Promoting positive learning in Australian students aged 10- to 12-years-old using attribution retraining and cognitive behavioral therapy: A pilot study

Abstract

This study piloted an intervention using attribution retraining and cognitive behavioral therapy techniques to promote positive learning experiences and outcomes for students. This research is an important step to revitalise the dwindling field of attribution retraining research by assessing whether these techniques effectively improve student learning in modern classrooms. Participants were 50 students from grades five and six (age 10- to 12-years-old). Findings revealed that students in the intervention group showed significantly greater average reading levels compared to their control group peers at two months following the intervention. Whilst no other areas measured (mathematics, spelling, and self-concept) reached the level of significance, a number of interesting patterns were observed regarding student selection, intervention focus, and the trajectory of treatment effects. These findings encourage future researchers to expand the range of students targeted by school-based interventions, supports the use of attribution techniques, and highlights that without follow-up data, lagged treatment effects may go undetected. This is one of only a handful of studies to combine attribution retraining with cognitive behavioral therapy, and the results of this pilot study support the need for further research in this area.

Keywords

attribution retraining attributions cognitive behaviour therapy educational psychology inclusive education positive psychology school improvement school psychology special education

The question asked by many school psychologists as to how one should best support the learning and development of students is slowly being answered as researchers uncover the complex and dynamic nature of the learning process (Wigfield, Eccles, Schiefele, Roeser, & Davis-Kean, 2007). In response to our growing understanding of youth development and the vital role psychological factors play there have been calls for the inclusion of positive cognitive interventions within schools to support student well-being (Chodkiewicz & Boyle, 2014; Stallard, 2003; Weiner, 2010). The responsibility of identifying and implementing such intervention programs often resides with school psychologists, as they typically act as the bridge between psychological research and educational practice (Boyle & Lauchlan, 2013). It is therefore important for school psychologists to remain up-to-date on school-based interventions to allow them to make informed decisions when choosing evidence-based programs for their schools. The current research presents the findings of one such intervention, the Believing you can is the first step to achieving program, which combines Attribution Retraining (AR) and Cognitive Behavioral Therapy (CBT) techniques in an attempt to improve academic outcomes and foster positive thinking styles in students aged 10- to 12-years-old.

Attribution theory and attribution retraining

Schooling, by its nature, is imbued with moments of academic success and failure. Following moments of academic evaluation, students typically seek explanations as to why they may or may not be doing well in school. Attribution researchers have been attempting to understand the interaction between how these causal explanations for success and failure (otherwise known as attribution style) influence learning and achievement (Weiner, 2010; Weiner & Sierad, 1975). A slightly optimistic outlook, one in which a student believes s/he is capable of improving upon past failures and will achieve future success, is seen to be advantageous. Such a belief comes from attributions that reflect the personal controllability of academic situations. These types of events would be characterized by thoughts such as ‘I succeeded because I have the ability to achieve my goal’ and ‘I failed because I did not use the correct learning strategy’. Conversely, a maladaptive attribution style involves pessimistic expectations of future failure, where failure is seen to be inevitable, and success is viewed to be beyond one’s control. Maladaptive explanations include, ‘I only succeeded because I was lucky, it won’t happen again’ and ‘I failed because I am not clever enough’.

The link between maladaptive attribution styles and academic underachievement has been well documented (Au, Watkins, & Hattie, 2010; Chan & Moore, 2006; Shmulsky & Gobbo, 2007). Students endorsing adaptive attribution styles have been shown to have higher levels of self-concept, to work harder, and persist longer in the face of difficulty (Marsh & Scalas, 2011; Núñez et al., 2005). While students holding maladaptive attribution beliefs have been observed to employ the following: Low self-beliefs and motivation, limited effort, task avoidance, and little resilience (Hsieh & Kang, 2010; Swinton, Kurtz-Costes, Rowley, & Okeke-Adeyanju, 2011). Of these factors, self-perceptions have attracted arguably the most research attention. The degree to which an individual believes him or herself to be capable of achieving a goal impacts the extent to which that student invests time and effort in the learning activity and subsequently what s/he achieves (Pinxten, Marsh, De Fraine, Van Den Noorgate, & Van Damme, 2014; Yeung, Craven, & Kaur, 2014). With research observing these trends across the lifespan (Chan & Moore, 2006; Swinton et al., 2011), there is growing insight into the potential for psychological interventions targeting attribution styles to combat lifelong underachievement.

