Distractibility and brain-based learning: The other side of the coin

Tony Yeigh; B. A., B. Psych (Hons), Grad.Dip(Ed) Centre for Children & Young People School of Education Southern Cross University Lismore, NSW 2480, Australia Tel: (0011) 61 2 66203659 E-mail: [email protected]

This paper represents an initial analysis of information relating to the role of inhibition in classroom learning, undertaken in fulfillment of a PhD.

Keywords: working memory, selective attention, cognitive inhibition, priming, cognitive load Distractibility & Brain-based Learning

Abstract Most research concerning the effectiveness of instructional strategies in relation to individual differences focuses on the learner’s ability to process task-relevant information during learning. In contrast, relatively little research has been reported concerning individual differences in relation to the ability to inhibit attentional distracters. This paper reports on an investigation identifying inhibitory processing differences for year-8 learners, and how these differences interact with the amount of mental effort and achievement outcomes the students experienced under instruction from four different teachers. The results support a view that cognitive inhibition, as an aspect of information-processing, does affect the learning process, and should therefore be viewed as a distinct element in the design of a comprehensive, cognitive-based pedagogy. Future research is directed at eliciting specific strategies for enhancing the role of inhibition.

Information processing and instructional design The concept of working memory (WM) is often used to explain differences in student learning within an information processing framework, where WM is conceptualised as the cognitive arbiter of an individual’s capacity to simultaneously store and process information. Because of this, individual differences in WM form an important aspect of instructional design, especially in relation to mixed ability classrooms, where the instruction must account for the limited processing capacity of WM, a capacity that ranges between 5 – 9 items of information, as efficiently as possible. However, of additional interest is that a number of executive processing mechanisms operate within the capacity range of WM, with each placing its own processing load upon the limited resources available (Byrnes, 2001; Byrnes & Fox, 1998). As an example, selective attentional processing, key to the teaching/learning process, is generally viewed as the result of two complimentary executive WM mechanisms: the regulation of attentional focus to task-relevant information (Baddeley, 2001; Milliken, Joordens, Merikle & Seiffert, 1998; Pashler, 1997; Stadler & Hogan, 1996) and the inhibition of distractions from task-irrelevant information (Cohen, Botvinick, & Carter, 2000; May, Kane & Hasher, 1995; Passolunghi, Cornoldi & De Liberto, 1999; Wentura, 1999). Of interest is that although some executive processing can occur in an automatised manner (Chi, Glasser & Farr, 1988; Sweller, van Merrienboer, & Paas, 1998), this depends to a large degree upon how the information to be processed is organised and scaffolded (Gagne, Yekovich, & Yekovich, 1993; Joyce, Weil, & Calhoun, 2000). Thus WM, as it operates to control the selective processes involved in perception and learning, must balance the demands made upon limited capacity by the executive cognitive mechanisms against the demands of short- term memory store, in order to process instructional information effectively. In terms of instructional design, this requires that efficient instruction account not only for the overall amount of information students are being asked to process, but also for how this processing might affect executive WM function. A consideration of how differences in executive function might affect the learning process appears; therefore, to be as important to the design of efficient classroom instruction as is the more general understanding of WM as a limited capacity system.

In this respect cognitive inhibition (CI), an aspect of executive function that controls the ability to inhibit distracting information during on-task attentional focus (Harnishfeger & Bjorklund, 1994; Karatekin, 2004), may deserve separate consideration as an aspect of WM function relevant to efficient instructional design. CI is a term used by cognitive and neuro-psychologists to refer to the suppression of distracting or non-relevant information during on-task cognitive engagement. Like other WM functions, it also varies in quality from individual to individual, displaying a wide range of differences in terms of the ability to process distracting or conflicting information. Although it is noted that some distinctions are made in professional literatures concerning the respective roles of inhibition (Dempster, 1991) and interference (D’Esposito et al., 1999), for the current research CI is used as a generalised term referring to the inhibitory function of WM, and conceptualised as a functional connection between items of information where, when two or more items are competing for attentional focus, one of the items needs to be suppressed (Dempster, 1993; Harnishfeger & Bjorklund, 1994; Mecklinger et al, 2003). The value of the current study rests in the fact that it seeks to identify whether or not CI manifests a distinct influence upon the teaching/learning process. The need for this research is highlighted by the fact that although a great deal of research has been invested in exploring the relationship between WM processes and

