Executive Function and Planning Features of Students With Different Types of Learning Difficulties in Chinese Junior Middle School

Abstract

The aim of the present study was to investigate the executive function and planning features of students with different types of learning difficulties. Students with mathematics difficulty (MD; n = 17), reading difficulty (RD; n = 12), and their commonalities (MDRD; n = 22), along with typically academically developing peers (TD; n = 22), were evaluated on an array of cognitive measures (working memory, inhibition, and planning) individually. Results revealed significant differences among groups on various cognitive measures. Students in the MD, RD, and MDRD groups showed poorer performance compared to the TD group on all of the working memory, inhibition, and planning tasks. The MDRD group showed an overall weakness when compared to other groups, indicating severe cognitive deficits in students with MDRD. The RD group showed deficits in inhibition and planning on tasks requiring verbal skills; MD students showed deficits in inhibition and planning on digit-related tasks. However, no salient difference was found among the MD, RD, and TD groups on working memory. Results have implications for understanding the cognitive features of MD, RD, and MDRD. Intervention programs targeting inhibition and planning may be beneficial for improving reading and mathematics achievement in students with learning difficulties.

Keywords

learning difficulties executive functions planning

Students with specific learning difficulties (SLDs) are challenged with considerable difficulties in the acquisition of specific academic skills. Studies have shown that students with learning difficulties, despite having an adequate level of intelligence, demonstrate not only academic failures but also specific cognitive deficits in comparing with their typically developing peers (e.g., Geary et al., 2000; Johnson et al., 2010; Jordan & Hanich, 2000). Mathematics difficulties (MDs) and reading difficulties (RDs) are the two common subtypes of learning difficulties and often occur together as comorbidities (Badian, 1999; Barbaresi et al., 2005; Dirks et al., 2008; Gold et al., 2013). RDs usually refer to difficulties in learning to read and spell fluently and accurately (Zhao et al., 2018). Difficulties in mathematics can include a significant degree of impairment in mathematics skills such as operation, algebra, and geometry (Cai et al., 2013; D. Zhang et al., 2012).

A cognitive approach to understanding children’s learning difficulties has been adopted in this study (Dockrell & McShane, 1993). The main idea of the cognitive approach is for educators and parents to understand the importance of children’s cognitive systems in the learning process and how the cognitive systems deal with the demands that have been imposed on the children during the learning process. Thus, successful intervention programs aiming to improve children’s learning abilities can be designed based on the cognitive features identified for different types of learning difficulties. In the past, cognitive approaches have been used to identify potential sources of learning difficulties in mathematics and reading among school-age children (Geary, 2005; Kendeou et al., 2014). Students’ mathematics and reading abilities are both found to be closely related to the core cognitive function, that is, executive function (EF; Blair & Razza, 2007; Müller et al., 2008; Willoughby et al., 2019). Recent studies have begun to explore the features of EF in students with learning difficulties, but they generally focused on students with either RD only (Church et al., 2019) or MD only (Abu-Hamour, 2018; Toll et al., 2011). To our best knowledge, only a few studies have focused on the cognitive characteristics of students with different learning difficulties (Cirino et al., 2015). Comparative studies aiming at distinguishing the cognitive features of students in different subgroups of learning difficulties and subsequently developing more fine-grained interventions for each subgroup appeared to be the next step of research in this field.

Cognitive features of students with learning difficulties in China have rarely been studied. Although Chinese students tend to rank high, especially in mathematics in the Program for International Student Assessment (PISA), the proportion of students with learning difficulties in China is still quite large (Organisation for Economic Co-operation and Development, 2000). Junior middle school students in China usually participate in larger classrooms compared to other countries and have a heavier workload in academic studies, and therefore students having learning difficulties would result in the need for more attention on their cognitive development (Kritzer, 2012). Also, the negative effects of learning difficulties could be seen most prominently in junior middle school years as the demands for academic performance, including both reading and mathematics, begin to increase. Thus, the current study aimed to examine the cognitive features, especially EF, in Chinese junior middle school students with different types of learning difficulties, namely, MD, RD, and MDRD (the comorbid type), by measuring two core EF components, (i.e., working memory and inhibition), and a higher-order EF component (i.e., planning) (Diamond, 2013; Sorel & Pennequin, 2008). The results would provide a baseline for intervention research in the future as well as a piece of scientific reference to other countries in conducting learning-related research in school-age children.