AR is an intervention designed to disrupt and discontinue the cycle of underachievement linked to maladaptive attribution styles and is based on the premise that academic improvements can be fostered through cognitive restructuring without the need for extra academic remediation (Koles & Boyle, 2013). Weiner and Sierad (1975) implemented one of the first AR interventions over four decades ago, finding that they could improve students’ mathematical performance through encouraging adaptive attributions. Since then, research has emerged investigating the potential benefits of this intervention form. Encouraging results have shown AR intervention leads to improvements in academic performance, attribution style, motivation, and self-concept (Chan & Moore, 2006; Dresel & Haugwitz, 2008; Haynes Stewart et al., 2011; Perry, Stupnisky, Hall, Chipperfield, & Weiner, 2010; Toland & Boyle, 2008). Not all studies, however, have found consistent positive outcomes following AR interventions (Fulk, Mastropieri, & Scruggs, 1992; Morris, 2013).

Unfortunately, over the past 20 years this field of research has been losing momentum (Chodkiewicz & Boyle, 2014). Of the small number of contemporary researchers still studying AR interventions, a handful have been looking to CBT as a means of rejuvenating interest in the field. For example, Toland and Boyle (2008) designed a program combining AR and CBT techniques to improve the reading skills of middle school students in Scotland. Similarly, Berkeley, Mastropieri, and Scruggs (2011) in the USA combined AR instruction with techniques taken from CBT, finding that such instruction was instrumental in maintaining the positive reading improvements following what they termed academic strategy training.

CBT is one of the most widely used modern therapeutic techniques (Dawood, 2013), making it a good option for researchers looking to strengthen AR interventions. Three decades ago Försterling (1985) stated that ‘because there are many similarities between cognitive behavior modification and attributional approaches to psychopathology … attributional change could easily be implemented in the practice of cognitive therapy’ (p. 510). Both forms of therapy look to restructure thinking styles and perceptual patterns to induce behavioural changes. However, AR remains distinct from typical CBT interventions by specifically targeting the causal reasons used to explain events and inducing change through challenging and reshaping future expectations based on these causal assessments. Combining AR and CBT techniques promises to therefore improve both the quality of AR interventions and add a new dimension not often seen in current CBT based programs.

AR interventions have typically been targeted at students experiencing learning difficulties (Berkeley et al., 2011; Toland & Boyle, 2008). While the link between underachievement and maladaptive attribution styles is well established (Au et al., 2010; Shmulsky & Gobbo, 2007), not all students who may be struggling actually demonstrate maladaptive attributions (Núñez et al., 2005) nor do they always feel that they are in need of intervention (Boyle, 2007). It is accepted that self-perceptions are shaped through social comparisons (Nagengast & Marsh, 2012) and that perceptions are a better predictor of attribution style than actual academic ability (Banks & Woolfson, 2008). It is therefore possible that even high achievers can feel as if they are struggling and develop maladaptive attribution styles. By widening the eligibility criterion for AR interventions beyond just academically low achieving students, there is the potential to reach a much broader population of students in need.

Current research aims and hypotheses

The current study set out to pilot an intervention combining both AR and CBT techniques to foster positive learning in students aged 10- to 12-years-old. This pilot study investigated the viability of strengthening an AR intervention by incorporating techniques of CBT, such as the analysis of self-talk and teaching coping skills. Following the footsteps of some noted researchers (Berkeley et al., 2011; Toland & Boyle, 2008) this study aims to take the concept of a combined AR and CBT program one step further by using a highly structured program with an interactive and engaging format. The design of a student workbook and teacher manual allows this program to be easily translated into real classrooms by school psychologists or classroom teachers, making it a distinct addition to the few such interventions that have come before it. As part of the current study, small groups of five to seven students were withdrawn from their classrooms to participate in hourly lessons each week across a single school term. A provisional school psychologist ran these sessions.

