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Article

Validity of the Simplified Computerized Comprehensive Learning Ability Screening Test for the Early Detection of Learning Disabilities

by
Eun Kyoung Lee
,
Hannah Huh
,
Woo Young Kim
,
Hyunju Lee
and
Hanik Yoo
*
Seoul Brain Research Institute, Seoul 05118, Republic of Korea
*
Author to whom correspondence should be addressed.
Psychiatry Int. 2025, 6(2), 60; https://doi.org/10.3390/psychiatryint6020060
Submission received: 4 September 2024 / Revised: 11 March 2025 / Accepted: 17 April 2025 / Published: 16 May 2025
Versions Notes

Abstract

:
This study aimed to validate the usefulness of the Simplified Computerized Comprehensive Learning Ability Screening Test (SCLST) in schools and homes, to facilitate early detection and intervention for children with reading disorder (RD), math disorder (MD), or attention-deficit hyperactivity disorder (ADHD). Participants included 207 children and adolescents diagnosed with ADHD, RD, or MD and the healthy control group that was matched 1:1 by gender, age, and educational years. Higher rates of omission errors, commission errors, and standard deviation of response times were observed in the ADHD group (p < 0.001) in the SCLST-ADHD. The accuracy rates of the SCLST-RD and SCLST-MD were lower in the RD (p < 0.001) and MD group (p < 0.01), respectively. The mean response times were higher in the MD group (p < 0.001). In addition, the optimal sensitivity and specificity values were 84.6% and 88.5%, and the positive and negative predictive values were 88.0% and 85.2%, respectively, in the SCLST-ADHD. In the SCLTS-RD, the sensitivity and specificity values were 81.1% and 85.6%, and the positive and negative predictive values were 84.9% and 81.9%, respectively. In the SCLST-MD, the sensitivity and specificity values were 97.4% and 76.9%, and the positive and negative predictive values were 80.9% and 96.8%, respectively. Thus, by supporting timely assessment and intervention, this tool can support clinicians and educators in early-stage learning disabilities screening and reduce long-term psychosocial impairments.