1 Distractibility & Brain-based Learning learning, most of this has been directed at issues of memory or memory function (Anderson, 2000; Ericsson, Chase, & Faloon, 1980; Reynolds, 1993; Rose, 1992), the limited capacity of WM and the attentional system (Baddeley, 1986; Spear & Riccio, 1994; Just & Carpenter, 1987), the relationship between selective attention and the automatic processing of information (Sweller, 1999; Stanovich, 2000), and how meaningful elaboration of information is constructed (Kintsch, 1998; Pressley & Wharton-McDonald, 1997). As well, a great deal of research investigating WM in relation to learning has focused on why the learning is easier or more difficult in relation to development (e.g., Swanson, 1999; Towse, Hitch, & Hutton, 1998), to task difficulty and strategy use (Chi, Glaser, & Rees, 1982; Guttentag, 1997; Miller, 1994), or to specific learning difficulties (Gathercole & Pickering, 2000; Pennington & Ozonoff, 1996).

In contrast, little has been done in the way of connecting the inhibitory function to specific classroom learning (although related studies have been carried out on interference and the nature of forgetting, see Greene, 1992; Waugh & Norman, 1965), and relatively few studies have examined the specific role of WM function in relation to irrelevant information processing (although see Barrouillet, Fayol, & Lathuliere, 1997; Mecklinger et al, 2003; Passolunghi, Cornoldi, & de Liberto, 1999; Swanson, Cooney, and Brock, 1993). None of these studies, however, have attempted to profile the underlying relationships that might exist between inhibitory differences (differences in students’ ability to inhibit distracting information), instructional encoding (how teachers organise instructional information), and cognitive load (how much mental effort the students experience in relation to instruction). In light of this the current study represents an attempt to explore these very relationships within the context of classroom learning. It is the goal of the research to distinguish how individual differences in the ability to suppress or inhibit information interact with received instruction, to influence the amount of perceived mental effort required to achieve set learning outcomes.

The importance of inhibition to the learning process may be seen with respect to problem-solving activities. Successful problem-solving across an achievement task requires efficient inhibition because the strategic searching for correct, task-relevant information, while simultaneously testing problem-solving strategies by which the relevant information may be applied, will require the inhibition of both low dominance (task-irrelevant) information and more familiar (more automatised) problem-solving strategies. Thus, differences in susceptibility to interference and/or suppression may be a key factor in explaining differences in problem-solving ability, and may require the implementation of teaching strategies that recognise and make some account of the inhibitory role in this overall process. The current examination of cognitive inhibition in relation to instructional design is therefore assumed to provide necessary and worthwhile information for educators to use in understanding and developing more holistic and effective classroom instruction.

In terms of the relationship between CI and instruction, a close look at WM research investigating the inhibition or suppression of irrelevant information suggests that the way in which information is organised has a significant effect on how individuals are “primed” for it’s processing (Brown, 1979; Kintsch & Greeno, 1985; Roediger, Neely, & Blaxton, 1983), with priming referring to how information is cognitively activated for ongoing processing. Importantly, priming seems to impart a processing bias, either for increased efficiency in the way information is accessed (positive priming), or for a decreased efficiency in the way information is accessed (negative priming ~ for an overview of inhibition research in relation to priming effects, see Dempster & Brainerd, 1995). Priming effects occur due to the way antecedent information is used to organise for on-task processing, and tend to be generalised, occur at various levels, and respond closely to patterns of stimulus-response (S-R) mapping (Neil, Valdes, & terry, 1995). At the classroom level, inefficient priming might result from many different design factors that organise for on-task learning, including lexical ambiguity, the use of homographs or ambiguous language, how the teacher uses intra-categorical information in relation to advance organisers or in relation to classroom questioning, and how lexical decision tasks are organised in terms of early learning or in terms of those areas of learning that are rich in jargon or technical words. A key underlying assumption of

2 Distractibility & Brain-based Learning the current research is that instructional approaches that do not organise information in a way that primes for efficient processing are creating greater demands upon the limited processing capacity of the WM system, due to an increased need for inhibitory activity.