A key aim of this study was to identify whether a purely cognitive based intervention would have positive ramifications for student academic achievement. Measures of student reading, spelling, and mathematics were predicted to increase following the intervention. It was also predicted that all students would show positive outcomes from the intervention, regardless of initial levels of academic ability.

Method

Participants

Participants were from six public primary schools in inner city Melbourne, Australia. Initially, 170 students in Grade 5 and 6 (aged 10- to 12-years-old) completed a screener survey (The Children’s attribution style questionnaire – revised). A total of 58 students were selected based on signs of maladaptive attribution profiles. Of the sample 60% were male and 40% female. The age ranged from 10:2 to 12:6 years, with a mean age of 11:1 years (SD = 0.62). From each school between five to seven students from various grade five and six classrooms were randomly allocated into the AR group. The remaining students were allocated to the control group. Due to the small number of participants in each school this resulted in a larger number of students being allocated to the intervention group (N = 34) than the control group (N = 24). During the research project eight students withdrew from the study. Reasons for participant attrition included extended school absence (AR: N = 2; Control: N = 2), school relocation (Control: N = 2), and voluntary withdrawal (AR: N = 1; Control: N = 1). The final sample consisted of 50 participating students; 31 students in the intervention group and 19 in the control group.

The intervention

The Believing you can is the first step to achieving intervention uses both AR and CBT techniques (Chodkiewicz & Boyle, 2015) to teach three core elements: The link between thoughts, feelings, and actions; the identification of unhelpful thinking patterns/causal attributions; and the learning of coping skills through challenging unhelpful thoughts and restructuring attributions used to explain success and failure situations (refer to Table 1 for a description of topics covered). The program is made up of eight core sessions running for approximately one hour each. The sessions were highly structured and followed a student workbook and teacher’s manual. Each session began with a class discussion of the new topic followed by a group-based activity/game, and ended with independent workbook activities. Students were also required to complete home learning sheets between each session. Further elaboration of the content along with copies of all worksheet material can be located in Chodkiewicz and Boyle (2015).
Table 1.
Outline of AR Program content.

Session Session title Session content

1 Thoughts → Feelings → Actions This session introduces students to the program, introduced feeling vocabulary and demonstrates the link between thoughts feelings and actions

2 Helpful and Unhelpful Pathways This session demonstrates how thoughts influence behaviour in helpful and unhelpful ways through the thought – feeling – action pathway. Students work on identifying helpful thoughts to use in difficult situations

3 Where do we have helpful and unhelpful thoughts? This session helps students identify unhelpful thoughts in their own lives. Students are encouraged to be aware of places/situations in which unhelpful thoughts are more likely to arise, and to prepare helpful thoughts in advance of difficult situations. The session also encourages students to identify and appreciate moments of personal success through the use of self-congratulations

4 How does this all relate to school and learning? This session introduces the concept that thoughts can have a big impact on how well students perform in school and what they are able to achieve. Students are shown examples of how thoughts can change a student’s motivation, persistence, and learning behaviours. Students are then given the chance to practice replacing unhelpful thoughts with more helpful ones

5 Why do we succeed and fail? This session introduces students to attribution theory. Adaptive and maladaptive attributions for success and failure situations are outlined. Students then have the chance to practice using adaptive explanations in response to hypothetical situations

6 Becoming Super Heroes – Stopping unhelpful thoughts This session teaches students skills to overcome unhelpful thoughts in their every day lives using three super powers. Students are encouraged to identify if their thoughts are helpful/unhelpful, analyse whether their causal explanations are adaptive/evidence-based, and restructure unhelpful thinking

