In the last decade, a revolution in learning theory and educational psychology has brought dramatic changes in our current understanding of cognitive development processes. This new direction for learning theory and cognitive education was influenced in part by Feuerstein’s mediated learning experience approach to education (Kozulin and Presseisen, 1995). Feuerstein’s theory of cognitive structural modifiability concentrates on the experience of mediated learning, which is the intentional process of focusing a learner’s interaction with the world (Feuerstein et al., 2010). Mediated learning experiences are critical to the development of the unique human conditions of modifiability, or the capacity to benefit from exposure to stimuli in a more generalized way (Feuerstein, 1990).

Research also indicates that brain training can change cognitive functioning based on concepts of neural plasticity and environmental stimulation. For example, Willis et al. (2006) cites evidence that sustained engagement in cognitively stimulating activities impacts neural structure and given appropriate practice, humans consequently improve on essentially every task performed. Nouchi et al. (2013) suggest that adaptive cognitive training techniques in particular reveal far transfer effects. Furthermore, new research on brain training programs has been shown to manifest both near transfer and far transfer of training-specified tasks as well as other non-trained cognitive functions (Smith et al., 2015). Encouraging outcomes following cognitive training include significant post-training improvement in working memory (Dahlin, 2013), processing speed (Gibson et al., 2015), and general intelligence (Jausovec and Jausovec, 2012; Carpenter et al., 2016). Evidence of improvements in math and reading skills were found in a study using BrainWare Safari with children with learning disabilities (Avtzon, 2012). Neural activity changes following cognitive training have also been documented (Westerberg and Klingberg, 2007). However, there are many studies that do not support the use of cognitive training for remediating cognitive deficits. For example, a meta-analysis of 13 cognitive training programs indicated consistently low-effect sizes for the training effect on executive functions (Karch et al., 2013). Further, Redick et al. (2013) found no evidence of intelligence improvement following working memory training—a finding consistent with the oft-cited analysis of Melby-Lervag and Hulme (2016) whose review of working memory training studies produced “no convincing evidence” that training leads to cognitive benefits.

Although prior cognitive training studies lend insight on neural plasticity, further research is needed to investigate the transfer of cognitive change to academic success. Despite the controversy surrounding the efficacy of cognitive training, brain training interventions have gained appeal in the past decade for both educators and clinicians as an alternative and supplement to traditional skills-based approaches for learning (Kearns and Fuchs, 2013). Fiorello and Primerano (2005) state that underlying cognitive abilities are associated with academic achievement in school, and contend the way a client processes, stores, retrieves, and analyzes information influences how that client will perform in school. Thus, certain specific abilities may be important for understanding the development of particular skills above and beyond the understanding gained from general cognitive and achievement clusters. Several studies on cognitive training support this notion. For example, Shiran and Breznitz (2011) reported improvements in decoding, reading rate, and comprehension for both dyslexic and skilled readers following working memory training—suggesting a relationship between working memory capacity and reading ability. Dunning and Holmes (2014) also found improvements in 6-year-olds’ reading and math grades following 23 sessions of computerized executive function training. Titz and Karbach (2014) assert that interventional training in working memory and executive function has been shown to amplify academic achievement.

However, there is contrasting evidence as well. In a randomized controlled trial of Fast ForWord—a reading intervention that targets auditory and visual discrimination, attention, and working memory—researchers found little differences among the three intervention groups and the control group (Gillam et al., 2008). A study using the Cognitive Training for Children inductive reasoning intervention also failed to show significant differences between treatment and controls on measures of academic achievement (Barkl et al., 2012). Similarly, an adaptive working memory training program also failed to affect treatment and controls differently on measures of single-word reading, spelling skills, or mathematics tests (Henry et al., 2014). Indeed, the results are mixed.

