A meta-analysis of the effects of academic interventions on academic and academic anxiety outcomes in secondary students.

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Published in final edited form as: J Sch Psychol. 2025 Mar 28;110:101433. doi: 10.1016/j.jsp.2025.101433 A Meta-Analysis of the Effects of Academic Interventions on Academic and Academic Anxiety Outcomes in Secondary Students Sarah Fishstrom 1 , Philip Capin 3 , Bethany H Bhat 2 , Katlynn Dahl-Leonard 4 , Blair Payne 2 , Hsuan-Hui Wang 6 , Jordan Dille 5 , Sharon Vaughn 2 Author information Article notes Copyright and License information 1 University of Hawaii at Manoa, USA 2 The Meadows Center for Preventing Educational Risk, The University of Texas at Austin, USA 3 Harvard Graduate School of Education, USA 4 University of Virginia, USA 5 University of Nebraska at Kearney, USA 6 National Taiwan Normal University, Taiwan ✉ Correspondence concerning this article should be addressed to Sarah Fishstrom, College of Education, University of Hawai’i at Manoa, 1776 University Avenue, Wist 121, Honolulu, HI 96822, USA. sarahcf@hawaii.edu . Issue date 2025 Jun. PMC Copyright notice PMCID: PMC12163233  NIHMSID: NIHMS2070395  PMID: 40506165 The publisher's version of this article is available at J Sch Psychol Abstract The purpose of this meta-analysis was to examine the effects of academic interventions on academic achievement and academic anxiety outcomes among secondary students. A systematic search yielded 19 studies comprising 2,377 participants from research conducted between 1990 and 2020. Results revealed statistically significant differences favoring academic treatments over the control on academic outcomes ( g = 0.66, SE = 0.17) but no statistically significant benefits for academic anxiety outcomes ( g = −0.13, SE = 0.11). Moderator analysis revealed that the domain focus of the intervention (i.e., math, literacy, or science) did not explain the variance in student outcomes in either achievement or anxiety. The findings from this study corroborate previous research with elementary students, which found that academic interventions improve academic outcomes but do not substantially reduce academic anxiety. These findings suggested that academic anxiety may need to be addressed directly. However, the authors caution against drawing strong conclusions due to the limited research in this area. Keywords: academic intervention, school-based, academic anxiety, academic outcomes, anxiety outcomes, meta-analysis Anxiety disorders are a very common—perhaps the most common—mental health condition affecting youth ( National Institute of Mental Health ). Typically, anxiety disorders begin in childhood or adolescence and persist well into adulthood ( Woodward & Fergusson, 2001 ). Estimates of their prevalence vary widely ( Cartwright-Hatton et al., 2006 ), with most estimates stating that between 7% and 20% of adolescents between the ages of 13 and 18 have anxiety disorders (e.g., the National Institute of Mental Health estimates that 8.3% of adolescents are severely affected by anxiety; Ghandour et al., 2019 ). Anxiety disorders refer to a group of related mental health disorders characterized by fear and anxiety that vary from what is considered developmentally appropriate because they are excessive or continue beyond developmentally appropriate time periods. Fear refers to emotional responses to imminent threats, which may be real or perceived, and is often associated with involuntary arousal, such as accelerated heart rate, sweating, and shortness of breath ( American Psychiatric Association, 2013 ). Anxiety is the anticipation of future threat and is often associated with muscle tension and excessive caution or avoidant behaviors ( American Psychiatric Association, 2013 ). The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013 ) identifies 11 types of anxiety disorders (e.g., selective mutism, separation anxiety, specific phobia, social anxiety disorder, panic disorder, agoraphobia, generalized anxiety disorder) that differ based on the types of situations or stimuli that induce fear and anxiety. Like many other internalizing and externalizing disorders, anxiety is best understood as a dimensional disorder that occurs on a continuum from minimal to mild psychopathology in healthy individuals, progressing to subclinical presentations for individuals with mild to moderate symptoms, and ultimately extending to clinical levels that meet diagnostic criteria ( National Institute of Mental Health, 2024 ) and that can negatively influence development ( Lenaert et al., 2014 ; Ohannessian et al., 1999 ). Anxiety is often considered to have trait and state characteristics. Trait anxiety is typified by stable levels of arousal across situations; state anxiety involves variable levels of arousal that are experienced in specific situations ( Owens et al., 2008 ). Academic anxiety is a state-specific type of anxiety in which students experience cognitive, physiological, and behavioral responses to educational contexts ( Cassady, 2010 ). Although not recognized as a type of anxiety by the DSM-5, academic anxiety is well-established in the research literature, particularly math anxiety and test anxiety ( Cassady, 2010 ). Maladaptive levels of academic anxiety can, for instance, inhibit children’s working memory (i.e., the ability to receive, process, and retrieve information), which can negatively impact children’s ability to perform well. In addition to working memory difficulties, symptoms of academic anxiety include attention difficulties, oversensitivity, withdrawal, emotional dysregulation, and task avoidance ( Huberty, 2009 ; Killu et al., 2016 ), all of which can directly impede a student’s progress in, and experience with, school. For secondary-aged students, whose school experiences are more closely tied to real-world outcomes (e.g., graduating, obtaining a job, completing postsecondary education), maladaptive levels of academic anxiety may inhibit their attainment of postsecondary educational and professional goals. Prior Academic Anxiety Research Although a small level of anxiety may be associated with enhanced academic achievement ( Shih & Lin, 2017 ), excessive levels of anxiety have been shown to be maladaptive and associated with academic underachievement ( Bryan et al., 2004 ; Ohannessian et al., 1999 ). Zhang and colleagues (2019) reported meta-analytic findings showing a negative effect size ( r = −0.32) between math performance and math anxiety. Corroborating these findings, Namkung and colleagues (2019) also conducted a meta-analysis of math performance and math anxiety, which yielded a similar negative effect size ( r = −0.32). Two years later, Barroso and colleagues (2021) used broader inclusion criteria (i.e., resulting in more effects), and their meta-analysis yielded a similar overall correlation between math performance and math anxiety ( r = −0.28). The consistency in the main findings across these three studies corroborates the nature of the relationship between math anxiety and math achievement. In 2018, von der Embse and colleagues conducted a meta-analytic review of 30 years of test anxiety research, demonstrating that test anxiety was negatively related to academic performance (including academic outcomes at the classroom, school, and state levels). To our knowledge, two meta-analyses have been conducted on the relation between reading achievement and anxiety. Francis and colleagues (2019) meta-analyzed data comparing poor and typical readers on measures of anxiety and found that poor readers demonstrated, on average, higher levels of anxiety ( d = .41). Johnson and colleagues (2024) conducted a meta-analysis to examine the relation between reading achievement and reading anxiety. Their search yielded 44 studies and 113 effect sizes, including any age range of individuals, and found a moderate negative correlation between reading achievement and reading anxiety ( r = −0.31), which was not moderated by the age of the participant. Previous researchers have sought to understand the causal link between anxiety and academic achievement. One hypothesis is that a bidirectional relation exists, such that students with elevated academic anxiety have difficulty engaging in academic tasks and students with academic difficulties experience anxiety ( Grills-Taquechel et al., 2012 ; Livingston et al., 2018 ; Ramirez et al., 2019 ). Longitudinal data showing the relations from math anxiety to math performance and from math performance to math anxiety were similar in strength, providing some evidence in support of this hypothesis ( Namkung et al., 2019 ). Another model suggests that anxiety primarily influences achievement (based on the cognitive interference theory; Carey et al., 2016 ; Peng & Fuchs, 2016 ). The cognitive interference theory suggests that students who have anxiety are preoccupied and have limited cognitive resources to dedicate to academic tasks ( Carey et al., 2016 ; Peng & Fuchs, 2016 ). Prior research found that interventions targeting reduced test anxiety led to reduced test anxiety and improved academic test performance ( von der Embse et al., 2013 ). Finally, academic anxiety is proposed as primarily a byproduct of repeated academic difficulties, often accompanied by social or environmental signals that performance is below average. This hypothesis is grounded in academic self-worth theories, which suggest that academic difficulties threaten students’ sense of self-worth, leading to academic anxiety (e.g., Lawrence & Williams, 2013 ). One way to examine the extent to which academic difficulties lead to anxiety is to investigate the extent to which academic interventions have led to improvement in academic performance and anxiety outcomes. Fishstrom and colleagues (2022) reported findings from a meta-analysis examining the effects of academic interventions for children in the elementary grades (i.e., kindergarten to grade 5 or ages 5 to 11) on academic and anxiety outcomes. The findings revealed that academic interventions led to statistically significant, moderately large effects on academic skills ( g = 0.63) but no significant reductions on academic anxiety ( g = −0.06). If poor achievement were the primary cause of academic anxiety, as suggested by the third hypothesis, we may have expected improvements in academic achievement to be associated with reduction in anxiety. It is also possible that academic anxiety may be an issue in and of itself or that there are other explanations for insignificant reductions in academic anxiety, such as measurement issues (e.g., not enough time to see reduction in academic anxiety). However, it is important to note that this study focused on children in the elementary grades, and students in the secondary grades have different pressures, such as personal postsecondary goals, which may differently impact the relationship. For example, if a student has postsecondary goals that require a certain score or level of academic achievement, it may be that even some improvement in academic achievement is not enough to result in substantial impacts to their academic anxiety. Put differently, pressures surrounding academic achievement at the elementary and secondary levels are different and worth considering. Academic Anxiety in Secondary Students The magnitude of the relation between academic anxiety and achievement has been shown to vary across development. For example, Hill et al. (2016) found that math anxiety was robustly and reliably associated with secondary students’ math performance; however, no stable relation was found for elementary students. Their findings and the results of other studies (e.g., rise in test anxiety between grades 3 