7 Practicing Super Powers This session is a chance for students to further practice the skills learnt in the last session and apply them to a range of different situations

8 Review This session is used to consolidate what students have learnt in the program by creating a video that can be used to teach others about the program

Materials

The Children’s attribution style questionnaire – revised (CASQ-R; Thompson, Kaslow, Weiss, & Nolen-Hoeksema, 1998) consists of 24 vignettes asking a student to choose causal explanations to match each event. Scores on the CASQ-R indicate the degree to which a student attributes internal, stable, and global factors to positive and negative events. The CASQ-R has been shown to have a moderate internal consistency, with reliability measures (Cronbach alphas) of 0.61 for positive composite results and from 0.53–0.60 for negative composite, with an overall test–retest reliability coefficient of 0.53 (Thompson et al., 1998). The reliability of the CASQ-R in the current pilot study were as follows: Positive composite α = 0.526 and negative composite α = 0.525.

The Wechsler individual achievement test, second edition (Australian) abbreviated (WIAT-IIA: Wechsler, 2007) is a standardized brief measure of academic achievement across the three academic domains of reading, mathematics, and spelling. The grade based reliability coefficients of the three subtests, Word Reading, Numerical Operations, and Spelling are respectively: Grade 5: 0.96, 0.91, and 0.91; Grade 6: 0.95, 0.91, and 0.94 (Wechsler, 2007).

The Myself-as-a-learner scale (MALS; Burden, 1998) is a student self-report measure of academic self-concept. The 20-item questionnaire asks students to rate enjoyment of learning and ability to learn on a five-point Likert scale. Scores range from 20–100. The scale was shown to have an alpha reliability index of 0.85 (Burden, 1998).

Procedure

Across the six participating schools a total of 170 students completed a group screener (CASQ-R) used to identify students showing low levels of adaptive attribution styles (≤ 0 overall index and/or ≤ 5 negative or positive index). This screener was group administered. All eligible students completed pre-intervention assessments consisting of the WIAT-IIA and MALS.

Five to seven students from each school were then randomly allocated to the intervention group with all remaining students being assigned to the control group. Students in the intervention group attended eight weekly sessions, which were conducted by the first author across the six participating schools. This helped ensure that treatment fidelity was maintained and measures were built into the project as suggested by Houghton et al. (2013). Students in the control group did not take part in any of the intervention sessions and remained in their regular classes. Post-intervention and follow-up (two-months) assessments included individually administered CASQ-R, WIAT-A, and MALS.

Results

Initial group differences

A multivariate analysis of variance (MANOVA) was conducted to identify initial differences between intervention and control groups. The intervention group was observed to hold significantly lower academic self-concept beliefs when compared to their control group peers, F(1,48) = 6.03, p = 0.018, which represents a medium effect size (η²= 0.112). As a result, the initial levels of student academic self-concept were included as a covariate in all further analyses. No significant differences in any of the other measures of interest were observed (age, grade, gender, attribution style, or academic skill). Pre-intervention means and standard deviations are displayed in Table 2.
Table 2.
Pre-intervention means and standard deviations categorized by group.

Intervention Control

Reading 98.77 (10.57) 100.11 (9.71)

Mathematics 93.77 (14.49) 97.63 (22.34)

Spelling 96.19 (14.90) 101.53 (15.22)

Academic Self-concept 68.90 (9.15) 75.42 (9.05)

Attribution Style 3.10 (2.99) 2.95 (2.17)

Academic achievement

Reading

Based on observed average reading levels, the intervention group showed greater levels of reading growth across the year compared to their control group peers. A mixed ANOVA was conducted to assess whether the observed group differences were significant, with initial levels of academic self-concept included as a covariate. Results indicated no significant main effect of time, F(2,94) = 1.0, p = 0.36 or group, F(1,47) = 0.87, p = 0.36. However, a significant interaction between time and group was observed, F(2,94) = 3.82, p = 0.025, which was classified as having a medium effect size (η²= 0.075). That is, students in the intervention group achieved significantly greater average reading levels compared to students in the control group. Contrast analyses revealed a significant difference between groups on average reading performance only between post-intervention and follow-up measures F(1,47) = 6.9, p = 0.012, which was found to have a large effect size (η²= 0.128). These results indicate that the intervention group had a significantly greater increase in average reading compared to the control group, however this difference only began to appear after the completion of the program, in the two months following the intervention. Such a result suggests that students require time to internalize and practice intervention skills before reading outcomes can be influenced.