The investigation The investigation reported here addressed relationships between WM capacity, CI, and instructional efficiency (viewed in terms of perceived cognitive load), with overall learning outcomes (as represented by final marks awarded, out of 100 possible) being used to measure how the impact of the instruction affected individual students. A key question was, “Do different instructional approaches appear to differentially support WM function in terms of scaffolding for effective inhibitory function?”. Table 1 gives a conceptual overview of how these different factors are understood to interact. Note that individual student processing capacity can interact with different levels of cognitive inhibition (CI), and can also vary between cognitive load ratings for the various teachers. In turn, individual student profiles for these processing-related measures can then also vary in relation to achievement outcomes.

Table 1: Conceptual overview of study factor relationships. Student WM Capacity CI Function Cog. Load Rating Achievement Marks High Teacher 1 Teacher 1

Teacher 2 Teacher 2 (measured in terms of Medium 1 individual processing differences) Teacher 3 Teacher 3

Teacher 4 Teacher 4 Low

Methods Participants Participants in the study included 114 Year-8 students (54% female, 46% male), with an average age of 13.83 years (SD of 0.40), and 4 teachers (1 female) with an average of 8.5 years teaching experience (SD = 5.20 yrs, range = 2 to 14 years).

Procedures After obtaining ethics approval for the research from the relevant authorities (University ethics committee approval ECN-03-157, Department of Education and Training approval SERAP 04.44), a prospective power analysis was conducted to determine optimum student participant numbers. Assuming three independent factors (WM function, instruction, and cognitive load) and a range of processing divisions along a continuum of CI, and nominating an effect size of 0.4 with alpha at 0.05 and a power level of 0.80, the power analysis determined a minimum sample size of 96 participants for the study. To maintain a similar developmental focus across the participant population, teacher, student, and parental permission for a mixed-ability cohort of Year-8 students to participate in the research was organised at a local secondary school.

Information processing ability Standardised measures of WM function (the OSPAN, a measure of overall WM capacity), and of executive attentional function (the ANT test, a measure of attentional focus), were used to measure a range of information-processing abilities across the sample of students.

WM capacity WM is generally conceptualised in terms of an individual’s ability to simultaneously store and process information. For this study WM capacity was measured via a computerised version of the operation-word span task (the OSPAN; Turner & Engle, 1989). The OSPAN is a complex span task that measures the overall processing capacity of WM by invoking a secondary cognitive task

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(solving mathematical problems) in conjunction with a primary task (memorisation and subsequent recall of words) to measure functional WM capacity. Although complex span measures are usually scored in a way that combines both storage and processing components into a single score, in this research the storage component (word recall) and the processing component (accuracy of the arithmetic operations) were analysed separately. This was done in order to allow more specific comparisons between these separate component functions and cognitive inhibition. As well, the combined approach makes no distinction between the different types of errors that can be made on the OSPAN, and thus does not allow for sensitivity to the fact that the difficulty of individual span items can vary on many dimensions (see Conway et al., 2005), whereas analysing the component functions allows for a more fine grained consideration of these errors. The OSPAN demonstrates acceptable levels of validity and reliability (using Cronbach’s alpha, reliability estimates for internal consistency average between .89 - .93; see Turner & Engle, 1989, p. 134), and thus was also used to ensure that participants were processing information at developmentally appropriate levels.

Cognitive inhibition WM function involves several distinct mechanisms, including the passive representation of information, and an active attentional component (Baddeley, 2001; Bjorkland & Harnishfeger, 1990). It is generally assumed that CI supports attentional processing by helping to modify the salience of interfering information (Harnishfeger, 1995). Interference-sensitive tasks include the Wisconsin Card Sorting Test, the Stroop task, Rapid Naming Tasks, and the Flanker task. For this research, student inhibitory function was tested using a computerised Flanker task, considered a good overall indicator of inhibitory function in that it measures both the identity and location of task-irrelevant information (Eriksen & Eriksen, 1974; Schmidt, & Dark, 1998). The Flanker as used here was embedded within the Attentional Networks Test (the ANT), a test designed to measure differences in attentional control (Fossella et al., 2002; Posner & Fan, 2005). The ANT is based on Posner’s theory of attention, which views attention as deriving from three distinct neurological anatomies. In line with this, the ANT measures three attentional functions: Alerting to information, orienting to the information, and what Posner and Fan (2005) call “executive attention”. This last function is concerned with how the learner is able to resolve attentional conflict relating to the identity and/or location of information, as operationalised by the Flanker task.