and 5; Hembree, 1988 ) suggested that the relation between academic anxiety and performance strengthens over time. Perhaps anxiety and achievement are more closely related for older students as learning becomes more demanding and students begin to think about their postsecondary prospects. Additionally, adolescence includes new emotional and social influences on students both in and outside of school settings, which may further influence this relation. Or perhaps the relation may strengthen with age as the number of adverse learning experiences accumulate. Barroso and colleagues’ (2021) moderator analyses suggested a nonlinear relation between math anxiety and achievement across development, but this analysis corroborated findings of a negative relation between academic achievement and anxiety in secondary students. Current Gaps in the Research Content Areas Considering that three meta-analyses have been published in the last 5 years addressing the relation between math achievement and math anxiety, with the most recent meta-analysis compiling accumulated research from 372 studies of K–12 students, it is clear that math anxiety and math achievement have the attention of researchers, policymakers, and practitioners ( Barroso et al., 2021 ; Namkung et al., 2019 ; Zhang et al., 2019 ). Yet, academic anxiety is much more comprehensive ( Cassady, 2010 ), and there is a dearth of research in other content areas (i.e., science or reading). For example, science anxiety has been identified as different but related to other types of academic anxiety ( Mallow, 2006 ). Science anxiety is common, interferes with cognitive processing, and can lead to students avoiding science-related activities ( Britner, 2010 ); yet, limited research has examined science anxiety’s impacts on academic outcomes. Reading anxiety, which has a limited but quickly growing body of research, has been identified as unique, yet related to math and test anxiety (Fishstrom et al., 2023; Jalongo & Hirsh, 2010 ; Piccolo et al., 2017 ), with a negative impact on reading achievement. The same is true for foreign language anxiety ( Young, 1991 ), which has been found to be a unique form of anxiety specific to second-language contexts. There are limited meta-analyses that specifically examine secondary students because of the scarcity of available research that specifically targets secondary students with learning difficulties (e.g., Jitendra et al., 2018 ; Payne & Yoon, 2024 ; Scammacca et al., 2015 ), let alone academic interventions that consider the specific challenges students face at that age, such as anxiety. Therefore, we sought to provide a comprehensive review of the research related to academic achievement with secondary students for initial exploration into whether academic interventions influence academic anxiety and for investigation into whether different content areas influence outcomes. We wondered whether the domain with the most research (math) was associated with reductions in anxiety to a greater extent than other domains (e.g., reading or science) due to the prevalence of research in this area. Combined Interventions Codding and colleagues (2023) sought to examine the difference between the effects of two types of interventions, academic-based (i.e., skill-based) and therapeutic interventions, on math achievement and math anxiety outcomes. Their search yielded 17 studies (two studies are also included in our corpus; Orabuchi, 2013 ; Wei, 2010 ) including school-age children (K–12). Results revealed that the skill-based interventions improved math performance better than the therapeutic interventions and that therapeutic interventions improved math anxiety better than skill-based interventions, as might be anticipated. However, when accounting for study quality, they found that high-quality skill-based studies resulted in higher math performance and reduced anxiety symptoms. Therefore, Codding and colleagues (2023) concluded that math skill building can help to lessen math anxiety, but because therapeutic interventions had a larger impact on anxiety outcomes and did not influence math outcomes, students with higher anxiety symptoms may need a combined intervention approach ( Barroso et al., 2021 ; Namkung et al., 2019 ). Due to this speculation that a combined intervention approach may be beneficial when addressing academic anxiety, we had hoped to compare combined intervention effects to solely academic intervention effects, but this moderator analysis was not possible due to a limited number of studies that met inclusion criteria and that used an integrated approach. Current Study This meta-analysis sought to expand understanding of the impact of academic interventions on academic anxiety and academic achievement outcomes among secondary students. A primary purpose of the meta-analysis was to further determine the extent to which academic interventions can address academic achievement. Given the prevalence of anxiety in adolescence ( National Center for Education Statistics, 2022 ), including academic anxiety, and the dearth of school-based anxiety interventions ( Tutsch et al., 2019 ), it is also important to understand the impact of academic interventions on academic anxiety. This study filled a gap in the extant literature, as no meta-analysis to date had comprehensively investigated the effects of school-based academic interventions on academic achievement and academic anxiety outcomes among secondary students (only solely in the math domain; Codding et al., 2023 ). We focused on this age group because anxiety and achievement have been found to be associated for all ages and affect students at the secondary level ( Barroso et al., 2021 ). Considering that most of the research related to academic anxiety has focused on math interventions ( Barroso et al., 2021 ; Codding et al., 2023 ; Namkung et al., 2019 ; Zhang et al., 2019 ), we sought to combine academic interventions to conduct a comprehensive review and use content area as a moderator to help interpret findings. Recent investigations have suggested that academic interventions with an anxiety management component may be a useful approach ( Codding et al., 2023 ), so we intended to review intervention type (academic only or academic plus anxiety management practices) as an additional moderator, but there were not enough effects to be able to do so. With these considerations in mind, the current study was designed to answer the following questions, targeting secondary students: What are the features of studies examining the effects of school-based academic interventions on academic performance and academic anxiety outcomes? What are the effects of school-based academic interventions on academic performance and academic anxiety outcomes? To what extent are intervention effects moderated by the domain of the intervention (i.e., content area) and intervention type (i.e., academic intervention only or academic intervention and anxiety management)? Related to our second research question and in line with prior research ( Fishstrom et al., 2022 ), we hypothesized that the effects would be larger on academic outcomes than anxiety outcomes. However, consistent with findings from Ma and Xu (2004) , we expected to see small reductions in anxiety because students may have decreased anxiety if academic achievement improves. Related to our third question, we wondered if math interventions might have a larger positive impact on math anxiety than other content areas because of the preexisting research in this area. We also hypothesized that academic plus anxiety management practices would have a positive influence on both academic and anxiety outcomes, but we were not able to examine these effects as there was an inadequate number of effects that included embedded anxiety management practices. Method Search Procedures The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA; Liberati et al., 2009 ) guided this review. In order to locate all relevant studies and conduct a comprehensive review of the evidence base, published and unpublished studies were searched through June 2021; a start date was not specified. The following electronic databases were searched through EBSCOhost: Education Source, ERIC, PsycINFO, CINAHL Plus, and MEDLINE. Dissertations were searched through ProQuest Dissertations and Theses. Search terms were organized by category: location ( school OR classroom OR student OR teacher ), AND intervention ( intervention OR treatment OR program* OR curricul* ), AND anxiety ( anxi *), AND academic outcomes [(( reading OR math * OR academic ) n2 ( achievement OR outcomes OR performance OR success OR skills ))], AND experimental study or design ( study OR experiment* OR “clinical trial” OR randomized OR “randomly assigned” OR “assigned at random” OR “quasi-experimental” ). N2 is a proximity operator term that was used to specify that there could be only two words between terms in that Boolean string, regardless of the order they appear (e.g., reading achievement , reading comprehension achievement , and achievement in reading would all satisfy the academic outcomes search term). The database search returned 8,351 records. After duplicates were removed, 5,883 records required abstract and title screening. Inclusion and Exclusion Criteria We included studies that met the following criteria: Published in a peer-reviewed journal or an unpublished dissertation printed in English through June 2021 Employed an experimental or quasi-experimental design with a treatment and a comparison to determine the experimental effect Selected participants in secondary school (6th to 12th grades, depending on the school system) and included both typical learners and learners with academic or emotional disabilities Examined an academic intervention related to an academic skill or content area, instructional method, or instructional tool Assessed both academic achievement outcomes and academic anxiety outcomes Included sufficient information to calculate effect sizes During the screening process, source articles were examined against operational definitions. Articles were included if they examined an academic intervention as the independent variable and both academic achievement and academic anxiety outcomes as the dependent variables. Academic interventions were defined as instruction in an academic content area (e.g., reading comprehension instruction in English language arts) with a primary objective to enhance the understanding of academic content knowledge. In other words, interventions had to comprise academic activities and have the explicit anticipation of enhanced academic knowledge (this definition aligns with academic intervention research; Fishstrom et al., 2022 ; Dietrichson et al., 2020 ). This screening process excluded interventions solely focused on generalized strategy instruction (e.g., cognitive behavior therapy, meta-cognitive training, time management, test-taking skills, mindset instruction, relaxation techniques, stereotype threat education, cognition strategies, goal setting, learning strategies). However, if a study included an intervention with instruction aimed at improving content knowledge and embedded generalized strategy instruction, the study qualified. We did not include studies where content knowledge was not the goal of the study (e.g., when the intervention was focused on self-monitoring and positive self-talk; Weintraub, 1995 ). See Figure 1 for further information on excluded studies. Figure 1. PRISMA Diagram. Open in a new tab Note . Among the 20 studies included, 19 studies contributed enough information to be included in the quantitative Academic anxiety was operationalized as a broad construct that includes anxieties