A post hoc analysis was undertaken to investigate the link between initial academic ability and reading outcomes among students in the intervention group. Each student in the intervention group was assigned to one of three academic classifications (Low: Standard score below 90, Average: Standard score of 90–110 and High: Standard score above 110) based on the pre-intervention reading assessment. A mixed ANOVA was then conducted to assess differences in reading improvements. A significant main effect for both time, F(2,56) = 54.7, p < 0.001, and group, F(2,28) = 51.1, p < 0.001 were observed. However, no significant interaction effects between academic group and reading achievement over time were found, F(4,56) = 1.5, p = 0.20, suggesting that students of all ability level in the intervention group demonstrated an equivalent degree of change in reading ability.

Mathematics

A mixed ANOVA was conducted to assess whether there were group differences in mathematics achievement measured across the three time-periods, with initial levels of academic self-concept included as a covariate. Results indicated no significant main effect of time, F(2,94) = 2.38, p = 0.12 or group F(1,47) = 0.14, p = 0.71, as well as no significant interaction effect, F(2,94) = 0.30, p = 0.71.

Spelling

A mixed ANOVA was conducted to assess whether there were group differences in spelling achievement measured across the three time-periods, with initial levels of academic self-concept included as a covariate. Results indicated no significant main effect of time, F(2,94) = 0.20, p = 0.82 or group F(1,47) = 0.15, p = 0.71, as well as no significant interaction effect, F(2,94) = 1.10, p = 0.34.

Academic self-concept

A spike and then a subsequent drop in self-concept immediately following the intervention implementation was seen in the intervention group but not the control group. A mixed ANOVA was conducted to assess if these patterns of academic self-concept were significant. Results indicated a significant main effect of time, F(2,96) = 88.62, p = 0.043, which was found to have a medium effect size (η²= 0.063). Contrast analyses revealed a significant difference of academic self-concept over time only between pre-intervention and post-intervention measures F(1,48) = 4.47, p = 0.04, suggesting the greatest change in self-concept occurred during the intervention period. As indicated by the initial analysis significant main effect for the groups was confirmed F(1,48) = 5.5, p = 0.023. No significant interaction effect between time and group were observed, F(2,96) = 0.74, p = 0.48, suggesting that the intervention did not have a significant impact on academic self-concept.

Attribution style

A mixed ANOVA was conducted to assess if patterns of attribution style were significant between the two groups, with initial levels of academic self-concept included as a covariate. Results indicated no significant main effect of time, F(2,94) = 0.29, p = 0.75 or group F(1,47) = 0.04, p = 0.84, as well as no significant interaction effect, F(2,94) = 3.1, p = 0.052.

Discussion

Schools and school psychologists around the world are looking for psychological-based interventions to support the well-being, achievement, and development of their students (Chodkiewicz & Boyle, in press). This research aimed to investigate an intervention that can be used within schools by piloting a program combining AR and CBT techniques to foster changes in students aged 10- to 12-years-old. It was hypothesized that the intervention would lead to positive improvements in attribution styles, self-concept, and ultimately academic achievement among students involved in the intervention. It was also hypothesized that students of all academic ability levels would benefits from the intervention.

Academic improvement

Toland and Boyle (2008) observed that through a combined AR and CBT intervention they could affect academic improvements without conducting any academic based remediation. When attempting to replicate these findings the current study found significant improvements in one, but not all, academic domains.