Importantly, the ANT is used to measure “…the role of attention in monitoring and resolving conflict between computations occurring in different brain areas” (Posner & Fan, 2005, p. 6). Cognitive conflict has been linked to the need to suppress attentional interference in the form of perceptual and sensory processing, decision tasks, multi-tasking activities, task-switching, and a variety of WM tasks (see Dempster, 1993; Harnishfeger & Bjorklund, 1994). The ANT utilises the Flanker task to measure conflict in the form of response time differences for congruent versus incongruent processing conditions, measuring the effect of interference on processing behaviours in terms of the accuracy of processing as well as response times for the processing. Conflict data from the ANT was therefore used here as an indicator of individual differences for CI among the participating student population. This is in line with a widely accepted theoretical explanation of inhibitory function that views the Flanker as an index of the inhibitory process, due to the way it transforms relevant, congruent representations into irrelevant, incongruent representations (May, Kane, & Hasher, 1995). This particular transformative attribute is known as priming, and as an effect is understood to be generalisable across a variety of attentional models, as well as across various methodological and procedural applications (Driver & Tipper, 1989; Laplante, Everett, & Thomas, 1992; Neil, Valdes, & Terry, 1995). Tipper and Baylis (1987) used a measure of cognitive failure (an index of memory failure) to link negative priming to the ability to inhibit distracting information during learning, based on the notion that the processing of primed irrelevant information (e.g., from poor instruction) causes increased processing times and lowers the accuracy of information recall.

Perceived cognitive load

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In terms of mental effort, the students rated each teacher according to how much cognitive load (CL) they experienced during lessons received from the teachers, that is, according to how “hard” they had to work mentally to understand and remember the learning. Cognitive load theory (Sweller, 1994; Sweller, van Merrienboer, & Paas, 1998) represents an approach to instructional design that attempts to maximise features of the limited capacity WM system, by highlighting how schema-driven instruction facilitates automaticity via the “obligatory” processing of well- organised or well-learned schematic information. Stanovich (1990) suggests that interference sensitive tasks actually measure the degree to which conflict situations interrupt the automatic processing of information. Cognitive load theory looks at instruction in terms of the types of extraneous and intrinsic processing loads the instruction places on the WM system (Sweller et al., 1998), emphasising the way in which extrinsic and intrinsic factors interact to require an overall amount of mental effort for the processing. CL was measured here by having the students assign a numerical value on a scale of 1 – 7, (adapted from Pass & van Merrienboer, 1994, see Appendix A) to the amount of invested mental effort they experienced during individual lessons. These individual CL ratings were then averaged for each student, in relation to each of the teachers, to deliver an overall index of cognitive load as perceived by the student for each teacher. As a comparison, students also rated the OSPAN and ANT in terms of how much cognitive load they felt each of these tasks required.

Learning outcomes Student achievement outcomes were reported by the teachers as an overall academic score for each participating student at the end of the academic year (% rating, out of 100). Academic scores were used as an indication of the student’s ability to utilise the instructional scaffolding provided by the teacher in relation to their individual processing abilities. The pattern of relationships that occurred between the instructional strategies, the range of student inhibitory ability, and the reported cognitive load ratings for teachers were then analysed in relation to the student achievement outcomes. Hypotheses relating to these relationships are listed in table 2.

Hypotheses

Table 2: Overview of research hypotheses and how each hypothesis is tested Hypothesis Operational Measure

H1: That a significant positive relationship exists Students scoring higher on the OSPAN tasks (word between overall WM function and student recall & math’s accuracy) will obtain higher achievement outcomes. achievement marks than students scoring lower on these tasks.

H2: That a significant positive relationship exists For the Flanker, “conflict” scores reflect the ability between CI as a function of attentional conflict and to inhibit distracting information, with lower scores student cognitive load (CL) ratings for the indicating more efficient inhibition and higher scores instruction. indicating less efficient inhibition. Cognitive load (CL) ratings reflect how “hard” the students had to work under each instructional approach, with higher ratings indicating greater mental effort and lower ratings indicating less mental effort. Conflict scores (CI) will thus correlate positively to the CL ratings overall, with less efficient inhibitory types (higher CI scores) experiencing greater mental effort (higher CL ratings), and with more efficient inhibitory types (lower CI scores) experiencing less mental effort (lower CL ratings).

