SayPro SAQA Training Material 108863

Product Summary


        Associated Assessment Criteria for Exit Level Outcome 1:

Use terminology, concepts, principles and theories in written and/or oral communication are correctly.
Critically appraise relationship among concepts and principles of Data Science discipline are critically.
Apply knowledge effectively to propose solutions to problems in the field of interest are effectively within the Data Science discipline.

Associated Assessment Criteria Exit Level Outcome 2:
Reflect in explicit recognition of the diversity, complexity and multi-dimensionality of a context and how that affects the particular work being undertaken.
Demonstrate through the provision of relevant information pertaining to the strengths, weaknesses and opportunities of the context for addressing specific problems.
Identify relevant role players and resources that will contribute to resolution of specific problems.
Describe all relevant factors pertaining to the context and performance(s) in these contexts and how they affect the particular work being undertaken.
Identify critical factors impacting on practical problems to be investigated from the perspective of the discipline.

Associated Assessment Criteria for Exit Level Outcome 3:
Know the ethical implications of various kinds of research and be able to act accordingly.

Associated Assessment Criteria Exit Level Outcome 4:
Apply reasoning skills to by expressing own opinions clearly and coherently, justifying a position and presenting it logically, systematically using properly substantiated arguments.
Show an awareness of audience, and capability in using different modes of communication (oral and written) and discipline-specific conventions, and utilisation of different techniques and strategies for communicating results.

Associated Assessment Criteria Exit Level Outcome 5:
Demonstrate logical thinking (including identification of flawed reasoning in a text).
Demonstrate inductive and deductive thinking skills.
Demonstrate thinking and reasoning (self-reflexivity is demonstrated at the appropriate level).

Associated Assessment Criteria Exit Level Outcome 6:
Identify a research problem.
Consult appropriate literature.
Select methodologies that are appropriate to addressing the research problem.
Undertake data collection, presentation, analysis and interpretation of data.
Produce a written report of the research project, including inter alia the findings, conclusions and recommendations.

Associated Assessment Criteria Exit Level Outcome 7:
Identify the central theme (s) of the essay.
Extract and explaining the competing arguments.
Give a knowledgeable perspective to the debate.
Derive a conclusion that fills the gap.

Associated Assessment Criteria Exit Level Outcome 8:
Structure the essay by showing understanding of the question and indicating the direction of the answer through laying out the subsections of the essay and their linkages with the overall theme.
Operationalise the topic by stating clearly the context in which its basic concepts and assumptions are to be evaluated in the essay.
Debate the subject matter by developing logically coherent arguments as well as citing and discussing illustrative examples.
Draw a conclusion that is consistent with the arguments raised in the essay and suggesting possible outcomes of alternative initiatives at addressing the subject matter.
Present the essay according to appropriate and/or recommended word processing and referencing protocol.

Integrated Assessment:
The purpose of this assessment is to establish student learner achievement of the required knowledge at this level and also the ability to use and apply practical skills learnt from the programme. This will mainly be done through formulation and carrying out a research project, reporting on it and critically reviewing current literature in the Data Science spectrum.

Quality assessment is central to credible certification and recognition of student achievement. SPU (Sol Plaatje University) the Institution will ensure credibility in assessment through the application of clear and rigorous procedures and practices, in keeping with the principles of fairness, validity, reliability and practicability.

Integrated Assessment is used extensively across the qualification. Self-and peer assessment takes place in various ways in the face to face context, including through classroom activities, assignments, and written work (formative assessments).

Summative Assessments are integrated into the learning in each of the constituent modules of the programme.

Assessment methods will normally include formal examinations assessment (i.e. written examinations), written reports, the creation and presentation of data science artefacts (such as data sets, models, and computer program source code) and oral presentations. 


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