education

Structure of the Observed Learning Outcomes (SOLO): A Taxonomical Bridge

Structure of the Observed Learning Outcomes: A Taxonomical Bridge

Teaching practice is better informed with the knowledge of surface and deep processing, its role in learning, and the transfer-appropriate potential for student achievement in schools. However, these subsurface processes represent styles of thinking and learning – not necessarily physical behavior. In order for teachers to more fully utilize this information and synthesize strategies that support developing these processes, a correlated taxonomy will be useful. The Structure of the Observed Learning Outcome (SOLO) taxonomy is a widely recognized and accepted tool for showing changes in complexity of understanding. McMahon and Garrett (2016) report that,

SOLO is a useful contemporary tool that incorporates ... aspects of former taxonomies (Bloom, Engelhart, Furst, Hill, & Krathwohl, 1956; Merrill, 1971; Gagne, 1977/1984) in that it studies the cognitive complexities of a learner’s response to a given learning stimulus... [SOLO] emphasizing the observation of student learning cycles to describe the structural complexity of a particular response to a learning situation through five different levels: prestructural, unistructural, multistructural, relational, and extended abstract. (p. 422)

This approach more thoroughly examines changes in thinking by addressing changes in observed behavior. Just as Aha! moments represent sudden and unexpected cognitive illumination when a solution is found, along with their observable correlates, SOLO taxonomy represents a classification tool for the physiological behavior in learners as it changes over the complete cognitive continuum. This rubric for progression is a practical framework for teachers to evaluate achievement, “in a language that is generally applicable across the curriculum” (Biggs & Collis, 1989, p. 151). SOLO taxonomy is a form of measuring students’ understanding of subjects, from the introduction of a concept to a student’s expertise with it.

According to Biggs and Collis (1982/2014), who first introduced this taxonomy, SOLO is, “based on the observation that, over a large variety of tasks and particularly school based tasks, learners display a consistent sequence, or ‘learning cycle,’ in the way they go about learning them” (p. 152). In essence, as a learner moves from a superficial understanding of the components of a concept towards a deeper processing of the concept’s features, the taxonomy accurately shows these progressions in a manner that makes learning more easily observed by teachers. The final mode in the SOLO taxonomy suggests learners’ ability to extend comprehension into a final transfer-application understanding. The SOLO spectrum from prestructural to extended abstract is also analogous to the cognitive change represented when introducing a stimulus to a learner through to the development of an Aha! moment. Biggs and Collis (1989) discuss congruency among similar theories that support neo-Piagetian models (Case, Hayward, Lewis, & Hurst, 1988; Fischer, 1980; Fischer & Pipp, 1984; Halford, 1982), distinguishing, “between learning and development in a way similar to that suggested here [SOLO] with their terms ‘optimal level’ (the last mode reached) and ‘skill acquisition’” (p. 157).

Hunt, Walton, Martin, Haigh, and Irving (2015) studied the implications of school-wide adoption and application of the SOLO taxonomy to inform teaching and learning in a secondary environment. Hattie and Purdie (1994) were among the first to show that SOLO taxonomy is useful and effective for training teachers on how to structure questions, design activities, and to matriculate through modes of learning along the SOLO hierarchy in multiple curricular areas. Hattie and Purdie also showed that teachers indicated using SOLO taxonomy for accomplishing learning objectives, surface and deep processing, and found it much easier and more effective to use. Hattie, Clinton, Thompson, and Schmidt-Davis (1997) indicate in their research that,

expert teachers are more likely to lead students to deep rather than surface learning. These teachers will structure lessons to allow the opportunity for deep processing, set tasks that encourage the development of deep processing, and provide feedback and challenge for students to attain deep processing. (p. 54)

SOLO seems to promote stronger deep processing effects for teachers and with students, likely due to a reliable and understandable hierarchy for witnessing change in a learner’s thinking and cognition. In all of these studies, it is clear that surface and deep processing strategies are embedded into practices that are reflected through SOLO, and opportunities to inform and improve teaching practice are present.

What's the Difference Between Teacher Quality and Quality Teaching?

Contemporary researchers have published quantitative and qualitative research which examine learning in classrooms, particularly emphasizing learning outcomes and the effects of teacher quality and quality teaching in classrooms (Biggs, 2012; Gardner, 2011; Hattie 2016; Marzano, Frontier & Livingston, 2011; Nuthall, 2007). These two categories have specific influences and observable outcomes. Quality teaching and teacher quality both have tremendous impact on positive outcomes for students, particularly with regard to creating opportunities for moving learning objectives between surface processing and deep processing – at times into transfer-appropriate strategies for learning.

Recent arguments have been made that help to differentiate between quality of teachers and quality of teaching (or teaching efficacy) (Hanushek, 2011; Harris & Sass, 2011; Taylor, Roehrig, Hensler, Connor, & Schatschneider, 2010). Darling-Hammond and Jaquith (2012) posit that teacher quality and quality of teaching should be considered independently, but as equally important. Darling-Hammond and Jaquith argue that the talents, personal mannerisms, and paradigms each teacher draws from in order to inform their teaching should not be evaluated independently of factors that enable, “a wide range of students to learn” (p. i), asserting that teaching efficacy,

is also strongly influenced by the context of instruction: the curriculum and assessment system; the “fit” between teachers’ qualifications and what they are asked to teach; and teaching conditions, such as time, class size, facilities, and materials. If teaching is to be effective, policymakers must address the teaching and learning environment as well as the capacity of individual teachers. (p. i)

It is crucial to understand these distinctions while exploring the potential for introducing insight learning opportunities into learning environments. Teachers may be effective at implementing pedagogy, but lack the requisite training to maximize Aha! moments in learning. Similarly, an expert pedagogue may be inducing preconditions for Aha! moments but may lack the effectiveness to maximize their effect in learning, especially for moving from superficial information acquisition to deeper thinking strategies and transfer-appropriate opportunities.

Goe (2007) outlines a comprehensive framework for better understanding teacher quality in terms of its effect upon student success, following on from the concern with measurable and broad impacts upon the widest range of students. The graphic representation in Figure 2 presents teacher quality as a combination of inputs and processes, and student outcomes as measurable effects of teacher quality. These inputs and processes include teacher certification, beliefs, instructional delivery, interactions with students, teacher test scores and experience, and classroom management. Student achievement is both an input and output, often part of teacher evaluations and other forms of feedback influencing practice. Inputs, processes, and feedback from outcomes (generally in the forms of grades from student assessment) all inform the basis for teacher quality.

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Figure 1. Graphic representation of a framework for teacher quality (Goe, 2007, p. 9).