development and implementation of an adaptive learning and grading system with the goal to expedite and improve the feedback provided to students for their personal productivity software (i.e. spreadsheet and database) assignments. This research builds upon previous knowledge from the cognitive, behavioral, and resource-based views of learning as well as the establishment of the appropriate grading rubrics.

Computer-assisted assessments (Conole and Warburton, 2005) or automated grading systems are becoming more popular in higher education institutions because they can significantly enhance the learning process. In our study, an automated grading system, also known as the Adaptive Grading/Learning System (AGLS), was developed to allow instructors to quickly grade multiple and complex computer literacy assignments while providing meaningful feedback to students in order to stimulate an efficient learning process. The system provides for a consistent grading rubric for each assignment. A unique feature of the system is the ability of the system to "learn" the correct and incorrect responses and add them to the rubric. It is unique and different from what is currently provided by book publishers as it enables instructors to build more complex assignments and also share this enhanced grading rubric with other instructors.

This research investigated how 'auto grading' with an adaptive learning component might be used to affect the quality, quantity and the speed of feedback. Hypotheses were developed and evaluated using data collected by the existing gradebook reporting systems.

2. LITERATURE REVIEW

A student's overall success is largely influenced by the ability of the educator to present new information in creative and meaningful ways while at the same time evaluating a student's understanding of this information. This process requires students to learn the material covered by the educator. A brief overview of three learning theories is discussed in this section with particular attention to feedback theories and concepts.

2.1 Cognitive Learning Theory

Robert Gagne (1965, 1985, 1988; Gagne, Briggs and Wager, 1992) proposed a list of nine elements that should be present in any lesson in order for learning to occur. These nine elements form the framework for cognitive learning theory, where each element leads to the next, higher level element. They are: Gaining attention ("reception"), Informing learners of the objective ("expectancy"), Stimulating recall of prior learning ("retrieval"), Presenting the stimulus ("selective perception"), Providing learning guidance ("semantic encoding"), Eliciting performance ("responding"), Providing feedback ("reinforcement"), Assessing performance ("retrieval"), and Enhancing retention and transfer (generalization"). …

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.