Caso 2: Daniela

De Goede (2007) translated the underlying cognitive model of Taylor (1989, 1992, and 1994, as cited by De Goede) and his resulting assessment measures into two learning competencies, namely transfer of learning and automatisation, influenced respectively by the competency potential variables abstract thinking capacity and information processing capacity. He thus devised a model of the competencies and competency potential latent variables contributing towards learning performance as a basic cognitive model, with basic causal paths as can be seen in Figure 3.1.

Figure 3.1. Hypothesised learning potential structural model. Adapted from An investigation

into the internal structure of the learning potential construct as measured by the APIL-B test battery, by De Goede, J., & Theron, C. C. (2010), Management Dynamics, 19(4), p. 38. Copyright 2010 De Goede & Theron; Management Dynamics.

De Goede used the APIL-B test battery to operationalise the model, and it was tested on 44 new recruits from the South African Police Service (SAPS) Training College in Philippi, Cape Town, to obtain empirical evidence that the relationships postulated in the learning potential structural model provide a plausible explanation for differences in learning performance during evaluation. Learning performance (or job competency potential) was determined by two measures used by the SAPS in the evaluation of constables in their basic training programme. Structural Equation Modelling (SEM) was used to test the hypotheses. Reasonable model fit was obtained, though with only limited support obtained for the proposed causal paths – only four of the ten hypotheses. The postulated relationship between information processing capacity and automatisation was corroborated. The direct path hypothesised between information processing capacity and learning performance was corroborated. Similarly, the direct path hypothesised between automatisation

and transfer of knowledge was corroborated. Support was also obtained for the indirect effect of information processing capacity on learning performance, mediated by automatisation. No support was found for the following hypothesised paths/linkages between:

• Abstract thinking capacity and transfer of knowledge. • Abstract thinking capacity and learning performance. • Transfer of knowledge and learning performance. • Automatisation and learning performance.

• The indirect effect of abstract thinking capacity on learning performance, as mediated by automatisation (De Goede, 2007).

According to De Goede (2007) the degree of measurement model fit achieved was reasonable and the specific indicator variables used reflected the respective latent variables in the model, although the validity of the learning performance and transfer of knowledge measures seemed to be questionable. He argued however that the transfer of knowledge measure cannot be changed easily as it forms an integral part of the APIL-B test battery. Professor Callie Theron suggested at the 2015 Empowerment for Development Seminar, held at Stellenbosch University, that perhaps the more fundamental TRAM-2 measure would have been a better choice to operationalise these variables. De Goede raised the concern that the learning performance measure did not completely reflect the ability to use newly obtained knowledge in a creative manner in problem- solving and that the extent to which real learning took place is not determined effectively by many training institutions. Instead, tests or examinations rather reveal the amount of information committed to memory and not whether those who qualify can indeed cope with the novel challenges posed by the role they are being trained for. This also highlights a pertinent issue surrounding the definition of learning performance, as discussed by Van der Westhuizen (2015) with reference to De Goede’s results. Indications of instructor effectiveness and student learning have traditionally relied on achievement outcome measures such as graded performance, despite the inherent imperfection of the assessment as a measure of trainer success, and, for that matter, student learning performance. De Goede defines learning performance (in terms of outcomes, and in the workplace development context) as the (achieved) level of malleable job competency potential latent variables – the person characteristics that directly or indirectly affect the level of competence achieved on job competencies. De Goede (2007) and Burger (2012) define learning performance as the extent to which an individual has acquired knowledge, or a specific skill or ability, corresponding to the specific learning situation. De Goede (2007) and Theron (2010) argue that the latent variable learning performance should be removed from the modified model as the learning competencies already constitute learning performance, more specifically classroom learning performance. The essence of this argument is that learning performance cannot be modelled separately from the learning competencies and outcomes that comprise learning. They