H3: That a significant interaction exists between Student CI scores, in combination with CL ratings, student CI, cognitive load (CL), and learning will interact significantly with achievement marks, outcomes (as measured by achievement marks – out as measured by a General Linear Model univariate of 100%). within-subject analysis of variance.

Analyses

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Table 3 shows the characteristics of the variables in these analyses viewed as impacting most directly on the research.

Table 3: Overview of variables related to the research hypotheses.

Variable Measurement Study Mean SD RECALL (word recall/OSPAN) Mean word recall 4.91 1.29 ACCURACY (math’s operations/OSPAN) % (out of 100) 75.29 11.64 CI (ANT: Conflict scores) Response Time (in 184.36 87.84 millisecs.) OSPAN (student CL rating for OSPAN) 7-point scale 4.89 .73 ANT (student CL rating for ANT) 7-point scale 2.04 .78 ACADEMIC (student achievement marks) % (out of 100) 72.72 9.67 T1_CG.LD (student CL rating for teacher 1) 7-point scale 4.12 .81 T2_CG.LD (student CL rating for teacher 2) 7-point scale 4.23 1.19 T3_CG.LD (student CL rating for teacher 3) 7-point scale 4.04 .98 T4_CG.LD (student CL rating for teacher 4) 7-point scale 3.48 1.24

It is to be noted that the conflict variable (CI) contained an extreme outlier, and was therefore re- computed as a logarithm (LnCI) to enable a more proportionate analysis of the findings to proceed (C/F Tabachnick & Fidell, 1996). However, note also that the designated labeling for this variable has been retained as CI in order to maintain conceptual clarity in the discussions that follow. The logarithmic CI is viewed as the independent variable of interest in these analyses. Student cognitive load ratings for the four teachers (T1 – T4_CG.LD), cognitive load ratings for the computerised tasks (OSPAN and ANT), outcomes for the WM tasks (RECALL and ACCURACY), and the variable ACADEMIC (student achievement marks), were all normally distributed.

Results Cognitive load ratings for the OSPAN and ANT test (FLANKER) were significantly correlated, r(114) = .37, p < .01. This is not surprising in that these functions form inter-related aspects of information processing, with attentional processing being dependent upon WM function. Correlations between ACCURACY and RECALL, as measures of overall or complex WM function, were also significant, r(114) = .54, p < .01, again indicating the interdependent nature of these processing functions. A Wilk’s Lambda for the relationship between CI and student cognitive load ratings for the overall ANT was also significant, F(50,58) = 1.61, p < .05, suggesting the students applied themselves reasonably well to the performance of this task. Because of these results, and because normality assumptions have been met for the data, further analyses were performed.

Correlations between student achievement outcomes and the WM measures were significant, with word recall (RECALL) and ACADEMIC returning r = +.24, n = 114, p < .05, two tails, and ACCURACY and ACADEMIC returning r = +.23, n = 114, p < .05, two tails. This is in line with prior research showing that a significant relationship exists between measures of WM function and academic achievement (Conway et al., 2005).

Correlations between student conflict scores (CI) and student cognitive load (CL) ratings for the teachers were mixed. For teacher one (T1) this relationship was significant (r = + . 19, n = 114, p < .05, two tails). For T2 it was also significant (r = +.20, n = 114, p < .05, two tails). T3 also returned a significant finding (r = +.19,n = 114, p < .05, two tails), yet for T4 this relationship was not significant (r = +.18, n = 114, p > .05, two tails). It is of interest that T2 was the least experienced participating teacher (2 years teaching), and that T4 was one of the most experienced teachers (12 years teaching).