connected to typical academic situations and activities ( Cassady et al., 2019 ) and that includes both context-specific academic anxiety (e.g., test anxiety) and content-area-specific anxiety (e.g., reading anxiety and math anxiety). Coders reviewed the anxiety measures to ensure that the questions or items to be rated were specific to academic anxieties (e.g., I avoid writing; I am nervous when doing a math problem; Taking reading tests scares me; I feel tense and nervous when working with texts). Source articles were excluded if they stated that they measured school anxiety but used a general anxiety measure and asked general anxiety questions (e.g., I worry a lot of the time; I am nervous; Often, I feel sick to my stomach). The questions had to specifically ask about anxiety related to learning, school, or academic performance. If the study itself did not provide information about the questions or items included in the anxiety measure, the coder sought to locate further information about the measure, met with the research team to discuss the measure, and reached a consensus about its inclusion. If an anxiety-related measure could not be determined to be in relation to academic anxiety, and no other anxiety-related measures fulfilled the criteria, the study was excluded. The first author reviewed all the abstracts and full texts (i.e., through June 2021), using a gated screening approach with the inclusion and exclusion criteria. If, from the abstract, it could be discerned that the study did not meet criteria (e.g., participants were the wrong age, study did not use experimental design, study took place outside of school; see Figure 1 ), it was excluded. However, if the study could not be excluded based on the abstract, it was flagged for a full text review. The screening team, including the first, fourth, and seventh authors, reviewed 10% of abstracts independently to ensure that reliability was above 95% before completing the rest of the screening. Next, 85% of the abstracts were reviewed independently by two screeners and 5% were reviewed independently by one screener after reliability was established. Reliability was 96% (i.e., the agreements divided by the agreements plus disagreements), and all discrepancies were discussed to reach consensus. Of those, 5,460 records were excluded. That left 423 articles to be assessed for eligibility using the full text, which were independently reviewed by two members of the screening team. All discrepancies were discussed to reach 100% agreement. Of those, 403 articles were excluded for the following reasons: not available in English, no academic or academic anxiety outcomes, did not evaluate an academic intervention, study design, age of the participants (not in secondary school), publication type, or the setting was not in a school. One study was excluded because there was not enough information provided to calculate effect size. Next, a hand search of the following journals was conducted (from June 2019 to June 2021): Educational & Child Psychology , The Journal of Clinical Child and Adolescent Psychology , Metacognition and Learning , Evidence-Based Practice in Child and Adolescent Mental Health , and American Academy of Child & Adolescent Psychiatry . Journals were selected because they contained relevant empirical content related to academic anxiety research, as is recommended ( Giustini, 2019 ) and common for reviews (e.g., Roberts et al., 2015 ; Williams et al., 2018 ). To confirm that all relevant studies had been located, the selected journals were reviewed for a 2-year period, with the plan to review 2 more years from any relevant article identified. Because no additional studies were found, the search extended back to June 2019. No additional articles were identified in the hand search. Lastly, a forward and backward ancestral search of included studies resulted in a full-text screening of 48 additional articles, of which 2 qualified. A total of 20 experimental studies published in peer-reviewed journals or doctoral dissertations met inclusion criteria. One study could not be included in the analysis due to inconsistent reporting (i.e., it was not possible to discern which numbers to use to calculate effect sizes), yielding 19 studies included in the meta-analysis. Data Extraction We applied the same methods for coding as Fishstrom and colleagues (2022) . We coded studies that met inclusion criteria by using a protocol ( Vaughn et al., 2014 ) developed and used in previous research ( Wanzek et al., 2018 ) to extract information from academic intervention studies. The protocol aligns with study features detailed in the What Works Clearinghouse Design and Implementation Assessment Device ( Valentine & Cooper, 2008 ). We extracted the following from each article: (a) study characteristics (publication year, study design, research quality), (b) student participant demographics (e.g., age, grade, gender, risk factors), (c) intervention characteristics (e.g., interventionist, group size, length of intervention, content area), (d) measure details (e.g., name, content area, standardized vs. unstandardized), and (e) overall findings and effect size. Study characteristics, student participant demographics, and intervention characteristics were extracted to describe our source studies. We separated studies into math, language and literacy (which included foreign language learning), social studies, and science to address our third research question (to what extent intervention effects are moderated by academic content area). We also coded for control variable predictors, beyond the focal moderators, including measure type (standardized vs. unstandardized), research quality, and location. Previous research found that standardization of measures ( Scammacca et al., 2015 ), research quality ( Shadish & Heinsman, 1997 ), and location (e.g., Jung