As an elusive cognitive domain, EF has been argued to be an umbrella term that includes cognitive processes such as planning, sustaining, shifting, and inhibition (Barkley, 1997; Rajendran & Mitchell, 2007). EFs are necessary for formulating goals, changing plans, and resisting temptations and previously used rules, and core EFs include inhibition, working memory, and cognitive flexibility (Diamond, 2013). It has been suggested that EF is highly predictive of scholastic achievement in students (Klesczewski et al., 2018; Pearson et al., 2016; Willoughby et al., 2012) and is essential for developing domain-specific skills, especially mathematics and reading (Best et al., 2011). Studies have shown that EFs are persuasive predictors of students’ learning difficulties, especially two of the most common EF subcomponents, working memory and inhibition (Bull et al., 2008; Miciak et al., 2019; Reiter et al., 2005). However, when the learning difficulties were classified by type, different conclusions were drawn about working memory and inhibition deficits in different research. Some studies have found students with MD (e.g., Paul et al., 2019; Toll et al., 2011) and RD (Reiter et al., 2005) showed deficits in working memory, while others did not find a predictive relationship between working memory deficits and MD (e.g., Van der Sluis et al., 2005; Wang et al., 2018) or RD (Gathercole et al., 2004). In terms of inhibition deficits, some studies have found students with MD (Censabella & Noël, 2007) and RD (Reiter et al., 2005; Willcutt et al., 2005) showed deficits in inhibition, while others found no connection between inhibition deficits and MD (Peng et al., 2012) or RD (Gathercole et al., 2004). According to these previous inconsistent research conclusions, the deficits features of core EF components among students with different types of learning difficulties still need further investigation.

Within the wide spectrum of EF, planning refers to the ability to provide cognitive control and organize behaviors for achieving the desired goal (Luria, 1978), which can be seen as a higher-order EF, supported by core EFs, like working memory and inhibition, as well as processing speed (Diamond, 2013; Sorel & Pennequin, 2008). According to the PASS (planning, attention, simultaneous processing, and successive processing) theory of intelligence (Das et al., 1994), the planning system is the primary system that involves EFs responsible for controlling and organizing behavior, selecting and constructing strategies, and monitoring performance. Planning has been generally found to be closely associated with working memory (Gilhooly et al., 2005; Miller et al., 1960) and may affect particular aspects of academic performance (e.g., Cai et al., 2016; Kroesbergen et al., 2010; Locascio et al., 2010). In particular, Locascio et al.’s (2010) study has shown that students with reading comprehension deficits performed poorly in planning. Their results suggest that planning plays an important role in students’ reading performance, and students with poor planning skills may lack cognitive control, resulting in RDs. However, there is still a lack of research to confirm whether students with MD also have deficits in planning. Therefore, the profiles of planning in different subgroups of learning difficulties would need further investigation.

The current study aimed to reveal the profiles of EFs and planning among students with different types of learning difficulties (i.e., MD, RD, and MDRD) and compare the profiles with those of typically academically developing peers (TD). It is worth noting that the current study was designed to investigate the cognitive characteristics of students with RD in the Chinese context, providing further contribution to the literature as previous studies were mostly devoted to the English context. In addition, by comparing the performance of the four groups of students on an array of cognitive measures, our results could provide a comprehensive evaluation of cognitive deficits for informing the direction of intervention.

There were two specific hypotheses for this study. First, we expected weaknesses in EFs and planning for students in the MD, RD, and MDRD groups compared to TD; second, we expected students with MD, RD, and MDRD to show different profiles of EFs and planning deficits, with the MDRD group showing the most extreme difficulty among the three groups.

Method

Participants

Participants were recruited on a voluntary basis from the Grades 7–9 in a junior middle school in Shanghai, China. Subgroups of learning difficulties were identified through an initial screening procedure. In the procedure, scores of 393 students in the recent four tests (mid-terms and finals) on mathematics and Chinese (representing students’ reading scores) were collected and standardized to z scores. Discrepancy formulas for the identification of learning difficulties in the current study were as follows: students in the MD group were those with Chinese test scores ranked at the top 50%, while mathematics test scores ranked at the bottom 25%; students in the RD group were those with mathematics test scores ranked at the top 50%, while Chinese test scores ranked at the bottom 25%; students in the MDRD group were those with both mathematics and Chinese test scores ranked at the bottom 25%; and students with both mathematics and Chinese test scores ranked at the top 25% were assigned to the TD group. There was no buffer zone, and all students were classified mutually exclusively.