1. Introduction

Learning disabilities broadly refer to cases in which academic achievement does not meet the expected level for children and adolescents of the same grade [1]. Learning disabilities can arise from a variety of causes and are often confusingly used with terms such as underachiever, slow learner, learning disorders (LDs), or specific learning disorders (SLDs). Underachiever refers to the individual who, despite having normal levels of intellectual development and receiving standardized education, fails to achieve an expected level of academic performance. Various factors contribute to underachievement, including SLDs such as dyslexia and dyscalculia, psychiatric conditions such as attention-deficit/hyperactivity disorder (ADHD) and depression, as well as psychosocial factors like conflicts with parents or teachers and school maladaptation [2]. In contrast, slow learner typically refers to the case in which developmental delay in overall cognitive function, such as delayed intellectual development, is the primary cause [3]. SLDs are defined as difficulties caused by inherent functional or structural issues in the central nervous system that affect fundamental cognitive processing, such as understanding or using language, or speaking. These brain functions are essential to develop basic academic skills such as reading, calculating, or writing, the impairment of which are major feature of SLDs. Generally, basic academic skills are acquired under ordinary circumstances during normal teaching–learning process. Moreover, the deficit of these skills causes the impairment of further knowledge acquisition, which is indispensable for human life [4,5]. Usually, LD is the same as SLD.
To comprehend written text, two abilities are essential: decoding, which involves recognizing written words as sounds, and reading comprehension, which refers to understanding the meaning of the text. Reading disorder (RD) can be classified into four types based on their characteristics: dyslexic, compensatory, comprehension, and mixed types [5]. The dyslexic type involves difficulties in decoding without deficits in comprehension ability. Because a significant amount of cognitive resources is involved in the decoding process, fewer resources remain available for comprehension, making it appear as though comprehension is impaired. Phonological deficits are widely recognized as the primary etiology of dyslexia, characterized by impairments in processing the sound structure of language. This deficit is related with the function of the left posterior temporal lobe [6]. The compensatory type refers to the cases in which decoding difficulties have been significantly overcome. In contrast, the comprehension type involves adequate decoding skills but poor comprehension. The mixed type occurs when both decoding and comprehension difficulties coexist [5].
Because mathematics requires more complex cognitive processes than reading, the causes of mathematical disorder (MD) are more varied and complicated than those of RD, and their manifestations are also different. Many scholars have classified MD based on their causes, but their classifications are inconsistent [6,7,8,9]. Therefore, this study adopted the classification proposed by Jung and Yoo [10], which is based on the frameworks of Geary [6] and Hale et al. [7]. Mathematical disabilities are categorized into three types: poor number sense, procedure–performance, and poor verbal ability types. Dyscalculia, a subtype of MD, involves deficits in number recognition, intuitive number sense, understanding numerical meaning, comparing the relative sizes of numbers, and mathematical reasoning. Dyscalculia is primarily linked to dysfunction in the parietal lobe, particularly the intraparietal sulcus, which plays a crucial role in numerical processing and calculation [11]. When dyscalculia co-occurs with dyslexia, individuals may also experience significant difficulties in understanding instructions, storing and retrieving numerical and mathematical information in verbal memory, and other language-related aspects of mathematics learning [12].
The prevalence of SLDs among school-age children is estimated to be approximately 5–15% across various linguistic and cultural population groups. In the United States, it has been reported that about 5–10% of children have learning disorders, with a higher prevalence among boys [13]. A previous Korean study found that 3.8% of third- and fourth-grade students in Seoul exhibited reading disabilities [14]. Another recent study using the objective and standardized assessment tools [15] found that 13% of elementary school students had learning disabilities. Among these, RD and MD accounted for 9% of the participants.
Another major cause of learning disabilities in elementary school students is ADHD [16]. ADHD is characterized by deficits in executive function, making self-regulation difficult and leading to inefficient and disorganized learning tendencies [17]. Barkley [18] proposed that deficits in behavioral inhibition are the primary cause of impulsivity and hyperactivity observed in ADHD. This often results in problems such as memory issues, distractibility, failure to complete tasks, mistakes, and avoidance of tedious or complex assignments, all of which contribute to learning difficulties [19]. In particular, the difficulties of ADHD children in reading and mathematics can extend to reduced achievement levels in adulthood [20]. According to a previous Korean study [15], 65% of children with learning disabilities had attention deficit, while 74% of children with RD or MD also had attention deficit.
Intelligence level can cause deficits in cognitive processing, memory, and language abilities. Additionally, children with intelligence impairment show limited capacity to apply and use concepts, with frequent omissions in the thought process [21]. As a result, they tend to struggle across all subjects, require more time to complete tasks, and ultimately exhibit a lower academic achievement. Many of these children experience repeated failures in learning, which, combined with criticism from teachers, peers, or parents, may lead to withdrawal or difficulties in social adaptation [22].
Learning difficulties result in significant socioeconomic costs. In Canada, an estimated CAD 638 thousand per individual is spent on treatment, educational services, healthcare, and social services. When indirect costs, such as income reduction due to learning disorders, are included, the total cost borne by individuals, families, and society amounts to approximately CAD 1.982 million [23]. In the United Kingdom, 26% of employed adults were reported to have learning difficulties, leading to reduced household income at the individual level and additional costs for repeated training at the corporate level [24]. In the United States, the employment rate for adults with LD was 1.5 times lower than that of the general adult population, and their economic activity rate and socioeconomic status were also found to be lower [25]. Therefore, early detection, intensive treatment, and appropriate educational interventions are crucial. These measures represent the most effective approach to mitigating the socioeconomic losses associated with LD.
Because the social as well as individual burden and losses of children with learning disabilities are significant, early and appropriate interventions are essential. This should be preceded by comprehensive and accurate screening testing to examine diverse causes of learning disabilities at school. Although there have been attempts to measure the basic learning status of children, mostly relying on paper-based tests or partially using computers for scoring and result analysis [26], these tests are relatively time-consuming and expensive, and are likely to produce different results depending on the examiners, raising issues of reliability. However, computerized tests can reduce time and costs, and allow to evaluate more various cognitive functions and to record reaction times more accurately and sensitively. Additionally, computerized tests have more precise control over item presentation and test execution, meaning that they are independent from the examiners, facilitating remote testing through information networks, and enabling immediate access to test results and automatic storage for later group analysis or reporting [27]. Despite these advantages, there is a lack of systematized computerized learning screening tests available at the community level such as schools or homes, especially to evaluate attention, reading, and mathematical basic learning skills comprehensively.
Therefore, this study was conducted to verify the usefulness of a comprehensive computerized comprehensive learning ability screening test (SCLST) that can be used in schools or homes. It is expected to contribute to the early identification of individual causes of learning disabilities or underachievement in children, subsequently providing early and proactive intervention.