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Looking at the interactions occurring between CI, CL, and student achievement outcomes across the various teachers, no significant interactions were obtained. However, a possible trend is apparent for T1, where a within-subjects analysis of variance, using ACADEMIC 2 x CI x CL returned F(23,7) = 3.26, p < .06, η = .71. Looking more closely at this interaction, it appears that although the correlation between CI and CL is not significant, the correlation between CI and ACADEMIC is significant (r = - .23, n = 114, p < .05, two tails). Of additional interest is that this relationship is negative, suggesting that the academic achievement for students with this teacher (who had been teaching the longest, at 14 years classroom experience) stemmed from a situation in which the more efficient inhibitors (receiving lower conflict scores) were obtaining higher achievement outcomes, and the less efficient inhibitors (receiving higher conflict scores) were obtaining lower achievement outcomes. The only other teacher for whom this interaction was noticeable is T2 (with just two years teaching experience), where Achievement x CI x CL returned 2 F(21,7) = 2.86, p < .08, η = .64. Here again, although the correlation between CI and CL is not significant, the correlation between CI and ACADEMIC is significant and negative (r = - .23, n = 114, p < .05, two tails).

Discussion The first hypothesis predicted that a significant, positive relationship would exist between WM function and academic outcomes, proposing that academic achievement is predicted from WM functionality, including short-term storage capacity and the ability to actively discriminate the truth value of information. Significant relationships were found for both RECALL and ACCURACY scores in relation to ACADEMIC, suggesting that in order to support effective learning, instructional designers (including the classroom teacher) need to be aware of the degree to which WM function acts as a processing gatekeeper for learning. This finding is in agreement with a body of information that indicates the generalised importance of WM to the teaching and learning process, and supports the first hypothesis of the research.

Beyond this generalised relationship however, the WM construct also subsumes what Baddeley (1986, 2001) calls executive control mechanisms. The idea of executive control is functionally similar to the notion of an executive attention system, as put forward by Posner and Fan (2005). In this respect it is to be noted that CI is also viewed as an executive control mechanism (Baddeley, 2001; Byrnes & Fox, 1998; Neill et al, 1995), and posited here as the primary cognitive process involved in the resolution of attentional conflict. In the classroom, an important source of attentional conflict is the way the teacher organises and formats instructional information. The second hypothesis tests CI as a measure of attentional conflict by exploring differences in the relationship CI has to the cognitive load ratings that students applied to the various teachers. It is an assumption of this hypothesis that CI offers an additional, distinct explanation as to differences in students’ ability to respond to instruction.

Hypothesis two predicted that a positive relationship would exist between CI (via conflict scores on the ANT) and student cognitive load (CL) ratings for the instruction they received. This relationship was predicted on the basis that, while higher CI scores indicate less efficient processing of conflicting or distracting information and lower CI scores indicate more efficient processing of conflicting or distracting information, higher CL ratings indicate greater mental effort and lower CL ratings indicate less mental effort. Thus, in general, less efficient inhibitors are expected to experience greater cognitive load for the instruction than the more efficient inhibitors. Although, overall, mixed results were obtained for the CI x CL correlations, significant relationships were found for three out of the four participating teachers, suggesting that a close connection does exist between the inhibitory function and mental effort. Of interest, the strongest correlation occurred in relation to the least experienced teacher, indicating that the students felt they had to work harder under this teacher’s instruction. What may be occurring here is that the less experienced teacher, assumed to have a more limited instructional repertoire, is not formatting task-relevant information as clearly as the more experienced teachers, and perhaps is incorporating

7 Distractibility & Brain-based Learning irrelevant information at a higher level than the other teachers. In addition, it is expected that this teacher may not demonstrate the same level of conceptual formatting during instruction, leading to greater ambiguity in the way schematic information is being activated for the students.

Of additional interest, the same CI scores were related negatively to cognitive load ratings for the

OSPAN itself (F[82] = -1.82, p < .05), suggesting that the specific role of the inhibitory function is somehow inversely related to the amount of mental effort required to perform complex, multi-task activities (such as the OSPAN), that require the sophisticated inhibition of incongruent, task- irrelevant information and extended representational holding. In this case it seems that the more efficient inhibitor types found this task easier then the less efficient types. If this interpretation is transferred to the relationships found for CI in relation to CL, it could be said that the negative findings there indicate that the less efficient inhibitors struggled more with the instruction they received from T1 and T2, perhaps due to greater complexity in the teaching (T1?), or to less well organised instructional formatting (T2?). Further research looking more specifically at the role of priming for these different teachers may shed greater light on these possibilities. However, for the present analyses, the significant relationship found for CI scores in relation to word recall is also noteworthy. This finding suggests that the role of CI is important to the processing of task-relevant information in addition to that of irrelevant information, suggesting that inhibition furnishes, overall, a moderating effect on the teaching/learning process. The positive relationship between the main measure of inhibition (CI) and the variable ACADEMIC demonstrates that this relationship does affect learning outcomes.