The Motivation Adaption Assessment Test (MAAT; Zhou, 1991) was used as a screening measure to exclude students with a learning motivation score lower than two standard deviations. In addition, to eliminate students who performed poorly on intelligence tests, the Chinese version of the Raven’s Progressive Matrices Test (H. C. Zhang & Wang, 1985) was used to assess student’s general ability, and the teacher’s daily observation reports were used to evaluate students’ in-class performance. Students with intelligence quotient (IQ) scores below 80 were excluded from the study. These screening tests were employed to allow the identification and exclusion of students who had low level of motivation and general learning ability, which might otherwise confound measures on academic achievement and cognition, as research has reported strong relationship among these variables (Duckworth et al., 2011). The exclusion of these students was crucial to maintaining the homogeneity of the sample as the sample size was small.

After screening, a total of 73 students (34 seventh graders, 26 eighth graders, and 13 ninth graders) participated in the study (see Table 1). The experimental group included 17 students with MD, 12 students with RD, and 22 students with MDRD. The TD group consisted of 22 students who had good grades in both mathematics and Chinese language. Parental consent and child assent forms were sent to the students and their families, and permission was obtained from all of the 73 students.

Table 1.

Participants in the Executive and Planning Features Study.

Grade	Mean age(months)	Group (n)				Total
Grade	Mean age(months)	MD	RD	MDRD	TD	Total
Seventh	155.13	9	7	9	9	34
Eighth	164.43	6	4	8	8	26
Nineth	178.37	2	1	5	5	13
Total	165.98	17	12	22	22	73

Note. MD = mathematics difficulty; RD = reading difficulty; MDRD = mathematics and reading difficulty; TD = typically academically developing.

Measures

Inhibition

The Stroop task and flanker task, which were widely used tasks in evaluating inhibition, were applied in the current study (Chen & Wang, 2009). The Stroop task was derived from the Stroop Color and Word Test (Luo, 1999). Chinese words “red,” “blue,” “green,” and “yellow” were printed in incongruent ink colors (red, blue, green, and yellow) and presented on a matrix of eight × five words. Students were required to name the ink color but not the literal meaning of the word (i.e., students should name “red” for the word “green” printed in red ink). Students were asked to make corrections when they made errors. The participant’s score was the total naming time. Cronbach’s alpha for the current sample was .70.

The flanker task was adapted from the work of Eriksen (1995) and was implemented on E-prime 1.1. Students were presented with a series of numbers on the screen, one at a time. The number 5 was selected as the target stimulus and another number as the interference stimulus. When the number 5 appeared right in the center of the screen, students were instructed to press button A on the keyboard of the computer quickly and accurately within 1500 ms; otherwise, they were asked to press button L. Six practice trials were given to the students for familiarization. In the formal test phase, there were 288 trials, half of them were target stimulation and half were non-target stimulation. The participant’s score was the accuracy of performance. Cronbach’s alpha for the current sample was .91.

Working memory

Working memory was assessed by four tasks, of which N-back was used to measure updating ability, and word series task, digit span forward task, and digit span backward task were used to measure working memory span (Redick & Lindsey, 2013). The N-back task was adapted from the work of Owen et al. (2005) and was implemented on E-prime 1.1. The experimental materials were visual figures represented by two solid black geometrical shapes (▲ and •) that were randomly located in a 3 × 3 matrix for 1000 ms in the center of a computer screen. Students were asked to compare the current stimulus with one stimulus back on both the geometrical shape and location (i.e., 1-back). A target appeared when both the shape and location of the current stimulus were identical to the one shown before. Students were instructed to press button A on the keyboard within 3,500 ms when the target appeared; otherwise, they were asked to press button L. Eight practice trials were given to the students for familiarization. The task consisted of 24 trials, half of them were targets and half were non-targets. The participant’s score was the accuracy of performance. The split-half reliability coefficient for the current sample was .70.

The word series task required students to repeat a sequence of single-syllable words in Chinese (e.g., pencil, shoes, bike, dog, book, duck, flower, people, and sheep) in the same order that the experimenter presented. The words were presented orally by the experimenter at a rate of one word per second, and there was no connection between each word. The task was discontinued when the students made four consecutive mistakes. The participant’s score was the sum of the correctly repeated sequences of words (max. = 27). Cronbach’s alpha for the current sample was .77.