et al., 2020 ) can affect effect size. The variables that we extracted were identical to Fishstrom and colleagues (2022) and guided by previous research studying academic interventions and academic anxiety and what is commonly recommended for reviews ( Lipsey, 2019 ). When extracting these items from our corpus of studies, both forced-choice and open-ended questions were used, depending on the question. For example, defining the intervention used open-ended response; however, standardization of a measure, which is a dichotomous variable, used forced choice. We examined the aforementioned descriptors of interest, as is common in systematic reviews, to help with interpretation of effect sizes, including study conditions, participant details, and study quality ( Lipsey, 2019 ). For study conditions, coders extracted details about the independent variable (i.e., the academic intervention) and reported information about the comparison group as described in the study. We described the study descriptors using the language of the source articles (as is recommended; Wilson, 2019 ). See Table 2 for study condition examples. Furthermore, we coded each study for risk type of participants as described in the study. If students in the study were described as struggling learners or at academic risk, they were reported as academic risk. If students were described as typical learners with no academic or emotional disabilities, students were described as no risk. Studies that included students with mixed abilities were reported as such. Also, we reported whether students were learning in their first or second language and the language of instruction. Table 2. Characteristics of the Studies, Students, and Interventions Study Region Design Research quality N Gender Grade Risk type SES Academic content area Language Condition information Total hours Total sessions Awada & Ghaith (2018) Other RCT Medium 50 NR 9 LEP Low Language and literacy SL, English T: Paideia Seminar C: Regular reading comprehension instruction 30 NR Brown (1995) NA QED Low 96 NR 9–12 NR NR Math FL, English T1: Experimental Algebra I T2: Experimental basic algebra C1: Control Algebra I C2: Control basic algebra NR NR Chen & Chien (2020) Other QED Low 39 M = 21 (53.8%) F = 18 (46.2%) 8 NR NR Language and literacy SL, English T: SOAR sharing C: Independent SOAR 1 1 Chen et al. (2016) Other QED Medium 88 M = 50 (56.8%) F = 38 (43.2%) 7 NR NR Language and literacy SL, English T1: Functions of individual and collaborative annotation T2: Functions of individual and collaborative annotation with instructor’s support for reducing reading anxiety based on proposed PRAPM C: Only functions of individual annotation NR NR Chu & Leighton (2019) NA QED Medium 262 M = 141 (53.8%) F = 121 (46.2%) 8 NR NR Science FL, NR (Canada) T1: Prelab activity and learning error intervention T2: Prelab activity only T3: Learning error intervention only C: No additional activities 2 2 Frampton (2009) NA QED Low 304 M = 148 (48.7%) F = 156 (51.3%) 8 NR Middle; middle-upper a Science FL, English T: Integrated approach C: Traditional approach NR NR Franklin (1991) NA QED High 145 M = 68 (46.9%) F = 77 (53.1%) 8 No risk Low Science FL, English T: With concept mapping C: Without concept mapping 1.5 2 Green (2014) NA QED Medium 62 M = 21 (33.9%) F = 41 (66.1%) 8 NR NR Math FL, English T: Children’s literature linked to mathematics C: Traditional learning—mandated curriculum 30 20 Hwang et al. (2017) Other QED Low 77 NR 9 NR NR Language and literacy SL, English T: Problem-based English listening game C: Conventional instruction 4.5 b NR Idris (2006) Other QED Low 109 NR 10 NR NR Math FL, NR (Malaysia) T: TI-84 graphing calculator C: BAU NR NR Jegede et al. (1990) Other RCT Medium 51 M = 30 (58.8%) F = 21 (41.2%) 10 No risk NR Science FL, English T: Concept mapping heuristic C: Expository teaching 24 18 Khairiatin Nida et al. (2020) Other QED Medium 246 NR 7 NR NR Math FL, Indonesian T1: Station rotation T2: Flipped classroom C: Direct learning model NR NR Lim & Chapman (2015) Other QED Medium 103 M = 33 (32.0%) F = 70 (68.0%) 11 No risk NR Math FL, English (Singapore) T: With history of math C: Without history of math 100 c 100 Moyer (2012) NA QED Low 226 d M = 105 (47.1%) F = 118 (52.9%) 8 Other e Low Science FL, English T: Technology enhanced C: Typical instruction NR NR Orabuchi (2013) NA QED Low 155 M = 69 (44.5%) F = 86 (55.5%) 6 NR Low Math FL, English T: Brainingcamp C: Typical instruction 12–18 24 Samuelsson (2008) Other RCT Medium 119 M = 60 (50.4%) F = 59 (49.6%) 7 NR Average Math FL, English T1: Independent work T2: Problem solving C: Traditional instruction 25 30 Wei (2010) NA QED Medium 128 M = 58 (45.3%) F = 70 (54.7%) 9 AR f NR Math FL, English T: Treatment messages C: No treatment messages 3.33 g 4 Yang et al. (2020) Other QED High 51 NR 11 No risk NR Language and literacy SL, English T: Cognitive complexity level-based gaming C: Conventional situational English vocabulary gaming approach 1.5 1 Yu & Pan (2014) Other QED Low 66 NR 8 NR NR Social studies FL, Mandarin T: Online prompts to generate questions C: Instruction without online prompts 4.5 6 Open in a new tab Note . N = sample size; SES = socioeconomic status; RCT = randomized control trial; NR = not reported; LEP = limited English proficiency; SL = second language; T = treatment; C = comparison; NA = North America; QED = quasi-experimental design; FL = first language; T1 = Treatment 1; T2 = Treatment 2; C1 = Comparison 1; C2 = Comparison 2; M = male; F = female; SOAR = selection, organization, association, and regulation; PRAPM = personalized reading anxiety prediction model; BAU = business as usual; AR = academic risk. a 48% of treatment and 35% of control were low income. b Based on calculation. c 40 lectures and 60 tutorial sessions. d Sample changed to 223 due to participant mortality. e