Hypothesis three proposed that student inhibitory ability (CI) would interact with mental effort (CL), to significantly influence achievement outcomes in a manner distinctive to this particular set of relationships. This hypothesis was not overtly supported. Yet the trend that occurred for T1, as well as some support in this direction for T2, suggest that while CL may not play as strong a role in these interactions as proposed, there is, nonetheless, some significance in the link between CI and achievement. In addition, the distribution initially analysed for CI had been left undifferentiated in terms of establishing and testing discrete levels of inhibitory function, that is, in terms of organizing the distribution into groups representing low, medium, and high CI ability, and testing these against achievement outcomes. Because of this, it was decided to divide the CI distribution into these three groupings, using the distribution mean (M = 189) to demarcate the high CI group from the lower two groups. On this basis, the range of scores for the three CI groups were as follows: Lo_CI = < 147, Med_CI = 147 – 188, Hi_CI = > 188.

Univariate ANOVAS were then used to test each of these CI divisions, to see if any particular inhibitory group (Lo-CI, Med_CI, Hi_CI) would interact significantly with CL in relation to achievement for T1 and T2. The only significant interactions observed were for T2, where this set 2 of relationships was significant for the Med_CI group (F[8,31] = 2.68, p < .05, η = .41), and for the 2 Hi_CI group (F[8,27] = 2.81, p < .05, η = .46). It is again of interest that T2 is the least experienced teacher in the study, and the same teacher for whom the strongest correlation occurred with respect to how much cognitive load (CL) the students experienced. As noted in table 3, the mean CL rating for T2 was 4.23, with a SD of 1.19. Although the cognitive load assigned to T4 displayed a larger standard deviation (SD = 1.24), the mean for this teacher was just 3.48, marking T2 as the single teacher in the study whose instructional loading exhibited both a high mean and large standard deviation.

Looking more closely at these analyses, and because neither of the interactions is negatively signed, what these findings may indicate is that those students more efficient at inhibition were able to achieve higher marks under the instruction of T2, while the less efficient inhibitors tended to achieve lower outcomes. In terms of cognitive instructional approaches therefore, these findings may well signify that, as an executive WM function, inhibition needs to be taken more directly into account when designing instruction, and especially as the instruction concerns students less able to inhibit distracting information. Indeed, establishing inhibitory processing differences to help cater for student-centred learning is expected to become more prevalent as research into the

8 Distractibility & Brain-based Learning executive functions of WM becomes more widespread and disseminated within mainstream educational research.

Implications for teaching These findings highlight how important it is that instructional designers, including the classroom teacher, understand the links that exist between instructional effectiveness and WM function. Firstly because WM function is a good predictor of general achievement outcomes, and secondly because executive WM processes govern specific types of attentional processing, one of the most important aspects of learning to be considered under any instructional approach. In addition, the findings suggest that, as an executive function, inhibition makes a specific contribution to learning, and therefore needs to be considered as a distinctive element within the instructional design process.

The need for this sort of consideration becomes more of an imperative when viewed in light of the modern classroom, which is generally characterised by a large diversity of interests, motivational factors, ability levels, processing styles, communication levels, and extra-curricular support factors. Yet it is precisely within this busy, often chaotic environment that the teacher plans for sustainable learning, and must teach to the diversity of the classroom: to the students’ discovery of knowledge, to possibilities of information mapping, to creativity and curiosity, and to the construction of personalised meaning. Inhibition appears to offer an increased ability to cater for this rich diversity of individual differences, by drawing the teacher’s attention to the fact that distracting information hampers efficient information processing, and more for some students than for others. Although the particulars of an inhibitory approach to instructional design are expected to involve ways in which priming may be used to control for more efficient processing, this will need to be established through additional research that probes the specific elements of priming as used by the teachers. Within the context of the present findings it is the establishment of inhibition as a distinct contributor to attentional processing that is indicated.

