Herman Kandjimi

Son | Brother | Uncle | Father | .. Husband

PetrolHead | Computer Scientist | Educator | Entrepreneur | Problem-Solver

Son | Brother | Uncle | Father | Aspiring Husband

PetrolHead | Computer Scientist | Educator | Entrepreneur | Problem-Solver

A diligent scholar who is always seeking opportunities to further his knowledge and enhance his professional competitiveness. Currently enrolled at the University of Cape Town pursuing a PhD in Computer Science, exploring the application of machine learning and digital tools towards personalized learning environments for the future. Aims to enhance computer literacy and development of digital solutions tailored to African problems.

Herman advocates for active reading in homes, sports participation and promotes innovation and entrepreneurship in the community. In the last few years, his studies have been spread through three main fields- Education, Business and Computer Science. Starting off with a Postgraduate Certificate in Higher Education, to a Master in Computer Science with a focus on Software Engineering and then lately a Postgraduate Diploma in Management aim at improving managerial competencies in business acumen, performance management and organizational culture. See CV page for detailed information.

Born and raised at Namutuntu, a small village along the Kavango River in the northern part of Namibia - Herman took an interest in technology in his first year of High school. At Elcin Nkurenkuru High School, he was introduced to LOGO (A general-purpose programming language, widely known for its use of turtle graphics, in which commands for movement and drawing produced lines or vector graphics) and this launched his career in Computer Science. After high school Herman, pursued a BSc in Information Technology majoring in Computer Science and Applied Mathematics at the University of Cape Town which he graduated in 2009. Upon graduation, he briefly worked in industry but ended up in the Education sector under Technical Vocational Education and Training (TVET) which accelerated his academic career and landed him at the Namibia University of Science and Technology - then Polytechnic of Namibia in 2014.

During the weekends, Herman can be found doing casual 10 KM runs following any available trail or occasionally joining organized marathons in whichever city he finds himself in. Ideally, the remaining part of the weekend is for Car projects and Business errands @kings_auto_na.

news

Jul 09, 2024	SAICSIT 2024 Paper published as book chapter by Springer
May 10, 2024	Nelson Mandela University, Gqeberha - Presenting a paper at the SAICSIT 2024 conference
Apr 10, 2024	Potsdam, Germany - Attending HPI Students annual research symposium
Jul 15, 2023	Cape Town, South Africa - Attending Deep Learning IndabaX South Africa.

selected publications

SAICSIT
Investigating Markov Model Accuracy in Representing Student Programming Behaviours

Herman Kandjimi, and Hussein Suleman

In South African Computer Science and Information Systems Research Trends , Jul 2024

Abs Bib

Problem-solving skills are an integral component within the computer science field. Due to the diversity brought about by students following different learning and programming behaviours, it is challenging to track and identify when students get overwhelmed while writing programs. When students are overwhelmed, they are unable to complete learning objectives on time and follow prescribed pathways, depriving them of the opportunity to learn new concepts. In this paper, we developed and evaluated the quality of Markov models that encode student programming behaviours based on the evolution of source code submissions during formative practical assignments. In doing so, we use Abstract Syntax Trees (ASTs) extracted from the source code, which are used for clustering similar submissions and tracking students’ progressive approaches within the Markov models. An approach based on MinHashLSH is presented that works on AST nodes as input to emphasise structural similarity and related programming approaches. As such, the effectiveness of the Modified MinHashLSH approach is based on the clusters that make up the Markov model.
@inproceedings{10.1007/978-3-031-64881-6_4, author = {Kandjimi, Herman and Suleman, Hussein}, editor = {Gerber, Aurona}, title = {Investigating Markov Model Accuracy in Representing Student Programming Behaviours}, booktitle = {South African Computer Science and Information Systems Research Trends}, year = {2024}, publisher = {Springer Nature Switzerland}, pages = {62--78}, month = jul, doi = {10.1007/978-3-031-64881-6_4}, url = {https://doi.org/10.1007/978-3-031-64881-6_4}, isbn = {978-3-031-64881-6} }