2. Methods

2.1. Participants

From January to December 2022, a total of 207 children and adolescents diagnosed with ADHD (n = 78), RD (n = 90), or MD (n = 39) who have visited a child and adolescent psychiatric clinic in Seoul, South Korea, were recruited as the patient group. The control group matched 1:1 for sex, age, and years of education was recruited from kindergartens, elementary schools, and middle schools in Seoul and Gyeonggi-do, where there are average academic achievement levels in South Korea.

2.2. Assessment Tools

2.2.1. The Comprehensive Attention Test (CAT)

The CAT was used to evaluate the attention state and level of the study participants. The CAT is a computerized comprehensive attention test for people from aged from 4 to 50 years and consists of six subtests including the simple selective attention (visual and auditory), inhibitory sustained attention, interference selective attention, divided attention, and working memory [28].

2.2.2. The Comprehensive Learning Test-Reading (CLT-R)

To evaluate reading achievement and reading-related cognitive processing abilities, the CLT-R was used. The CLT-R is a computerized test that includes the word attack/nonword decoding test, the paragraph reading fluency test, and the nonword repetition test, the rapid automatized naming test-numbers, letters, objects, and the letter–sound matching test [29].

2.2.3. The Comprehensive Learning Test-Mathematics (CLT-M)

To assess the level of math achievement and associated cognitive function, the CLT-M was applied. The CLT-M consists of seven subtests that includes the numerical comparison test (magnitude and distance), the enumeration of dot group test, and the number line estimation test as well as the whole number computation test that measures math achievement [30].

2.2.4. The Simplified Computerized Comprehensive Learning Ability Screening Test (SCLST)

Development of the Simplified Computerized Comprehensive Learning Ability Screening Test (SCLST)

For this study, 6 key subtests from the already developed and standardized CAT, CLT-R, and CLT-M were extracted for the screening assessment of basic learning abilities. For the screening of ADHD (SCLST-ADHD), a sustained attention test to response task and a flanker test known for its large effect size in screening children with ADHD were included [31,32]. For the screening of RD (SCLST-RD), a letter–sound matching test and an orthography test were applied, evaluating phonemic awareness through the ability to differentiate between phonemes in the reading and writing domains [33]. For the screening of MD (SCLST-MD), the enumeration of dot group test related to counting strategies, which is a key predictor of MD in young children, and the numeral comparing/distance test, evaluating number sense and known to be associated with the development of future calculation skills, were applied [26]. The composition of each subtest is presented in Table 1.
In the sustained attention test to response task and the flanker test, four key indicators were computed: omission error, commission error, mean response time, and standard deviation of response time. These indicators were converted to attention quotient (AQ) scores using normative data where the average score for each gender and age group was assumed to be 100 with a standard deviation of 15. The AQ values were categorized as follows: scores at or below 76 (more than 1.6 SD below the mean) were classified as impaired, scores between 76 and 85 (1.0 SD to 1.6 SD below the mean) were considered borderline, and scores above 85 (less than 1.0 SD below the mean) were deemed normal. For the letter–sound matching and orthography awareness tests, correct response rates were reported. Similarly, for the numeral comparing/distance and enumeration of dot group tests, both correct response rates and mean response time were provided. The results for these tests are presented according to the following percentiles: excellent (85th to 100th percentile), good (76th to 84th percentile), average (25th to 75th percentile), borderline (16th to 24th percentile), and impaired (0 to 15th percentile).