Although there is some evidence that cognitive skills training improves standardized test scores and academic achievement, behavioral changes are important to address as well. Rabipour and Raz (2012) suggest that cognitive training can produce changes measured at the behavioral as well as the neuroanatomical and functional levels. Early research in this area reveals that computer-based cognitive rehabilitation for patients with addiction-related neurological deficits improved both cognitive functioning and socially appropriate behaviors (Fals-Stewart and Lucente, 1994). In other words, behavioral improvements coincided with increases in neurological functioning. More recently, a meta-analysis of cognitive training studies (Karch et al., 2013) revealed favorable effects on secondary measures of outcomes such as behavior, regardless of academic gains. In addition, Farias et al. (2017) reported a reduction in both internalizing and externalizing behavior problems following cognitive training with Captain’s Log for children with ADHD. Yet, there is extant research with opposing findings. Three studies using the Cogmed working memory training program failed to find parent-reported improvements in behavior (Gray et al., 2012; Egeland et al., 2013; Chacko et al., 2014). Although other studies have shown improvements in parent-reported behavior (Green et al., 2012) and teacher ratings of oppositional behavior (Beck et al., 2010), the ratings did not differ significantly from the control groups in either study.

The aim of the current study was to examine parent-reported academic and behavioral benefits—as well as cognitive improvements—from clinician-delivered LearningRx cognitive training interventions. Prior research on LearningRx has revealed significant gains in cognitive skills following cognitive training. In a randomized controlled trial with children ages 8–14 with learning difficulties, there were statistically significant differences between intervention and controls on measures of working memory, long-term memory, auditory processing, visual processing, logic and reasoning, processing speed, and IQ score (Carpenter et al., 2016). These results were similar to an earlier controlled study (Gibson et al., 2015) and corroborate observational study findings on nearly 18,000 clients between 2010 and 2015 (Moore and Wainer, 2016). In a randomized controlled study with high-school students, magnetic resonance imaging revealed increased global connectivity in the LearningRx group as well as connections that correlated with gains on cognitive test scores (Ledbetter et al., 2016). However, a controlled study on transfer effects of LearningRx cognitive training has not been conducted. By examining parent ratings of intervention and control groups, the current two-part study addresses that gap in the literature. The study also examined changes in cognitive test scores for the intervention groups.

The questions answered by the first part of the study were: (a)

Does the completion of the ThinkRx or ReadRx cognitive training program reduce symptoms of academic difficulty as reported by parents?

For the first part of the study, we hypothesized that:

H1. Participants who completed cognitive training with ThinkRx or ReadRx would have reduced symptoms of academic difficulty as reported by parents compared with the control group.

For the second part of the study, we examined the change from pre-test to post-test in cognitive function as measured by the Woodcock Johnson III (WJ III)—Tests of Cognitive Abilities, for both intervention groups. The questions answered by the study were: (a)

Did participants who completed the ThinkRx or ReadRx cognitive training program achieve statistically significant changes in cognitive test scores?

For the second part of the study, we hypothesized that:

H3. Participants who completed cognitive training with ThinkRx or ReadRx would have statistically significant changes in cognitive skills as measured by the WJ III.

Prior to recruiting participants, the author obtained ethics approval by Capella University’s Institutional Review Board for his doctoral dissertation research. Participants were recruited via an email invitation sent by LearningRx headquarters to 6,000 parents of clients who had completed a pre-assessment consultation or a training program at any of the 88 LearningRx cognitive training centers in the United States. Parents replied to the email if they were interested in participating. A link to the full description of the study was then sent to those interested participants where they gave online informed consent and completed the Learning Skills Rating Scale (LSRS) via a web-based survey. LearningRx headquarters then provided the author with the cognitive testing and pre-test LSRS survey results previously completed by the same participants when their children had completed testing at a LearningRx center. Because only parent survey and archived test data were collected, assent from children was not required.

The sample for the current study included 178 clients between the ages of 5 and 18 (M = 12.1), including 78 females (44%) and 100 males (56%). Participants who completed the LSRS were placed in one of three groups: those who had completed a 60-h cognitive training program (ThinkRx), those who had completed a 120-h cognitive training plus reading program (ReadRx), and those who had completed an initial assessment but had not enrolled in a cognitive training program (Control).