Classes were a mix of students with and without disabilities. f Participants did not pass algebra in junior high school. g Based on calculation. We coded each study for quality based on four indicators: research design, sample size, attrition, and intervention contaminants. We used What Works Clearinghouse standards (2020) for research design quality and previous research focused on reading intervention study quality ( Austin et al., 2019 ) to identify these key indicators. A study was rated as high quality if group membership was determined through a random process, it had a sufficiently large sample of greater than or equal to 20 participants, it had low attrition (overall and differential) using What Works Clearinghouse standards ( Institute of Education Sciences, 2021 ), and there was no evidence of other intervention contaminants (e.g., events such as students in the control group receiving the treatment) that could have interfered with our ability to make a causal inference about the intervention. A study was rated as medium quality if group membership was not determined through a random process but equivalence was established at baseline, and there were a sufficient sample size (> 20), low attrition, and no intervention contaminants. A study was rated as low quality if group membership was not determined through a random process and equivalence was not established at baseline or if a study reported high attrition or an insufficient sample size (< 20). The gold-standard method of coder training ( Gwet, 2001 ) was used to establish interrater reliability prior to coding. The methodology employed in this study is a continuation of the approach used in the antecedent research ( Fishstrom et al., 2022 ). The first author was the trainer for three other coders. The full team of four coders included two Ph.D.-level researchers and two graduate research assistants studying learning disabilities intervention research. The three coders being trained received a four-part coding training. First, the trainer provided an overview in which the coders were informed generally of the research questions and then provided the codebook with instructions for how to report each item and examples (which included both open and closed responses). Second, the trainer modeled the coding processes and provided examples of similar studies from the antecedent research ( Fishstrom et al., 2022 ) that had already been coded. Third, the three coders took a reliability test by coding the same article independently and comparing their results to the trainer. Responses from each coder were used to calculate percentage of agreement (i.e., the agreements divided by the agreements plus disagreements); reliability needed to be above 90% to begin coding studies. And fourth, the trainer discussed any questions that coders had after completing the reliability test. Coders scored an average of 92% on reliability, and discrepancies were discussed. Next, two raters independently coded each study, resulting in an average reliability of 91% on the source studies. The coders met to review each code sheet and to identify and resolve any discrepancies, ultimately resulting in 100% agreement on all source studies. When the two coders were unable to resolve an item on the code sheet, the author team met to come to a consensus. Meta-Analysis Procedures Effect Size Calculation We used standardized mean difference for the metric of this analysis, calculated using the Hedges’s g estimator ( Hedges, 1981 ). For the academic achievement outcomes, a positive value indicated that the treatment students outperformed control students; for the anxiety outcomes, a negative value indicated that the treatment students outperformed their control peers (negative value indicating less anxiety, which reflects a good outcome). For studies that reported baseline measure or used analysis of covariance, we used effect size estimations that account for pretest ( Borenstein, 2009 ; Morris & DeShon, 2002 ). If a pre-post correlation or an R 2 were not provided, we imputed .5 or 0, respectively, as suggested by the What Works Clearinghouse (2022) . Furthermore, if a study used a cluster randomized design, the effects were standardized by the total variance to account for variance from the student level and cluster levels ( Hedges, 2007 ; Taylor et al., 2021 ). These cluster corrections require an intraclass correlation for each cluster level. If not reported, we imputed .2 for academic outcomes and .1 for behavioral outcomes following the What Works Clearinghouse guidelines (2022) . We also aggregated across subgroups to reduce unnecessary amounts of within-study variability when it was not relevant to moderator analyses. Outliers Using Tukey’s definition (values more than 1.5 times the interquartile range from the quartiles are classified at outliers), we detected one outlier among the anxiety outcome effect sizes and one outlier among the academic outcome effect sizes. We winsorized the outliers to be the minimum of the 25th percentile minus 3 times the interquartile range or 75th percentile plus 3 times the interquartile range. Analysis Strategy For overall pooled effect analyses to account for dependency within studies and to model the two outcome types, we used a modified subgroup correlated effects (SCE) model ( Pustejovsky & Tipton, 2020 ), where we modeled the academic anxiety and academic outcomes as correlated using metafor and clubSandwich R packages with small-sample correction adjustment to the residuals and degrees of freedom ( Pustejovsky, 2019 ; Tipton, 2015 ; Viechtbauer, 2010 ). Typically, this model assumes effect sizes from anxiety and academic outcomes are independent and allows us to synthesize effect sizes across the two constructs while accounting for dependency within studies. However, one variation of this model is to model the outcome