Components of the Simplified Computerized Comprehensive Learning Ability Screening Test (SCLST)

(1)
Sustained Attention to Response Task
This test measures the ability to sustain attention while inhibiting impulsive responses. It evaluates whether the participant can consistently respond to all stimuli except a specific target stimulus, maintaining inhibition toward the target. The participants are instructed to press the spacebar as quickly as possible for every shape presented on the screen, except when the shape is an “X”, in which case they must refrain from pressing the spacebar.
(2)
Flanker Test (Interference Selective Attention Test)
This test assesses the ability to ignore distracting stimuli and respond selectively to relevant stimuli. Multiple visual stimuli are presented simultaneously, and the participants are required to respond based on specific features of the target stimulus. For example, when five boxes with one open side appear on the screen, the participants must press the arrow key corresponding to the direction of the opening in the central box as quickly as possible.
(3)
Letter–Sound Matching Test
This test evaluates the participant’s ability to correctly associate letters with their corresponding sounds. Participants are presented with two boxes on the screen and must select the box that matches the sound they hear. The stimuli focus on phoneme pairs that are challenging to distinguish. This test can identify cases where individuals may recognize letter names but lack precise knowledge of letter–sound correspondences required for spelling.
(4)
Orthography Awareness Test
This test identifies whether participants can recognize correctly spelled words among two options. The tasks are designed based on common student errors and presented in a sequence to avoid redundancy while covering most letters. Since the test stimuli align with those used in the Letter–Sound Matching Test, it also evaluates whether participants can distinguish matching graphemes in listening and writing domains.
(5)
Enumeration Dot Group Test
This test assesses the ability to count objects quickly and accurately. The participants are instructed to count the number of dots displayed on the screen as fast as possible. The test evaluates their speed and accuracy in quantifying objects.
(6)
Numeric Comparing Test (Distance Effect Test)
This test measures the ability to compare numerical values based on their relative distance from a reference number. The participants are asked to select the closer number as quickly and accurately as possible. The test screens for the distance effect in dyscalculia, where smaller numerical differences lead to faster and more precise comparisons.

2.3. Procedure

This study was conducted after obtaining an approval from the Institutional Review Board designated by the Institutional Bioethics Committee of Public Organizations (P01-202203-01-034). Information about the study was provided to children or adolescents and their caretakers who were going to participate in the study, and written consent and ascent were obtained. To recruit the participants for the patient group, a child and adolescent psychiatrist conducted two in-depth clinical interviews with mental status exams and assessed the physical, mental problems, and environmental conditions. In addition, the Korean version of Kiddie-Schedule for Affective Disorders and Schizophrenia (K-SADS-PL) [26] was applied to make clinical diagnoses, including ADHD, RD, or MD. Two experienced clinical psychologists conducted an intelligence assessment to all participants using the Korean-Wechsler Intelligence Scale for Children-Fourth Edition (K-WISC-IV) [34], and those with an IQ of 70 or less were excluded from the study, in consequence. Other experienced evaluators administered the CLT-R, CLT-M, and CAT one-on-one according to their suspected diagnoses: the ADHD group completed the CAT, the reading disorder (RD) group completed the CLT-R, and the mathematics disorder (MD) group completed the CLT-M. Finally, a child and adolescent psychiatrist confirmed the research participant’s diagnoses based on all clinical data and test results.
The healthy control group was matched one-to-one with the patient group, considering sex, age, and years of education. The participants with intellectual disability, neuro-logical problems such as vision, hearing impairment, or reading and math underachievement (below the 15th percentile) were excluded based on individual interviews before the assessments.

2.4. Data Analysis

The χ2-square test was used to compare differences in categorical variables between the two groups, and the t-test was used for the comparison of continuous variables. Statistical analysis was conducted using SPSS 16.0, with the significance level set at 0.05 (two-tailed). Additionally, Microsoft Excel (Microsoft Corporation, Redmond, WA, USA) and Medcalc Software 21.x (Mariakerke, Belgium) were used to calculate sensitivity, specificity, positive predictive value, and negative predictive value for the two groups.

