BACKGROUND AND RATIONALE FOR STUDY
4.10 Literacies across Science Disciplines
The use of academic language and the discourse of science outlined in the first part of this Chapter are operational in the various disciplines of the faculty of science. It was imperative that a discussion on the nature of the language that constitutes science as well as the vocabulary and the grammatical and sentence structures used in the disciplines of science be presented as they are the literacies referred to in this study which the FP science students need to acquire to be able to engage with “talking, doing and writing science”
(Lemke, 1990: 1) in order to learn science and become productive citizens of the country.
Having explained how the research questions address the core issue of discipline-specific literacies in science and the way in which these issues are explored in this study, the next section of this Chapter outlines how discipline-specific literacies manifest across the disciplines of biology, chemistry, mathematics and physics. These are of particular importance for this study.
4.10.1 Literacies for Epistemology across Science Disciplines
Science disciplines are cognitively demanding. “Deep scientific understanding includes a coherent system of facts, concepts, scientific inquiry and strong problem solving ability”
(Staver, 2007: 11). In science, students acquire either conceptual or procedural knowledge.
While conceptual knowledge requires a deeper understanding of underlying concepts, procedural knowledge implies completing operations by following steps. The sub- disciplines of biology, chemistry, mathematics and physics offered in the discipline of science share similar academic discourse, genres and literacy practices. They share, too, similar register and grammatical features in their presentation of content knowledge.
The disciplines of chemistry, mathematics, physics loosely viewed as the least language- dependent disciplines in the sciences, are inclined to rely on more extensive use of symbols, formulae, figures and equations to convey, interpret, analyze, solve and assimilate content knowledge. There is, however, the discursive space in their scientific content (e.g.
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in reading and solving problems) to reason, infer, debate, discuss, hypothesize, question, solve and resolve. This, then, involves some measure of competence in both linguistic and science literacies.
This assertion above is clearly relevant to mathematics. Kilpatrick et al. (2001) highlight the five strands of mathematical proficiency required for successful mathematical competence noted below. I have amended the original extract by consciously drawing attention to the link between language and mathematics by highlighting the pertinent literacies in bold print:
● Conceptual understanding: comprehension of mathematical concepts, operations, and relations
● Procedural fluency: skill in carrying out procedures flexibly, accurately, efficiently, and appropriately;
● Strategic competence: ability to formulate, represent and solve mathematical problems;
● Adaptive reasoning: capacity for logical thought, reflection, explanation, and justification and;
● Productive disposition: habitual inclination to see mathematics as sensible, useful, and worthwhile, coupled with a belief in diligence and one’s own efficacy (116)
The point above can be substantiated by that of Gibbs and Orton (1994) who explain that mathematics discourse generally contains items that have linguistic, cognitive and contextual dimensions. Bohlmann and Pretorius (2008) define each dimension. The linguistic dimension involves both the receptive level (i.e. reading) and the productive level (e.g. writing, discussing). The cognitive dimension reflects the level of complexity of the concepts and cognitive skills such as logical reasoning, critical analysis and interpretation of abstract concepts. The contextual dimension reflects the level of contextual support provided (Bohlmann and Pretorius, 2008).
Bohlmann and Pretorius (2002) draw attention to procedural discourse in some parts of mathematics texts, which “provide instructions and explanations on how to carry out a task or algorithm” (197), “procedural fluency being the skill in carrying out procedures flexibly, accurately, efficiently, and appropriately” (Engelbrecht et al. 2005). Conceptual understanding is a highly valued learning outcome in tertiary studies. Anderson and Schönborn (2008) describe conceptual understanding as multi-faceted and “requires competence in the cognitive skills of memorization, integration, transfer, analogical reasoning, and system thinking” (309). Engelbrecht et al. (2005) describe conceptual
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understanding as “involving the understanding of mathematical ideas and procedures and includes the knowledge of basic arithmetic facts” (701). In conceptual discourse, students
“are not only expected to know the procedure that needs to be followed to solve a problem, but also why, when and how that procedure works” (Setati, 2005: 102); “they should be able to identify and apply principles, know and apply facts and definitions, and compare and contrast related concepts” (Engelbrecht et al. 2005: 701). Potgieter et al. (2008: 5) distinguish between algorithmic questions which are questions that can be answered by applying a step-by-step procedure to generate a response and conceptual questions that probe the depth of understanding of the concepts related to a question. (Bowen and Bunce, 1997). Algorithmic questions typically require lower order cognitive skills (Zoller et al.