The digit span forward task required students to repeat a sequence of digits in the same order that the experimenter presented (e.g., when the experimenter dictated 6 2 4, the student repeated them in the same sequence, i.e., 6 2 4). The strings of digits were presented orally by the experimenter at a rate of one digit per second. The strings of digits started with two digits, and one digit was added at each difficulty level, for a maximum of nine digits. There were two trials at each difficulty level, and the task was discontinued when the students failed both trials. The participant’s score was the total number of correct trials. Cronbach’s alpha for the current sample was .60.

The digit span backward task required students to repeat a sequence of digits in the reverse order that the experimenter presented (e.g., when the experimenter dictated 6 2 4, the student repeated them in the reverse sequence, i.e., 4 2 6). The strings of digits were presented orally by the experimenter at a rate of one digit per second. The strings of digits started with two digits, and one digit was added at each difficulty level for a maximum of nine digits. Similar to the digit span forward task, the task was discontinued when the students failed two trials at a given difficulty level. The participant’s score was the total number of correct trials. Cronbach’s alpha for the current sample was .98.

Planning

Planning was assessed with two tasks, the planned codes task and matching number task, derived from the Cognitive Assessment System-2 (CAS-2; Naglieri et al., 2014). CAS-2 is an operating tool of the basic cognitive process model, that is, PASS, which is widely used in research on cognitive processing.

In the planned codes task, students were asked to fill in a combination of Os and Xs in empty boxes labeled with numbers. The test consisted of six trials, each with a distinct set of codes as legend (e.g., 1 = OX, 2 = XX, 3 = OO, 4 = XO) and a set of empty boxes labeled with numbers (i.e., 1, 2, 3, and 4), arranged randomly in seven rows and eight columns on a piece of paper. For each trial, students were given 60 s to fill in the empty boxes with the Os and Xs corresponding to the legend. Students were instructed to complete the task as quickly and accurately as possible using their strategy. That is, those with better planning abilities might choose to complete all of Box 1 first (i.e., OX, regardless of the rows and columns in which Box 1 was distributed). The participant’s score was based on the average completion time of the six trials. Cronbach’s alpha for the current sample was .92.

In the matching numbers task, students were presented with three pieces of paper, one for each trial, each containing eight rows of numbers. The numbers began with two digits in the first row to nine digits in the eighth row. Students were instructed to underline a pair of identical numbers in each row (e.g., underlying 22 for the series: 18 22 25 17 33 22) as quickly and accurately as possible. The first two trials were limited to 150 s, and the last trial was limited to 180 s for completion. The participant’s score was based on the combination of the number of correct pairs and the completion time of each trial. Cronbach’s alpha for the current sample was .75.

Procedure

The data were collected at the participants’ school during the semester. All the tasks in this study were conducted by research assistants who received extensive theoretical and practical training on how to implement the tasks. The parents of all students gave their written consent in accordance with the Declaration of Helsinki. The screening and categorization of students into MD, RD, MDRD, and TD groups were based on the student’s academic performance on math and Chinese in mid-term and final exams at school. First, we sorted out the math test scores (based on equation operations and math problem-solving sections of the test) and Chinese test scores (based on reading comprehension sections of the test) of all students on their most recent four tests at school. Average scores for each of the math and Chinese tests were then computed, and the group membership of the students (i.e., MD, RD, MDRD, and TD) was determined according to the grouping criteria previously mentioned. After that, two screening tests (i.e., MAAT and Raven’s Progressive Matrices Test) were administered collectively to exclude students with low motivation levels and low IQ scores. After eligible students were identified, the inhibition, working memory, and planning tasks were administered individually in a specially prepared room. The tasks were administered in two separate sessions on the same day. Session A included all the paper-and-pencil tasks, and Session B included all the computer-based tasks that utilized the E-prime software. The tasks within each session were administered in a random order for each participant to avoid possible confounding issues due to a pre-set order, and there were sufficient rest periods between tasks. Also, the reliability scale of our sample, as reflected by Cronbach’s alpha, ranged from .60 to .98, which was considered moderate to high. In Session A, experimenters administered tasks that required a pen and testing sheets for recording, including the Stroop task, word series task, digit span forward task, digit span backward task, planned codes task, and matching numbers task, which took about 60 min in total. In Session B, experimenters administered tasks that required the use of a computer, including the flanker task and N-back task, which took about 40 min in total. Students were compensated for their time and participation with a gift (a pencil or an eraser) at the end of the experiment.