Domains of academic improvement

Students in the intervention group were observed to show significantly greater average reading ability across the year compared to their control group peers. No differential effects were found when measuring mathematic and spelling achievement. This once again mirrors the earlier work of Toland and Boyle (2008) who observed improvements in reading but not spelling achievement following their AR and CBT intervention. Toland and Boyle (2008) explained this finding by postulating that specific academic domains may be more malleable to improvements using AR because they lend themselves more easily to independent practice. Such an explanation fits with the findings of this pilot study, as many primary school students are encouraged to read for pleasure outside of school but may not independently practice mathematics or spelling.

Trajectory of treatment effect

The authors were also interested in understanding the trajectory of treatment effects, by looking at both immediate and delayed academic achievement outcomes. An analysis of the trajectory of improvements in students reading ability across the study provided an interesting observation – a lagged effect. Over the intervention period, no real changes in reading ability were observed between the two groups of students. It was only in the two-month period following the intervention that students in the intervention group displayed a significant difference in average reading ability compared to their peers.

A lagged effect of academic change following AR interventions is in line with Weiner and Sierad’s (1975) and Weiner’s (2010) view of an indirect relationship between attribution interventions and academic attainment. Weiner claims that it is through an increase in positive study behaviours brought about by adaptive attribution beliefs that leads to gradual improvement in academic skills. The current finding of lagged academic outcomes is also consistent with previous research by Berkeley et al. (2011), who measured reading achievement six weeks following an intervention, and Ziegler and Heller (2000) who assessed physics results months after the completion of their intervention. Both research teams found that the greatest levels of academic improvements were made in the weeks and months following the intervention. It may therefore stand to reason that spelling and mathematical improvements may simply take more time to appear than reading. Furthermore, the limited follow-up provisions of this research project (only two months) could be an explanation as to the failure to observe differences between the intervention and control group in these academic domains.

Selection criteria

As the question of who stands to benefit most from an AR and CBT intervention remains unclear, there is a growing body of evidence challenging the mainstream view that academically low achieving students should be the only focus of an intervention (Banks & Woolfson, 2008; Núñez et al., 2005). The current study attempted to assess if selection based on a students’ attribution profile may be a viable selection alternative. The results showed no differential treatment effects between students of differing abilities. This finding supports previous research by Hall, Hladkyj, Perry, and Ruthig (2004) and Toland and Boyle (2008) who observed AR interventions having positive impacts on both average and above average students. This study, therefore, adds support to the argument for extending the student selection beyond just struggling learners to support the learning of all students; a position which fits well within the current educational psychology thinking with regards to inclusive schools (Boyle, Topping, Jindal-Snape, & Norwich, 2012; Boyle, Topping, & Jindal-Snape (2013); Kraska & Boyle, 2014).

Academic self-concept

When considering the effect a combined AR and CBT intervention has on a student’s academic self-concept, the current study did not find significant differences in self-reported self-concept between the two research groups. Students who took part in the intervention however, did show a slight spike in their average self-concept directly following the intervention, a phenomenon that was not seen for the control group. This observed spike may suggest that any small gains in self-concept from taking part in the intervention may be short lived. These findings raise concerns regarding the true capacity for a one-off intervention to bring about marked and enduring change in students’ academic self-concept.

Attribution style

Given that the current intervention did not show significant differences in attribution style among the target students, but did show a higher level of reading ability for the intervention group compared to their control group peers, the question must be asked: What caused students to improve in reading?

It is possible that other factors not measured in this study may have been responsible for the interventions positive effect on reading achievement. For example, an increase in student motivation and persistence behaviors has previously been linked to both AR interventions and improved academics (Chan & Moore, 2006; Dresel & Haugwitz, 2008). It would be important for future research to include a greater breadth of cognitive and behavioral measures to more clearly identify potentially links between the intervention and achievement outcomes.

