Browsing by Author "Moebs, Sabine"

Now showing 1 - 3 of 3

Research Based Solution for Linking Higher Learning Institutions Students to Relevant Companies for Field Attachment
(Modern Education and Computer Science Press, 2019-10-08) Samwi, Erick R.; Michael, Kisangiri; Moebs, Sabine
Work-based learning is what equips students with practical skills. All higher learning institutions (HLIs) have a specified period of time for students to carry out field based practices in companies which are relevant to their fields of study. As the number of students in Tanzanian HLIs become larger, coordination and allocation of students to relevant companies is becoming tougher. This study therefore intended to examine a better method to facilitate coordination and allocation of students to relevant companies through development of an online computer system. The research study to determine systems’ requirements was conducted in Arusha and Kilimanjaro regions by involving 62 HLI students, 3 HLIs and 5 companies. Data were collected using key informant interviews, observation and workshop. Both informative and descriptive information regarding current practices and desired features were collected and analyzed. It was found that, a platform for registering students’ profiles and companies’ information has advantages to all three main stakeholders who are HLIs, students and companies. Prior to actual implementation, collaborative prototype was designed using pencil software and shared to 5 users from each group of stakeholders to evaluate the tasks. Responses from users were used to refine the requirements and design the final prototype. The final prototype design was used to develop a Field Attachment Management System (FAMS). FAMS indicated to have improved access of students to relevant companies, reports generation, students’ assessment and follow-up conducted by HLIs to their students.
The Role of a Web Portal to Facilitate Higher Learning Institutions Students’ Field Attachment in Tanzania
(IADITI–International Association for Digital Transformation and Technological Innovation, 2020-11-04) Samw, Erick; Michael, Kisangiri; Moebs, Sabine
One of the approaches applied by Higher Learning Institutions to equip students with the practical skills is through field attachment in relevant companies based on field of studies. There are so many challenges in the process of coordination and allocation of students to relevant companies including expenses in terms of time and resources due to prolonged process involved. A web-based portal was developed to address the challenges for Tanzanian context. The development approach based on Scrum framework was employed to allow users involvement. To ensure information completeness, mixed-methods approach including key informant interviews, observations and requirements workshop were applied for portal’s requirements elicitation. The requirements determined from users were further used to guide initial interface designs which were then converted to clickable wireframe pages using pencil software. The prototype was sent to real users via email for testing and improvement suggestions before real portal development. Scrum development approach was employed where increments development progress were frequently inspected to detect undesirable variances. The portal was finally validated and tested for usability and indicated to have improved field attachment process and open doors for more collaboration between Higher Learning Institutions and companies. This study provides insight on the approach used to come up with the solution regarding current challenges. Moreover, the contribution that the research based solution has brought to the students’ field practices process is identified.
A Survey of Machine Learning Modelling for Agricultural Soil Properties Analysis and Fertility Status Predictions
(Preprints (www.preprints.org), 2023-08-21) Malamsha, Augustine; Dida, Mussa; Moebs, Sabine
The problem of low soil fertility and limited research in agricultural data driven tools, may lead to low crop productivity which makes it imperative to research in applications of high throughput computational algorithms such as of machine learning (ML) for effective soil analysis and fertility status prediction in order to assist in optimal soil fertility management decision-making activities. However, difficulties in the choice of the key soil properties parameters for use in reliable soil nutrients analysis and fertility prediction. Also, individual ML algorithms setbacks and modelling expert implementation procedures subjectivity, may lead to exploitation of worst fertility level targets and soil fertility status targets classification models performance reported variations. This paper surveys state-of-affair in ML for agricultural soil nutrients analysis and fertility status prediction. Prominent soil properties and widely used classical modelling algorithms and procedures are identified. Empirically exploited fertility status target classes are scrutinized, and reported soil fertility prediction model performances are depicted. The three pass method, with mixed method of qualitative content analysis and qualitative simple descriptive statistics were used in this survey. Observably, the frequently used soil nutrients and chemical properties were organic carbon, phosphorus, potassium, and potential Hydrogen, followed by iron, manganese, copper and zinc. Predominant algorithms included Random Forest, and Naïve Bayes, followed by Support Vector Machine. Model performances varied, with highest accuracy 98.93% and 98.15% achieved by ensemble methods, and the least being 60%. Interdisciplinary ML related researchers may consider using ensemble methods to develop high performance soil fertility status prediction models.