Data Analysis

In the present study, test scores of each group of students were transformed into z scores to present more intuitive comparisons of cognitive performances. Multivariate analyses of variance (MANOVAs) were run to examine group differences on each task. The within-subjects factor was students’ performance on each EF component (i.e., inhibition, working memory, and planning) and the between-subjects factor was the four groups of students (i.e., MD, RD, MDRD, and TD). Bonferroni correction was applied to adjust for multiple comparisons, and the significance level was set at p < .05.

Results

The raw scores of the inhibition, working memory, and planning tasks were z-scaled based on the entire sample of 73 students (M = 0.00, SD = 1.00). The task performance in each of the four groups was illustrated in Figure 1. The overall pattern showed that the TD group outperformed other groups in all the EFs and planning measures. The MDRD group, on the other hand, had the lowest scores in all the EFs and planning measures. The patterns of performance were different between the MD and RD groups, with the RD group appearing to perform better than the MD group on some mathematically oriented tasks such as the matching task and the digit span backward task, while the MD group seemed to perform better than the RD group on the Stroop task and the word series task. Also, some specific differences in EFs and planning were observed between groups, and a series of MANOVAs was conducted to examine the differences among groups. Group means and statistical significance for each measure were shown in Table 2.

Figure 1.

z scores for all measures by participant performance.

Table 2.

Statistics for MD, RD, MDRD, and TD Groups on Various Measures.

Measure	MD(n = 17)		RD(n = 12)		MDRD(n = 22)		TD(n = 22)		F(3, 69)	Partial η ²	Post hoc(Bonferroni)
Measure	M	SD	M	SD	M	SD	M	SD	F(3, 69)	Partial η ²	Post hoc(Bonferroni)
Inhibition
Stroop	38.27	10.97	49.61	16.80	48.76	12.83	33.00	9.23	8.30***	.27	TD > RD = MDRD, TD = MD
Flanker	0.72	0.17	0.80	0.10	0.70	0.16	0.89	0.08	8.84***	.28	TD > MD = MDRD, TD = RD
Working memory
N-back	0.67	0.17	0.64	0.21	0.43	0.23	0.78	0.11	14.34***	.38	TD = MD = RD > MDRD
Word series	21.59	3.40	20.42	3.42	19.55	4.15	23.32	2.90	4.54**	.17	TD = MD = RD > MDRD
Digit span forward	25.82	.62	25.17	1.00	24.77	1.41	26.59	0.67	4.13**	.67	TD > MDRD, TD = MD = RD
Digit span backward	3.53	1.46	4.42	1.73	2.77	1.50	4.68	1.72	5.11**	.18	TD = RD > MDRD, TD = MD
Planning
Planned codes	43.02	3.67	44.91	6.70	48.86	7.65	37.81	5.69	12.04***	.34	TD > RD = MDRD, TD = MD
Matching numbers	0.42	0.08	0.51	0.15	0.37	0.08	0.58	0.10	18.24***	.44	TD > MD = MDRD, TD = RD

Note. MD = mathematics difficulty; RD = reading difficulty; MDRD = mathematics and reading difficulty; TD = typically academically developing.

p < .05. **p < .01. ***p < .001.

Group Comparisons in Inhibition

A one-way MANOVA was conducted on scores of the Stroop task and the flanker task. Results showed a significant medium main effect on Group, Wilks’s λ = .58, F(6, 136) = 7.23, p < .001, η² = .24. Tests of between-participants effect showed that the effect of group was significant on both the Stroop task, F(3, 69) = 8.30, p < .001, η² = .27, and the flanker task, F(3, 69) = 8.84, p < .001, η² = .28. Pairwise comparisons for the Stroop task revealed that the TD group performed significantly better than the MDRD and RD groups (both ps < .01), but no significant difference was detected between the MD and TD groups. For the flanker task, the TD group performed significantly better than the MDRD and MD groups (both ps < .01), but no significant difference was detected between the MD and RD groups. Taken as a whole, MDRD students showed deficits in both inhibition tasks, while the other two groups with learning difficulties showed distinct deficits, with the RD group performing poorly on the Stroop task and the MD group performing poorly on the flanker task.