3. Results

3.1. Demographic Characteristics

The demographic distribution of the ADHD, RD, MD, and healthy control groups are presented in Table 2. There were no differences between the patient and control groups in terms of gender, age, and years of education. The average age was 10.2 years (SD = 2.6) in the ADHD group and 10.1 years (SD = 2.7) in the control group. Both groups had 58 males (74.4%) and 20 females (25.6%). The RD and control groups had an average of 3.3 years of education (SD = 1.8), with 67 males (74.4%). The MD and control groups had an average of 3.9 years of education (SD = 2.2), with 22 males (56.4%).

3.2. Comparison of the SCLST Results Between the Patient and Healthy Control Groups

Comparing the AQ scores of the SCLST results between the ADHD and control groups, in both the sustained attention test to response task and the flanker test, higher omission errors (sustained attention test to response task: t = 4.88, p < 0.001; flanker test: t = 5.32, p < 0.001), commission errors (sustained attention test to response task: t = 4.70, p < 0.001; flanker test: t = 5.68, p < 0.001), and standard deviation of response times (sustained attention test to response task: t = 4.08, p < 0.001; flanker test: t = 10.48, p < 0.001) were observed in the ADHD group. In the flanker test, the mean response time (t = 6.80, p < 0.001) was also slower in the ADHD group. However, there was no significant difference in mean response time between groups in the sustained attention test to response task. When comparing the letter–sound matching test and the orthography test between the RD and control groups, the accuracy rates (letter–sound matching test: t = 6.92, p < 0.001; orthography test: t = 9.44, p < 0.001) were lower in the RD group. Comparing the results of the numeral comparison/distance test and the enumeration of dot group test between the MD and control groups, the accuracy rates were lower (numeral comparison/distance test: t = 3.29, p < 0.01; enumeration of dot group test: t = −3.01, p < 0.01), and the mean response times (numeral comparison/distance test: t = −3.95, p < 0.001; enumeration of dot group test: t = −5.50, p < 0.001) were slower in the MD group (Table 3).

3.3. Sensitivity and Specificity of the SCLST

We calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) according to the number of ‘borderline’ findings in the SCLST subtests (Table 4).
In the SCLST-ADHD, the results of response time in the sustained attention test to response task were excluded because they were not different between the groups. When there were more than three ‘borderline’ findings, the number of ‘borderline’ findings did not affect the sensitivity and specificity values. As the number of ‘borderline’ findings for negative results increased, the sensitivity decreased. Thus, in case all the variables with ‘normal’ findings were considered as negative, sensitivity and specificity were 84.6% and 88.5%, and the PPV and NPV were 88.0% and 85.2%, respectively. In the SCLTS-RD, when there were two ‘borderline’ findings or more, the sensitivity and specificity values were same regardless of the number of ‘borderline’ findings. When considering one ‘borderline’ finding as negative, sensitivity and specificity were 84.6% and 88.5%, and the PPV and NPV were 81.1% and 85.6%, respectively. In the SCLST-MD, when there was one ‘borderline’ finding or more, the sensitivity and specificity values remained the same regardless of the number of ‘borderline’ findings. In the case of considering one ‘borderline’ finding as negative, sensitivity and specificity were 97.4% and 76.9%, and the PPV and NPV were 80.9% and 96.8%, respectively (Table 4).