The ThinkRx group (n = 67) included 29 females and 38 males with parent-reported disabilities of ADHD (n = 22), autism (n = 3), dyslexia (n = 4), and speech delay (n = 4). The ReadRx group (n = 53) included 27 females and 26 males with parent-reported disabilities of ADHD (n = 18), autism (n = 3), dyslexia (n = 12), and speech delay (n = 9). The control group (n = 58) included 22 females and 36 males with parent-reported disabilities of ADHD (n = 17), autism (n = 4), dyslexia (n = 6), and speech delay (n = 7). In the current study, the clients in the ThinkRx intervention group had completed 4–5 h of training each week for an average of 12 weeks. Clients in the ReadRx group had attended 4–5 h of training per week for an average of 24 weeks.

For the current study, we used a quasi-experimental design to examine the pre-training to post-training differences in academic difficulty and oppositional behavior symptoms of participants who had and had not completed a LearningRx cognitive training program; and the archived cognitive test data from participants who had completed the ThinkRx or ReadRx intervention.

The LearningRx cognitive training programs are delivered one-on-one by a cognitive trainer during 60–90-min training sessions 3–4 days per week. The programs target seven primary cognitive skills as well as multiple sub-skills through intense and repeated engagement in mental tasks. Using a synergistic “drill for skill” approach to mastering each task, the cognitive trainers use adaptive levels of intensity, hierarchical sequencing, task loading, and dynamic feedback to move clients through the curriculum. Trainers keep every training session focused and demanding to push clients beyond their current skill levels, including adding deliberate distractions to tax the client’s focus and attention skills. The use of a metronome adds an element of multi-tasking while also increasing the intensity of the training procedures. Client workbooks outline a detailed progression of mastery over each training procedure to ensure fidelity of intervention implementation across clients.

All LearningRx clients complete ThinkRx (Gibson et al., 2003b), the 60-h basic proprietary cognitive training intervention that uses 23 different mental training procedures with more than 1,000 adaptive difficulty levels. The program is detailed in Carpenter et al. (2016), but Figure 1 illustrates an example of a training task. Memory Match is a working memory training task that also targets visual discrimination, processing speed, visual span, and sustained attention. The client and trainer sit across from each other with a workboard between them. The trainer creates a pattern of cards on his side of the board and the client recreates the pattern from memory while counting on beat to the metronome. There are nine difficulty levels and 34 variations of this training task.

At LearningRx cognitive training centers, a reading program is an optional follow-on to the ThinkRx program for clients with reading difficulties. The ReadRx (Gibson et al., 2003a) proprietary training program is an additional 60 h of an intensive sound-to-code reading and spelling intervention delivered through the cognitive training methodology. Clients receive training on auditory processing, basic code, and complex coding skills to target improved reading rate, accuracy, fluency, comprehension, spelling, and writing. The ReadRx program includes seven lessons on basic reading code such as three letter clusters and/aw/versus/au/, followed by 15 lessons on complex reading code such as the schwa, word parts, and code overlap for vowels. Auditory processing drills focus on tasks to master phoneme manipulation skills such as blending, segmenting, switching, and dropping.

Bonferroni-corrected post hoc univariate tests and pairwise comparisons on the difficulty ratings of individual academic skills revealed a similar trend. Figure 2 illustrates pre-training and post-training parent ratings for each individual academic skill.

Parent ratings of oppositional behavior decreased for both the intervention groups, and ratings increased for the control group. Figure 3 illustrates the pre-training and post-training parent ratings in oppositional behavior by group. The trend for the control group indicates a slight increase in oppositional behavior ratings by parents while the trends for both intervention groups reveal a downward trend in oppositional behavior ratings reported by parents. Bonferroni-corrected post hoc univariate tests and pairwise comparisons on the ratings of oppositional behavior revealed a similar trend but were not significant in the subsamples.