therefore proposed that a longitudinal explanatory structural model should be developed in which provision is made for the level of crystallised abilities at different points in time. A clear distinction can then be made between learning performance in the classroom and subsequent action learning in the workplace. This implies that transfer and automatisation latent variables should be operationalised using stimuli from the actual learning task. Van Heerden (2013) stresses that De Goede and Theron’s recommendation to delete learning performance from the model can easily be misunderstood. She notes that the intention was to clarify the point that the current learning performance latent variable should not be seen as conceptually distinct from learning performance in the classroom. Numerous learning competencies constitute learning performance. These learning competencies constitute learning performance in the classroom, as well as learning performance during evaluation, and action learning in the workplace. Learning performance in the classroom and learning performance during evaluation essentially constitute the same selection of learning competencies, however, the nature of the learning problem differs. The learning problem differs in term of the nature of the crystallised ability that is transferred and the nature of the insight being automated. In the classroom, specific crystallised ability, developed through prior learning is transferred onto novel learning problems comprising the curriculum (Van Heerden, as cited by Van der Westhuizen, 2015). Once meaningful structure has been established in the learning material, it must be automated. Actual transfer takes place in the classroom and the subsequent automatisation of the derived insight determines learning performance during evaluation. De Goede (2007) (see also De Goede and Theron (2010)) used the APIL-B subtests to measure transfer and automatisation as dimensions of learning performance in the classroom. The APIL-B uses essentially meaningless learning material to assess learning performance in a simulated learning opportunity. Van Heerden claims that the APIL-B subtests cannot be considered valid measures of the extent to which transfer and automatisation takes place in a classroom. Classroom learning performance should rather be assessed by tracking the extent to which learners transfer previous learning onto the novel material presented in the classroom and the extent to which they automate the newly constructed meaning successfully. Learning performance during evaluation, in turn, should be measured by providing learners with novel learning problems that they should be able to solve by using the crystallised knowledge that they should have developed through transfer and automatisation in the classroom. As such, learning performance during evaluation involves transfer of newly gained knowledge that has been automated onto novel problems related to, but qualitatively distinct, from those encountered in the classroom (Van Heerden, as cited by Van der Westhuizen). This is an important issue to be considered by researchers going forward and is for example noted by Mahembe (2013).

Another critical conclusion from De Goede’s research, was the need to expand the Performance@Learning Structural Model. De Goede focussed exclusively on cognitive ability as

determinant of learning performance, however, it is extremely unlikely and somewhat unreasonable to assume that cognitive ability would be the sole determinant of learning performance. The model did not acknowledge that learning performance is not solely determined by cognitive learning potential competency potential latent variables (Burger, as cited by Van der Westhuizen, 2015). As De Goede explains, the learning domain very likely consists of more than two competencies, and suggested that future research incorporate latent variables such as existing knowledge levels, conscientiousness, tenacity, learning support, learning motivation, self- efficacy, performance motivation, and mentoring or perceived support. This suggestion is in line with the theories of Vygotsky and Feuerstein, discussed earlier, which clearly stated that other elements than purely cognitive ones should also be explicated as to their impact on learning performance. Moreover, non-cognitive determinants of classroom performance would likely not affect the two learning competencies identified by Taylor and De Goede directly.

Theron (2010a) suggests that since learning potential only refers to the ability to benefit from a learning/development opportunity, and since learning is not solely a function of ability, additional aspects probably need to be considered, as they may relate to learning performance, and assessed to identify those that would maximize the return on the development investment. Van der Merwe and De Beer (2006) also theorise that non-cognitive factors such as locus of control, self-perceptions, expectations of personal abilities, as well as study habits, also influence academic performance, and as such suggested that these types of variables be accounted for in future studies.

According to Theron (2010a), these likely include:

• Personality. • Motivation. • Interest. • Learning strategies/skills. • Learning self-efficacy. • Language proficiency.

• Critical thinking competencies. • Cognitive complexity.

• Locus of control. • Learning self-efficacy.

Learning competencies may also be expanded to include:

• Time at task. • Self-motivation.

• Time management. • Managing resources.

Time management and Managing resources may be combined into “Academic self-leadership.” Being cognisant of the thinking described above, several scholars from Stellenbosch University have since revised the De Goede model.