4. Discussion

Learning disabilities cause not only individual difficulties but also social–economic losses. Le LK-D (2021) [35] conducted a systematic review of cost-effectiveness studies on the prevention of mental disorders and the promotion of mental health and well-being, focusing on research published between 2008 and 2020. The review concluded that targeted prevention was likely to be more cost-effective than universal prevention. Among children and adolescents, school-based screening combined with psychological interventions proved to be the most cost-effective strategy for preventing mental disorders. Therefore, it is crucial to detect these conditions early and to provide with an effective approach and education.
This study was conducted to verify the usefulness of the SCLST for screening learning disabilities that are related with deficits of attention or basic academic skills such as reading and calculation in communities. The results show that it is possible to screen ADHD with a maximum sensitivity of 0.85, specificity of 0.89, PPV of 0.88, and NPV of 0.85, compared to previous studies that reported the sensitivity for ADHD diagnosis through various neuropsychological tests ranged between 0.49 and 0.70 and the specificity ranged between 0.29 and 0.69 [36]. Considering that the Gordon Diagnostic System, widely used for ADHD diagnosis, showed a PPV of 0.91 and an NPV of 0.67 [37], this represented a higher discriminative ability with a lower NPV than the SCLST-ADHD. However, the Gordon Diagnostic System takes 26 min to apply [38], which is longer than 10 min for the SCLST-ADHD. Therefore, the SCLST-ADHD can demonstrate at least equivalent validity and more usefulness compared to other previous attention tests.
The SCLST-RD screened RD with a maximum sensitivity of 0.81 and specificity of 0.86, and PPV and NPV of 0.85 and 0.82, respectively. Previous research utilizing the manual Ujian Pengesanan Awal Disleksia Bahasa Melayu, which includes four subtests (rapid naming, one-minute reading, two-minute spelling, and pseudowords), showed both sensitivity and specificity of 1.00 for RD diagnosis. This high accuracy was achieved using the computerized analysis systems like fuzzy logic and WEKA in addition to the tests, as demonstrated in a study conducted in 2020 [39]. Because the high figures improved after the data filtering and reanalysis process after excluding nine slow learners and four dyslexic students who showed significant improvement after special education, it is hard to generalize it. The Dyslexia Adult Checklist, available online, was evaluated in a 2023 study and found to have sensitivity range of 0.76–0.92 and specificity range of 0.80–0.88 for RD diagnosis. However, this tool was designed for adults and it relied on self-reported data rather than the direct assessment of reading skills [40]. In addition, in a study conducted in 2023, although the Dynamic Indicators of Basic Early Literacy Skills Next is a brief test of about 5 min and has a sensitivity of 0.90 and specificity of 0.71, it is not for RD diagnosis but rather for assessing phonological awareness [41]. Similarly, although the writing tasks from the collective version of the Cognitive–Linguistic Protocol and the Non-Literacy-Based Alternative Tools for Educators achieved a sensitivity of 1.00 and a specificity of 0.90, they are also not for diagnosing RD but for assessing the literacy ability of fifth graders of elementary school [42]. Considering these previous studies, the SCLST-RD is expected to be at least equally useful and more applicable compared to other reading ability assessments.
For MD, the SCLST-MD showed a maximum sensitivity of 0.97, specificity of 0.77, PPV of 0.81, and NPV of 0.97. In previous studies, the DyscalculiUM showed both sensitivity and specificity of 1.00 when comparing dyscalculia with control groups. However, as this high performance was attributed to the iterative refinement of the test until optimal selection among small groups of 10 participants had been achieved, it is challenging to generalize the results. In addition, the administration of the test takes up to 48 min [2], which is too lengthy to use for screening only MD. The Arabic version of the Test of Mathematical Abilities—Third Edition showed a sensitivity of 0.97 and a specificity of 1.00 for MD diagnosis, but it requires 60–90 min to complete, as demonstrated in a study conducted in 2020 [43]. Considering these aspects, the SCLST-MD can demonstrate at least equivalent usefulness in screening mathematical problems compared to other math tests, while requiring a shorter amount of time for administration.
Previous researchers have also tried to examine learning disabilities comprehensively. For instance, the Screening Test for the Luria-Nebraska Neuropsychological Battery-Children’s Revision had a classification accuracy of 97.5% between groups with and without learning disabilities. However, it is available only offline by trained professionals or paraprofessionals [44]. Vidyadharan and colleagues developed a questionnaire that can be quickly administered by parents or teachers for approximately 25 min and demonstrated a sensitivity of 84.7% and specificity of 100% for learning disabilities compared to normal students. However, the sensitivity decreased to 24.5% when control groups displayed borderline intelligence or were poor achievers without learning disabilities. Moreover, it did not directly measure the child’s neurocognitive abilities [45]. In a study conducted in 2020, The Child and Adolescent Intellectual Disability Screening Questionnaire, an online screening tool, identified 90.4% of individuals with learning disabilities. However, diagnoses were based on solely self-reports without further verification, and no specificity was reported [46].