Similarly it remains unclear to what degree the AR and CBT techniques separately influenced student outcomes. The finding of no change in attribution styles following the intervention may suggest that academic achievement is influenced most strongly by the CBT components of the program. Alternatively the amount of AR techniques present in the combined form of intervention may not have been large enough to foster changes in student attribution styles robust enough to be identified by the form of self-report measurement used.

While AR is one method used to shape attributions, a number of extraneous variables also influence a student’s attribution style as they learn, such as previous learning experiences, teacher feedback, and the home environment (Morris, 2013; Perry & Hall, 2009). The findings of the current study may further reflect an inability for small-scale research projects to control for all the key external factors that may be influencing a student’s attribution style. These findings might, on the other hand, highlight that there is still a high level of uncertainty in the field as to what changes are conceivably achieved by AR interventions.

Limitations of the current study

The current study was affected by a number of limitations. Firstly, although a reasonable sample size was sourced as compared to other pilot studies in this area, the size of the sample may have limited the ability to find significant results. Conducting research within school settings can be fraught with obstacles and soft variable difficulties, with research designs being influenced by school schedules, policies, available resources, and attitudes of teachers and/or students. One such implication for the current study was the use of a passive control group as opposed to an active attention comparison sample. This decision was based on the ethical dilemma and schools’ justified reticence to simply remove students from learning in the classroom to be in a control group. It can therefore not be ruled out that the observed results may have been due to the extra attention the students in the intervention group received. Having a single researcher deliver the program across all groups further limited the research findings, as it may be possible that the personal qualities of the program administrator may have led to improved scores. The same principle applies to the students having different teachers who may have taught reading using different approaches. The results of this pilot study should therefore be viewed as providing preliminary support for the viability of a combined AR and CBT intervention and not causal proof of its’ effectiveness. Further research is needed to understand the true effectiveness and generalizability of the intervention in varied school settings.

A further area of potential limitation in the current study was the attribution style measure, CASQ-R. While the CASQ-R has been widely used in research (Collett & Gimpel, 2004; O’Kearney, Kang, Christensen, & Griffiths, 2009; Roberts et al., 2010; Sheikh et al., 2008), in this study a number of issues were observed. For one, both the current research and Thompson et al. (1998) found rather low reliability estimates when using the CASQ-R. During the research, concerns were also raised over the validity of student responses to the CASQ-R questions, as some students struggled to relate to the hypothetical situations presented. Furthermore, the use of hypothetical scenarios may not be the most ideal form of measuring attribution styles as evidence has shown that the attribution beliefs a student demonstrates in response to hypothetical scenarios can be drastically different to the causal reasons used in real life situations (Gipps & Tunstall, 1998). Upon reflection the CASQ-R may not have been the ideal measure for use in this study. Future researchers may benefit from using an attribution scale with a higher reliability rating and multi-modal assessment approach.

Implications and future directions

The findings of this pilot study have provided support for the effectiveness of combined AR and CBT interventions to impact reading positively and suggest that school psychologists should consider such interventions when selecting programs to support students learning and development. Though the results of the current study were modest, they nevertheless support the viability of this positive cognitive intervention developed by the authors. The results demonstrate that a purely cognitive based intervention can have positive ramifications for student academic achievement, albeit not in every curricula area.

It is important, however, to note that between group differences were not noted in all the targeted areas. While fewer significant results are not uncommon for a small scale pilot study such as this, it would nonetheless be beneficial for future researchers to investigate whether a larger sample size coupled with longer measurement follow-up periods would lead to a wider range of positive outcomes.

The findings also help to address the question of who should be the target of attribution interventions. The current observations discourage the narrow view of selection based on learning difficulties, encouraging school psychologists to consider the growing evidence that all students can benefit from school-based interventions, regardless of their initial level of academic ability.

Another interesting findings was that significant changes in average reading levels between groups were only seen after a delay of two months, once students had time to implement newly learnt skills into everyday life. This finding underscores the importance for school psychologists not to see such interventions as a ‘quick fix’; rather these interventions are the first part of a longer process.