Group Comparisons in Working Memory

A one-way MANOVA was conducted on scores of the four working memory tasks, (i.e., the N-back, word series, digit span forward, and digit span backward task). Results showed a significant medium main effect on Group, Wilks’s λ = .63, F(12, 174) = 2.75, p < .01, η² = .14. Tests of the between-participants effect showed that the effect of group was significant on all of the working memory tasks (p < .01 in all tasks). Pairwise comparisons revealed a similar pattern of group differences for all the working memory tasks. That is, the TD group scored significantly higher than the MDRD group (p < .01 in all tasks), but the MD and RD groups showed no significant difference from the TD group.

Group Comparisons in Planning

A one-way MANOVA was conducted on scores of the planned codes task and the matching numbers task. Results showed a significant medium main effect on group, Wilks’s λ = .46, F(6, 136) = 10.66, p < .001, η² = .32. Tests of the between-participants effect showed that the effect of group was significant on both the planned codes task, F(3, 69) = 12.04, p < .001, η² = .34) and the matching numbers task, F(3, 69) = 18.24, p < .001, η² = .44. Pairwise comparisons for the planned codes task revealed that the TD group performed significantly better than the MDRD and RD groups (both ps < .01), but no significant difference was detected between the MD and TD groups. For the matching numbers task, the TD group performed significantly better than the MDRD and MD groups (both ps < .01), but no significant difference was detected between the MD and RD groups. Taken as a whole, MDRD students showed deficits in all the planning tasks, while the other two groups with learning difficulties showed distinct deficits, with the RD group performing poorly on the planned codes task and the MD group performing poorly on the matching numbers task.

Discussion

The primary aim of this study was to investigate the profiles of EFs and planning among students with different types of learning difficulties (i.e., MD, RD, and MDRD). In line with our expectations, the results demonstrated that the groups with learning difficulties showed general weakness compared to their typically developing peers. However, a slight variation of patterns was found among the three types of learning difficulties. That is, the MDRD group was weak in all cognitive measures, but the MD and RD groups showed selective weakness on tasks that measure inhibition and planning. The findings provided insights into the understanding of the characteristics of cognitive deficits in different types of learning difficulties, which might shape the direction of intervention and/or remedial training in the future. The study also warranted further investigation to identify differential factors such as grade, age, and emotion that might affect performance on the cognitive measures.

Students with MDRD appeared to be the weakest in all aspects of cognitive function (i.e., working memory, inhibition, and planning), which could be reflected from the mean values of scores in all of the experimental tasks. The results were consistent with Badian’s (1999) study, which found that MDRD students were more impaired in cognitive abilities when compared to students with isolated MD or isolated RD. The findings suggested that students with MDRD exhibited extensive domain-general deficits in EFs and planning that was not specific to whether the tasks required numerical or verbal processing. The result was in line with Cirino et al.’s (2015) study, which proposed that the foundational competencies for both math and language might have been compromised in students with MDRD.

Although students with MDRD showed general weakness in all EFs and planning tasks, the cognitive deficits in students with MD and RD were not that uniform. The patterns of cognitive deficits for the MD and RD groups varied with the tasks. There was a tendency that the MD and RD groups had differential deficits based on the contextual nature of the task. That is, the MD group performed poorly on digit-related tasks (i.e., the flanker task and matching numbers task), while the RD group performed poorly on word-related tasks (i.e., the Stroop task and the planned codes task), and the differentiation was prominent for inhibition and planning. Previous studies also suggested that students with MD are especially defective in numeric-related cognitive tasks (Landerl et al., 2009; Passolunghi & Siegel, 2004). According to these studies, students with mathematical difficulties have distinctive impairments in foundational numerical ability and difficulties in understanding and processing numerical magnitudes. On the other hand, our findings showed that RD students demonstrated deficits especially in the Stroop task and the planned codes task. These findings were consistent with those of previous studies suggesting that students with RD generally experienced deficits on inhibition as the tasks might be more likely to place heavy demands on students’ reading skills (Helland & Asbjornsen, 2000; Peng et al., 2013; Swanson, 1993). It was also worth noting that the RD group performed more poorly on reading-related tests and the MD group performed more poorly on digit-related tests. Such a pattern might be a reflection of literacy and numeracy for students with RD and MD, respectively. Nevertheless, a large-scale study with a wide range of age and cognitive tests would need to verify the findings.