5. Conclusions

In this study, the SCLST’s usefulness as a screening tool for children and adolescents with learning disabilities was suggested. The SCLST can offer a comprehensive and integrative examination of basic learning abilities across attention, reading, and mathematics. The application of this test can be time-efficient and objective because the entire procedure from the administration and scoring to result analysis is computerized, allowing for standardized execution even by non-experts. Therefore, it is easily accessible for use in schools or homes without the need for a clinic visit.
Additionally, it also ensures a reliable experimental group through clinician-based diagnoses. When the patient group was selected, it was based not solely on academic achievement or objective test results but on a clinician’s comprehensive diagnosis, allowing for a more accurate evaluation of the sensitivity and specificity of the screening test. This approach suggests that the screening test may be not only valid in a research context but also practically useful in real clinical settings. Also, unlike self-reported measures of abilities and skills, the test derives results from direct assessments, enhancing the validity of the screening outcomes. Thus, it can contribute to the early detection and proactive correction of individual causes of learning disabilities.
However, this study has limitations as follows: First, the healthy control group was not assessed by a well-trained expert who can diagnose the psychiatrically healthy condition of the participant. Second, since the data for the patient group were obtained from a single clinic, there might be a lack of representativeness and limitations in generalizing the findings to other geographic areas and ethnicities, and it is necessary to expand the subject group in the future. Third, the small sample size of the MD group may have limited the statistical power to detect significant effects. Fourth, the limitation of a computerized test includes unfamiliarity with and low accessibility to a computer or a lack of flexibility to different or unexpecting test situations. Further research is needed to understand whether the SCLST can also be used to verify the effects of interventions or treatments, in addition to screening children and adolescents for learning disabilities.

Author Contributions

Conceptualization, H.Y.; Data Curation, H.H., H.L. and E.K.L.; Formal Analysis, H.H. and E.K.L.; Investigation, H.H., W.Y.K., H.L. and E.K.L.; Methodology, H.Y.; Project Administration: H.Y.; Resources: H.Y.; Software: H.Y.; Supervision, H.Y.; Validation, H.H. and E.K.L.; Visualization, E.K.L.; Writing—Original Draft Preparation, E.K.L.; Writing—Review and Editing, H.Y. and E.K.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The Institutional Review Board designated by the Institutional Bioethics Committee of Public Organizations (P01-202203-01-034).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author due to privacy.

Conflicts of Interest

H.H. is the employee of the company that is producing the CAT and CLT. The other authors declare no conflicts of interest.

