Abstract:
Student attendance is an important aspect at universities. Attending classes also increases a student’s interaction with a variety of faculty members. This raises the likelihood of finding mentors and role models who can help guide their academic, career and personal development. The digital strategy of the Faculty of Engineering and Information Technology at the Central University of Technology, Free State, encourages the replacement of manual processes by technological processes, to capture class attendance. The manner in which class attendance is generally captured, relevant to the research setting, is a manual process using pen and paper. With the advent of new technologies it is possible to replace the aforementioned manual system with a custom hardware and software solution, by using electronic input devices (EID’s). The question remains, how can a usability study on electronic input devices be used to assess and determine the most suitable device for recording class attendance electronically? The site of this research was restricted to the Central University of Technology, Free State (CUT). A mixed methodology to explore the phenomenon was used. Sixty-three (63) first- year students, currently enrolled for the Diploma in Information Technology, Extended Curriculum Programme (ECP), were identified as a suitable population, with the correct demographics and sample size, to participate in the study. An open-ended questionnaire was developed to determine student perceptions of EID’s as well as the traditional method of recording student class attendance. Three different input devices were identified, namely: Barcode Scanner, Fingerprint Scanner, and Radio Frequency Identification Scanner (RFID). The devices were connected to custom software to gather the quantitative data over a period of four (4) weeks for each device. All the data captured was stored in a database. Data mining was implemented to extract data from the database. The objective of the study was to determine which electronic input device performs the best. A Performance Metric was developed that comprises the student’s opinions, duration for each individual scan as well as the duration of the total scan time for each device. The data in the Performance Metric was analysed using correlation and standard deviation. Each student was identified using their student number, which is linked to the barcode on the student card. For both the RFID and Fingerprint Scanners, student numbers must be linked to the Fingerprint and the RFID code. At the inception of the study, it was envisaged that the Barcode Scanner would be the most efficient and take the least time. It was also expected that students would be in favour of the Barcode Scanner, mainly because they are more familiar with it than with the other two devices.