On the basis of these findings figure 1 offers a tentative model of instructional processing that is aimed at depicting a moderating effect for the inhibitory function. Note the model proposes that, whereas WM directly mediates the relationship between instruction and achievement, cognitive inhibition, as the conflict-resolving aspect of executive attentional processing, further moderates this relationship by either decreasing or increasing the amount of cognitive load experienced in relation to the learning. As well, it is also suggested that this relationship is reciprocal, with increases in cognitive load limiting the amount of available WM resources, and decreases in cognitive load freeing up the availability of WM resources. What this model suggests, therefore, is that the ability to handle attentional distracters depends on how effective the inhibition function is in relation to the overall limited capacity of the WM system, an aspect of learning considered directly relevant to the instructional design process.

WM Function T T3 4

Instruction Executive Attention Achievement

T2

Cognitive T Inhibition

1

Cognitive Load

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Figure 1: A proposed inhibition-based model of instructional processing

Recommendations for future research Instructional design shapes learning. What this research has sought to establish is that this fact may be especially significant to those learners more easily distracted by conflicting or interfering information. The study findings highlight inhibition as a distinct contributor to the teaching/learning process, and it is thus suggested that the inhibitory function needs to be considered as a unique element when attempting to design an instructional approach that caters as widely as possible for individual differences.

Future investigations of inhibition in relation to instruction will need to test these relationships among an increased number of both participating teachers and the students they teach, with the teachers displaying a wider range of teaching experience, and perhaps grouped in terms of years of teaching experience (e.g., 1 – 4 years teaching experience; 5 – 9 years teaching experience; 10 years+ teaching experience). As well, the use of additional measures of interference sensitivity (such as the Stroop task, another well known measure of incongruent processing) could be used to expand information concerning the role of inhibition in relation to instructional scaffolding.

In addition, the use of explicit priming strategies needs to be trialed at the classroom level, to determine the extent to which priming affects cognitive load and achievement. One way to do this would be to investigate how the instruction primes for the processing of relevant schemata, an aspect of efficient processing that has been emphasised by various educational researchers (e.g., Byrnes, 2001; Nuthall, 2000; Sweller, 1999). Mayer and Moreno (2003) have suggested a possible framework within which this type of investigation might occur. Their study emphasised that effective instruction is directed at the essential processing of information, while simultaneously minimising irrelevant or incidental processing and the representational holding of information in short-term memory store. According to Mayer and Moreno, this sort of instructional approach is designed around what they call the three “building blocks” of cognition: concepts, propositions, and schematic priming. These building blocks are facilitated via the use of various priming related strategies, including concept-maps, advanced organisers, concept integration, prompts and cues, information segmenting, and student summaries. Ongoing research is examining these strategies in terms of the relationship between priming, cognitive load, and on-task achievement, and further results will be presented as they become available.

Conclusion The central theme of learning from a cognitive perspective is the notion of a limited-capacity processing ability and the implications this has for learning. In light of a limited capacity system, these findings suggest that an important question for educators is how to use the notion of inhibition to develop a more integrated model of instructional design, one that engages individual differences more widely and contributes to the development of a more holistic pedagogy. Such pedagogy would require educators to design instruction in a manner that utilises both the task- relevant attentional and task-irrelevant inhibitory functions of executive WM to scaffold more broadly for effective classroom learning.

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Appendix A: Student Cognitive Load Ratings (adapted from Pass & van Merrienboer, 1994)

Mental Load for the lesson: On the following scale, please rate (by circling the appropriate number), how much mental effort you felt you had to make, to understand the teacher’s instructions and achieve the learning outcomes for this lesson.

1 2 3 4 5 6 7 Easy Peasey A Bit Moderate Effort Worked Hard Too Much!

ITEM RATING WHAT IT MEANS (1) Easy Peasey! I could have done this test while talking on the phone or throwing a ball around.

(2) A Bit It took a bit of effort for me to keep up with the test, but not much really…Concentration was not difficult to maintain.

(3 – 4) Moderate Effort I felt I had to work a bit, but it wasn’t too diffi- cult…I did have to concentrate though. (3 = low/moderate effort) (4 = high/moderate effort) (5 – 6) Worked Hard I found the test extremely difficult, but managed to keep on top of it by concentrating all the (5 = low/worked hard) time. (6 = high/ worked hard) (7) Too Much! This test took too much effort…I found it impos- sible (or close to impossible) to keep up with the demand. Couldn’t concentrate enough!

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