For working memory, significant differences were found only between the MDRD and the TD groups in all the four tasks, and neither students with MD nor those with RD showed significant impairments. Although this finding is consistent with results from some previous studies (e.g., Gathercole et al., 2004; Van der Sluis, van der Leij, & de Jong, 2005), a number of other studies have found working memory impairments in students with either MD or RD (Klesczewski et al., 2018; Passolunghi & Siegel, 2004; Peng et al., 2013). One possible reason for the discrepancy in our findings might be the variation in the selection criteria for students. In the present study, the MD and RD groups were identified at the cutoff score of the 25th percentile, while some studies utilized a lower cutoff score (e.g., Landerl et al., 2004). Therefore, the present sample might have included students with less learning difficulties and attenuated the differences of working memory among the MD, RD and TD students. Another plausible reason might be that the N-back task (i.e., 1-back) was not an ideal task to assess working memory, especially the updating ability. Some studies relied on 2-back, instead of 1-back, to induce a greater working memory load for assessing updating abilities (Davidson et al., 2018; Peng et al., 2013). However, 2-back might be too demanding for students at junior middle grades, and future research could consider utilizing other updating tasks to verify the results.

Study Limitations and Future Research

There were some limitations in the present study that worth mentioning. First, the sample size under each subgroup was relatively small and might affect the generalizability of the results. Second, our criteria for identifying subgroups was not exhaustive. In this study, we defined MD and RD students by referring to their academic scores with the cutoff score at the 25th percentile. However, students’ achievements at school sometimes cannot veritably reflect actual cognitive abilities, such as reading comprehension and math problem-solving. Thus, this identification approach might weaken the strength of our results. Another limitation was that the participants in this study were students in Grades 7 through 9, which were within a narrow age range. Such a narrow age range might limit our understanding of the cognitive characteristics of adolescents with learning difficulties, and it is possible that the role of age on cognitive performance could only be evaluated if a wider age range was included. Nevertheless, the results provided a new piece of reference for a more comprehensive study in the future. Future studies may benefit from a larger sample size, a wider scope of cognitive measures, and the inclusion of longitudinal designs to assess changes of performance over time.

To summarize, this study examined the profiles of EFs and planning among students with different types of learning difficulties (i.e., MD, RD, and MDRD) and compared the profiles with those of their typically developing peers. Results demonstrated that the MDRD group appeared to have severe and extensive deficits in EFs and planning, while students with MD or RD showed only partial deficits in EF and demonstrated differential patterns in their profiles. Both students with MD and RD exhibited deficits in inhibition and planning, but students with RD performed worse in verbal tasks while those with MD showed more deficits in digital-related tasks.

Implications for Practice

Despite some limitations, our results provided implications for the identification and guidance of students with different types of learning difficulties. First of all, the current study explored the features of planning ability in students with learning difficulties, offering a broader context of assessment in relation to learning difficulties. The assessments and classification criteria applied in this study also provided an effective method for educators to identify students with SLD in class. Second, understanding the cognitive features of students with different types of learning difficulties, as well as the deficiencies in specific aspects, could help teachers draw attention to specific difficulties of the students and develop individualized remedial strategies to minimize the impact of learning difficulties on the students. For example, according to our findings, students with RD performed poorly on inhibition would benefit from practicing a reading task daily that are based on verbal materials. Teachers and parents could help students with RD to avoid impulsive reading style and encourage them to read accurately and fluently. On the other hand, for students with MD, it is important to improve their basic numerical competencies and numerical storage by teaching basic numerical cognitive skills and training rehearsal and counting skills. In addition, for students with MD, remedial practice on numerical materials, such as training on computation span, may lead to more desirable results. Given that MDRD students showed general weakness in all cognitive domains, teachers and parents should consider providing these students with the opportunity to develop comprehensive cognitive skills that include a wide range of components such as inhibition, working memory, and planning. Also, early identification of the deficits and early interventions are strongly recommended. Because of the close relationship between students’ cognitive development and academic achievement, more attention and guidance should be aimed at improving the cognitive deficits of students who have SLD. Last but not least, future studies should consider increasing the sample size to verify the findings and incorporating longitudinal designs to examine the effects of intervention with reference to the cognitive features identified in the present study.

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

This study was supported by a grant from the National Natural Science Foundation of China (Grant No. 31600906).

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