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Table 1. Subtests of the simplified computerized comprehensive learning ability screening test.
Table 1. Subtests of the simplified computerized comprehensive learning ability screening test.
SubtestsTotal DurationStimulus IntervalNumber of Stimuli
SCLST-ADHDSustained attention test to response task 10′2″300
Flanker 5′2″150
SCLST-RDLetter–sound matching5″40
Orthography awareness 3′20″40
SCLST-MDNumerical comparison/distance 2′5′20
Enumeration of dot group 2′NA20
Time is expressed in minutes (′) and seconds (″). SCLST: simplified computerized comprehensive learning ability screening test, ADHD: attention-deficit hyperactivity disorder, RD: reading disorder, MD: mathematical disorder.
Table 2. Demographic characteristics of the study participants.
Table 2. Demographic characteristics of the study participants.
ADHD (n = 78)Healthy Control (n = 78)t or χ2p-Value
Age, mean (SD)10.2 (2.6)10.1 (2.7)0.100.918
Gender
Boys, n (%)58 (74.4)58 (74.4)0.001.000
RD (n = 90)Healthy control (n = 90)t or χ2p-value
Educational year, mean (SD)3.3 (1.8)3.3 (1.8)1.000.000
Boys, n (%)67 (74.4)67 (74.4)0.001.000
MD (n = 39)Healthy control (n = 39)t or χ2p-value
Educational year, mean (SD)3.9 (2.2)3.9 (2.2)1.000.000
Boys, n (%)22 (56.4)22 (56.4)0.001.000
ADHD: attention-deficit hyperactivity disorder, RD: reading disorder, MD: mathematical disorder, SD: standard deviation.
Table 3. Comparison of the attention quotients of the simplified computerized comprehensive learning ability screening test between the patient and healthy control groups.
Table 3. Comparison of the attention quotients of the simplified computerized comprehensive learning ability screening test between the patient and healthy control groups.
Mean (SD)Mean (SD)tp
SCLST-ADHDADHDHealthy Control
Sustained attention test
Omission error89.7 (23.6)103.0 (4.0)4.880.000
Commission error96.7 (20.3)108.9 (10.6)4.700.000
Response time89.5 (16.2)94.2 (17.1)1.770.079
Response time SD93.9 (15.8)103.3 (12.8)4.080.000
Flanker test
Omission error83.4 (34.7)104.5 (5.4)5.320.000
Commission error89.2 (26.4)107.2 (9.0)5.680.000
Response time72.5 (23.0)94.4 (17.0)6.800.000
Response time SD65.4 (28.8)101.6 (10.0)10.480.000
SCLST-RDRDHeathy control
Letter–sound matching
Correct response74.0 (14.8)85.6 (5.5)6.920.000
Orthography awareness
Correct response73.5 (17.3)91.9 (6.7)9.440.000
SCLST-MDMDHealthy control
Numeral comparison/distance
Correct response79.5 (18.6)89.7 (5.8)3.290.002
Response time2504.4 (521.9)2071.2 (444.8)−3.950.000
Enumeration of dot group
Correct response89.7 (17.9)98.5 (2.6)3.010.004
Response time2902.1 (681.8)2137.5 (538.2)−5.500.000
SCLST: simplified computerized comprehensive learning ability screening test, ADHD: attention-deficit hyperactivity disorder, RD: reading disorder, MD: mathematical disorder, SD: standard deviation.
Table 4. Sensitivity and specificity of the simplified computerized comprehensive learning ability screening test.
Table 4. Sensitivity and specificity of the simplified computerized comprehensive learning ability screening test.
‘Borderline’ Results for (−) Diagnosis, nADHD (n = 78)Healthy Control (n = 78)Sensitivity (%)Specificity (%)PPV (%)NPV (%)
SCLST-ADHD (+) diagnosis, n 0758184.688.588.085.2
SCLST-ADHD (+) diagnosis, n1589869.294.993.175.5
SCLST-ADHD (+) diagnosis, n2589869.294.993.175.5
SCLST-ADHD (+) diagnosis, n3579968.094.993.074.8
RD (n = 90)Healthy control (n = 90)
SCLST-RD
(+) diagnosis, n		01186288.957.867.883.9
SCLST-RD
(+) diagnosis, n		1869481.185.684.981.9
SCLST-RD
(+) diagnosis, n		2819977.887.886.479.8
MD (n = 39)Healthy control (n = 39)
SCLST-MD
(+) diagnosis, n		0562210056.469.6100
SCLST-MD
(+) diagnosis, n		1473197.476.980.996.8
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Lee, E.K.; Huh, H.; Kim, W.Y.; Lee, H.; Yoo, H. Validity of the Simplified Computerized Comprehensive Learning Ability Screening Test for the Early Detection of Learning Disabilities. Psychiatry Int. 2025, 6, 60. https://doi.org/10.3390/psychiatryint6020060

AMA Style

Lee EK, Huh H, Kim WY, Lee H, Yoo H. Validity of the Simplified Computerized Comprehensive Learning Ability Screening Test for the Early Detection of Learning Disabilities. Psychiatry International. 2025; 6(2):60. https://doi.org/10.3390/psychiatryint6020060

Chicago/Turabian Style

Lee, Eun Kyoung, Hannah Huh, Woo Young Kim, Hyunju Lee, and Hanik Yoo. 2025. "Validity of the Simplified Computerized Comprehensive Learning Ability Screening Test for the Early Detection of Learning Disabilities" Psychiatry International 6, no. 2: 60. https://doi.org/10.3390/psychiatryint6020060

APA Style

Lee, E. K., Huh, H., Kim, W. Y., Lee, H., & Yoo, H. (2025). Validity of the Simplified Computerized Comprehensive Learning Ability Screening Test for the Early Detection of Learning Disabilities. Psychiatry International, 6(2), 60. https://doi.org/10.3390/psychiatryint6020060

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