There was an overall significant difference between groups on post-test ratings of oppositional behavior [F (2, 168) = 3.4, p = 0.04, partial η² = 0.04], with a significant difference between the control group and the ThinkRx group (p = 0.03) but no significant difference between the control group and the ReadRx group (p = 0.31) or between the two intervention groups. In the subsample of participants ages 5–12, there was not an overall significant difference between groups, F (2, 125) = 1.2, p = 0.29, partial η² = 0.02. In the subsample of participants ages 13–18, there was not an overall significant difference between groups, F (2, 33) = 2.1, p = 0.13, partial η² = 0.11 on parent ratings of oppositional behavior.

Finally, to see if the results varied by age or gender, we ran linear regression analyses with age and gender as predictors of post-training changes. The results of the analysis indicated that age was not a significant predictor of change in oppositional behavior symptoms (p = 0.23). The results of the analysis indicated that gender was also not a significant predictor of change in oppositional behavior symptoms (p = 0.82).

A paired samples t-test was conducted on pre-test and post-test WJ III–COG scores for all clients who had completed a LearningRx intervention. After Bonferroni correction for seven comparisons, the results indicated statistically significant changes from pre-test to post-test with large to very large effect sizes across all cognitive skills: long-term memory [t (116) = −11.0, p < 0.001, 95% CI (−15.7, −10.9), d = 1.02], visual processing [t (116) = −8.2, p < 0.001, 95% CI (−8.6, −5.3), d = 0.76], auditory processing [t (74) = −8.2, p < 0.001, 95% CI (−12.9, −7.9), d = 0.95], reasoning [t (115) = −11.0, p < 0.001, 95% CI (−12.8, −8.9), d = 1.02], processing speed [t (87) = −5.6, p < 0.001, 95% CI (−11.4, −5.4), d = 0.60], working memory [t (113) = −7.06, p < 0.001, 95% CI (−12.6, −7.1), d = 0.66], and attention [t (116) = −13.1, p < 0.001, 95% CI (−15.3, −11.3), d = 1.21]. Figure 4 illustrates the pre-training and post-training cognitive test scores for both intervention groups.

For the subsample of participants ages 5–12, all changes from pre-test to post-test were statistically significant at p < 0.001. For the subsample of participants ages 13–18, all changes from pre-test to post-test were statistically significant at p < 0.001 except for logic and reasoning which was significant at p = 0.002.

To see if there were overall treatment effect differences between the two intervention groups on cognitive test scores gains, we ran a reliability-corrected multivariate analysis of variance (MANCOVA) on change scores using corrected pre-test values as the covariate. This method is preferred when comparing outcomes in non-equivalent group designs (Trochim, 2006). Because the groups were non-equivalent on six of the seven pre-test scores, we first corrected for pre-test measurement error using the following formula: Xadj=X¯​+r(X−X¯) where X_adj is the adjusted pre-test value, X¯is the original mean pre-test value, X is the individual pre-test score, and r is Cronbach’s Alpha reliability coefficient for each Woodcock Johnson test. Then we ran the MANCOVA using the adjusted pre-test values. There were no significant differences in cognitive test score gains between the two treatment groups (all ps > 0.05). Multiple regression on number of training hours as a predictor of cognitive test gains was not significant (p = 0.053).

For the subsample of intervention participants ages 5–12, we ran a reliability-corrected multivariate analysis of variance (MANCOVA) on change scores using corrected pre-test values as the covariate. There was no significant difference between intervention groups on any of the cognitive test measures (ps > 0.05). Multiple regression on number of training hours as a predictor of cognitive test gains was not significant (p = 0.14). For the subsample of intervention participants ages 13–18, we ran a reliability-corrected multivariate analysis of variance (MANCOVA) on change scores using corrected pre-test values as the covariate. There was no significant difference between intervention groups on any of the cognitive test measures (ps > 0.05). Multiple regression on number of training hours as a predictor of cognitive test gains was not significant (p = 0.56).