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02 October 2019 | Story Anneri Meintjes | Photo Charl Devenish
Anneri Meintjes
Anneri Meintjes from the Centre for Teaching and Learning at the UFS.

The #FeesMustFall student-led movement started in 2015 to protest against increasing student fees and to call for increased government funding of universities. At the end of 2016, the protests led to mass disruption of academic activities in higher-education institutions countrywide. Some universities, including the University of the Free State (UFS), suspended academic activities for extended periods which necessitated online and blended learning approaches (the combination of face-to-face and online learning) to complete the academic year. In most cases, these methods were unplanned and unstructured, and knowledge gaps in good blended learning practice were identified.

The Carnegie Corporation of New York funded a two-year research project in collaboration with the University of Pretoria, UFS, University of Cape Town and University of Johannesburg to investigate the use of blended learning at the end of 2016, during the campus disruptions, as well as how these respective institutions used blended learning in 2017.

The prohibitive cost of data in South Africa means few of our students have access to the internet off-campus. The most recent data on UFS student digital identity shows that only 21% have consistent, reliable access to the internet at home. This is a challenge not only for the UFS but for all universities in the country.

“For technology to be used in a way that contributes to learning and teaching, we needed to investigate what works well and what does not, considering our contextual challenges” says Anneri Meintjes from the Centre for Teaching and Learning, who was the principal researcher for the UFS on this project. In the first phase of the research, she wrote a case study on the UFS’ approach to blended learning during and after the protests in 2016. The findings of this phase of the research were presented at a national convening of higher-education institutions across South Africa.

In the second phase of the research, the four participating universities produced open educational resources on good, blended learning practice to share with universities countrywide. The UFS was responsible for the development of online assessment resources and general best-practice guidelines for the use of blended learning. Anneri says: “While we had laid solid foundations for the effective use of online assessment at the UFS prior to 2016 through the investment in online assessment software and staff development in online assessment design, we learnt many valuable lessons during that time. It provided momentum for the establishment of formal online assessment procedures and refinement of best-practice guidelines. This research project gave us an opportunity to share our work on a national platform.” The number of lecturers that use online assessment in their modules has grown considerably at the UFS since 2016. In 2016, 211 online assessments were completed on Questionmark (UFS online assessment programme) and in 2018, this number had grown to 743. Institutional Blackboard use data shows that at least one online assessment tool is used in 47% of all modules on Blackboard.

Resources developed by the other participating institutions include a self-evaluation app that academics can use to reflect on their existing blended learning practices, and an online utility that assists lectures and course designers to plan blended learning modules.

Anneri also coordinated the development of the national website, which was launched at the Flexible Futures conference hosted by the University of Pretoria on 9-10 September 2019. The website and resources were praised at the conference for being a timely response to a critical need in the higher education community in South Africa.

News Archive

Mathematical methods used to detect and classify breast cancer masses
2016-08-10

Description: Breast lesions Tags: Breast lesions

Examples of Acho’s breast mass
segmentation identification

Breast cancer is the leading cause of female mortality in developing countries. According to the World Health Organization (WHO), the low survival rates in developing countries are mainly due to the lack of early detection and adequate diagnosis programs.

Seeing the picture more clearly

Susan Acho from the University of the Free State’s Department of Medical Physics, breast cancer research focuses on using mathematical methods to delineate and classify breast masses. Advancements in medical research have led to remarkable progress in breast cancer detection, however, according to Acho, the methods of diagnosis currently available commercially, lack a detailed finesse in accurately identifying the boundaries of breast mass lesions.

Inspiration drawn from pioneer

Drawing inspiration from the Mammography Computer Aided Diagnosis Development and Implementation (CAADI) project, which was the brainchild Prof William Rae, Head of the department of Medical Physics, Acho’s MMedSc thesis titled ‘Segmentation and Quantitative Characterisation of Breast Masses Imaged using Digital Mammography’ investigates classical segmentation algorithms, texture features and classification of breast masses in mammography. It is a rare research topic in South Africa.

 Characterisation of breast masses, involves delineating and analysing the breast mass region on a mammogram in order to determine its shape, margin and texture composition. Computer-aided diagnosis (CAD) program detects the outline of the mass lesion, and uses this information together with its texture features to determine the clinical traits of the mass. CAD programs mark suspicious areas for second look or areas on a mammogram that the radiologist might have overlooked. It can act as an independent double reader of a mammogram in institutions where there is a shortage of trained mammogram readers. 

Light at the end of the tunnel

Breast cancer is one of the most common malignancies among females in South Africa. “The challenge is being able to apply these mathematical methods in the medical field to help find solutions to specific medical problems, and that’s what I hope my research will do,” she says.

By using mathematics, physics and digital imaging to understand breast masses on mammograms, her research bridges the gap between these fields to provide algorithms which are applicable in medical image interpretation.

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