To further our understanding of combined AR and CBT interventions future researchers are encouraged to investigate the unique contribution that the two therapeutic techniques separately have on learning. For example, comparing groups of students who receive either CBT only, AR only, or both CBT and AR may add some interesting insight into the potential benefits of a combined intervention form.

Conclusion

The aim of this pilot study was to investigate the potential benefits of combining AR and CBT to better support student learning. School psychologists are continually expected to be innovative and to consider various methods to improve the learning of students, especially those lacking in motivation. This pilot study has indicated that there is potential in using this type of intervention, which may benefit the learning of students with the support of the school psychologist whilst working in conjunction with school teaching staff. The observed change in average reading achievement suggests interventions combining AR and CBT can positively improve learning in some academic domains. While change was not seen across all areas measured, the preliminary findings of this pilot study indicate that such interventions bode well for attribution retraining interventions and are worthy of further investigation.

Session	Session title	Session content
1	Thoughts → Feelings → Actions	This session introduces students to the program, introduced feeling vocabulary and demonstrates the link between thoughts feelings and actions
2	Helpful and Unhelpful Pathways	This session demonstrates how thoughts influence behaviour in helpful and unhelpful ways through the thought – feeling – action pathway. Students work on identifying helpful thoughts to use in difficult situations
3	Where do we have helpful and unhelpful thoughts?	This session helps students identify unhelpful thoughts in their own lives. Students are encouraged to be aware of places/situations in which unhelpful thoughts are more likely to arise, and to prepare helpful thoughts in advance of difficult situations. The session also encourages students to identify and appreciate moments of personal success through the use of self-congratulations
4	How does this all relate to school and learning?	This session introduces the concept that thoughts can have a big impact on how well students perform in school and what they are able to achieve. Students are shown examples of how thoughts can change a student’s motivation, persistence, and learning behaviours. Students are then given the chance to practice replacing unhelpful thoughts with more helpful ones
5	Why do we succeed and fail?	This session introduces students to attribution theory. Adaptive and maladaptive attributions for success and failure situations are outlined. Students then have the chance to practice using adaptive explanations in response to hypothetical situations
6	Becoming Super Heroes – Stopping unhelpful thoughts	This session teaches students skills to overcome unhelpful thoughts in their every day lives using three super powers. Students are encouraged to identify if their thoughts are helpful/unhelpful, analyse whether their causal explanations are adaptive/evidence-based, and restructure unhelpful thinking
7	Practicing Super Powers	This session is a chance for students to further practice the skills learnt in the last session and apply them to a range of different situations
8	Review	This session is used to consolidate what students have learnt in the program by creating a video that can be used to teach others about the program

	Intervention	Control
Reading	98.77 (10.57)	100.11 (9.71)
Mathematics	93.77 (14.49)	97.63 (22.34)
Spelling	96.19 (14.90)	101.53 (15.22)
Academic Self-concept	68.90 (9.15)	75.42 (9.05)
Attribution Style	3.10 (2.99)	2.95 (2.17)

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author biographies

Alicia R Chodkiewicz is a Psychologist working in the areas of Education and Child Development, and a member of the Australian Psychological Society. Alicia has completed a Bachelor of Psychology at the University of Sydney, a Master’s Degree in Psychology at Monash University, and is currently completing a Doctor of Philosophy at the University of New England, Australia. Recent publications include Believing you can is the first step to achieving (with Christopher Boyle), published by Jessica Kingsley Press (2015).

Christopher Boyle, PhD, is a Registered and Chartered Psychologist with the Australian and British Psychological Societies, respectfully. He is as Associate Professor in Inclusive Education and Psychology at the University of Exeter, UK. He is also the Editor in Chief of The Educational and Developmental Psychologist. Recent publications include Ethical practice in applied psychology published by Oxford University Press (2014), and Believing you can is the first step to achieving (with Alicia Chodkiewicz), published by Jessica Kingsley Press (2015).

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