2002). Engelbrecht et al. (2005) offer a distinction between procedural knowledge and conceptual knowledge:
Procedural knowledge is the ability to physically solve a problem through the manipulation of mathematical skills, such as procedures, rules, formulae, algorithms and symbols used in mathematics. Conceptual knowledge is the ability to show understanding of mathematical concepts by being able to interpret and apply them correctly to a variety of situations as well as the ability to translate these concepts between verbal statements and their equivalent mathematical expressions. It is a connected network in which linking relationships is as prominent as the separate bits of information (701).
Science disciplines rely on the technique of problem solving to ascertain students’
conceptual and procedural knowledge. There is a fair degree of consensus among physics instructors that the activity of problem solving is a powerful tool in assisting with changing and expanding the conceptual framework of the learner as it sets up situations which force the learner to grapple with new and unfamiliar concepts which may then be internalized, sometimes after conflict with existing conceptual structures (Buffler and Allie, 1993: 15).
Problem solving as a learning tool is just as useful in the discipline of chemistry. Effective problem solving skills in chemistry require intellectual skills such as focussing, information gathering, remembering, analyzing, generating, integrating and evaluating. Drummond and Selvaratnam (2008) identified four types of intellectual strategies that are particularly important in solving chemistry problems:
1. clarification and clear presentation of the problem;
2. focussing on the goal and identifying a strategy for moving towards the goal;
3. identification of the principles needed for solution and;
4. proceeding step by step (56)
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In biology, students are expected to “acquire knowledge and understanding that is diverse at different levels of complexity and abstraction; flexibly transfer knowledge during problem-solving; and interpret and translate across multiple external representations ... the process of translation requires the comprehension and conversion of relationships between external representations such as diagrams, physical models and visuals” (Schönborn and Bögeholz, 2009: 931, 933).
Success at tertiary level science means being able to participate in the discourse of its disciplines, gaining an adequate knowledge base as well as learning and applying the specific genres and scientific activities required to communicate its epistemology. The components of academic discourse conveyed by Snow (2005) are applied in this context to illustrate what could contribute to academic success at higher education institutions:
1. linguistic understanding of lexical or word choice issues, syntactic or sentence structure issues, text structure, and language functions;
2. background knowledge of content and;
3. cognitive knowledge and critical thinking skills.
Achieving this academic success in science can be challenging for students when the LoLT is either a second, third or even an additional language. In this study, the LoLT at a South African university, the research site of this study, is English and the students in the FP speak EAL. Such students have to “immerse in two social practices together at the same time when learning science: one which has to do with learning a new language (i.e.
English) and the other which has to do with learning science (i.e. language of science)”
(Lemke, 1997). This becomes even more challenging when students who opt to undertake HE studies are underprepared for the rigours of academia as a consequence of the quality of their secondary schooling.
Thus, this study intends to find out, through critical research question 1, the specific discipline-specific literacies required in the foundation modules offered in the FP in the light factors such as reading, conceptual understanding, comprehension; procedural fluency, logical reasoning, and problem solving that have been discussed above. In doing so, critical research question 2 highlights the perceived challenges that can be presented by such literacies. Critical research question 3 then seeks to find out the mechanisms used by DSs to assist students in the FP with the acquisition of such discipline-specific literacies in science.
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The core of this study is the acquisition of discipline-specific literacies in science and an understanding of any perceived challenges that the FP students experience in respect o the acquisition of such literacies. The reference to student underpreparedness for science at HEIs due to their educational disadvantage has already been alluded to (in Chapter 1). It has thus been necessary to review existing literature with regard to this issue. This is presented in next part of this Chapter.