types as correlated, which more accurately reflects our hypothesis and the dependence in the data. Because the SCE model with outcome types as independent is a simpler model, we decided to include it as a sensitivity analysis. For moderator analyses, we applied the correlated-hierarchical effects (CHE-RVE) model ( Pustejovsky & Tipton, 2022 ; Vembye et al., 2022 ). The CHE-RVE model considers both the multilevel structure of the effect size data with effect sizes nested in studies ( Van den Noortgate et al., 2013 ) and guards against any misspecification of the model via RVE ( Hedges et al., 2010 ; Tipton & Pustejovsky, 2015 ) while accounting for both hierarchical and correlated dependence structures. The CHE model assumes a constant sample correlation between effect size estimates coming from the same study, and we assumed a correlation of .80 between effect sizes. We also corrected the standard errors for small sample size ( Pustejovsky & Tipton, 2022 ; Tipton, 2015 ). To address the research questions, we included control variable predictors to account for variation in the effect sizes due to factors beyond the focal moderator. We adjusted for measure type (standardized vs. unstandardized), research quality, and location (North America vs. not North America; similar to Jung et al., 2020 , who accounted for location differences in their meta-analytic review). Publication Bias Assessment To assess publication bias, we implemented Egger’s regression tests accounting for dependent effect sizes ( Egger et al., 1997 ; Rodgers & Pustejovsky, 2021 ) and step-function selection model tests ( Hedges & Vevea, 2005 ) using the weightr package in R ( Coburn, 2018 ). For the step-function selection model test, we used two cut points at p = .025 and p = .05 ( Hedges & Vevea, 2005 ). We ran the three-parameter ( Hedges & Vevea, 2005 ; Pustejovsky & Rodgers, 2019 ) and four-parameter selection models ( Chen & Pustejovsky, 2024 ) for the step-function selection models. For the three-parameter selection model, we evaluated a cut point at the p -value of .025, which divides the thresholds into significant p -values in the expected direction (one-tailed) and nonsignificant p -values. The four-parameter selection model evaluates three p -value intervals: 0.00–0.025, 0.025–0.05, and 0.05–1, which separate the p -values into significant p -values in the positive tail (expected direction of the effect size), marginally significant p -values, and nonsignificant p -values. Using these different thresholds, the model provides adjusted model estimates based on the weighting scheme. Following the example of Williams et al. (2022) , we then compared the adjusted parameters to the unadjusted parameters. No change in the parameters indicates that there is no presence of publication bias. This method assumes that the effect sizes are independent, so the standard errors are unreliable when the data structure has dependence among effect sizes. Because there is dependence among effect sizes, we only evaluated the adjusted mean from the results. Results Our results are twofold. First, we describe the 19 studies descriptively (research question 1). Then, we present our meta-analysis of the 19 studies, which included 62 effect sizes—25 anxiety effect sizes and 37 academic effect sizes (research questions 2 and 3). Table 1 summarizes descriptive characteristics about the corpus of studies reviewed, including publication year, region, study design, research quality, academic measure type, academic anxiety measure type, academic content area, and whether the intervention was a second language. Table 2 provides study-level descriptive information including study, student, and intervention characteristics. Table 3 reports study-level measure and effect size information. Table 4 shows the pooled estimates for academic and academic anxiety outcomes with and without the selected controls (i.e., research quality, standardization of the measure, and region). Table 5 shows the (a) overall average effects for academic and academic anxiety outcomes with and without nonfocal control variables and (b) overall average effects for academic and academic anxiety outcomes when removing studies with a low-quality rating. Lastly, Figure 2 is a forest plot for the academic outcomes, and Figure 3 is a forest plot for the academic anxiety outcomes. The first row in the forest plot represents the summary effects of the SCE models with control variables for each outcome type represented by a diamond. The width of the diamond is equal to the 95% confidence interval, and the center is the magnitude. Each effect size is represented with a circle, and the size of each circle corresponds to the weight of the effect sizes in the study’s meta-analysis regression model. Each effect also has a 95% confidence interval displayed as a bar, showing that the larger the confidence interval, the more imprecise the effect. When interpreting the forest plots, it is important to consider that the more imprecise effect sizes receive more visual attention due to the larger width of the bars, compared to more precise effect sizes, which are shorter ( Fitzgerald & Tipton, 2022 ). Table 1. Descriptive Characteristics of the Studies Characteristic k % Publication year 1990s 3 15.8 2000s 3 15.8 2010s 10 52.6 2020–present 3 15.8 Region North America 8 42.1 Other 11 57.9 Study design Randomized control trial 5 26.3 Quasi-experimental design 14 73.7 Research quality High 2 10.5 Medium 9 47.4 Low 8 42.1 Academic measure type Standardized 9 47.4 Unstandardized 10 52.6 Academic anxiety measure type Standardized 13 68.4 Unstandardized 6 31.6 Academic content area Language and literacy 5 26.3 Math 8 42.1 Science 5 26.3 Social studies 1 5.3 Language First language 14 73.7 Second language 5 26.3 Open in a new tab Note. k = number of studies. Table 3. Study Information Regarding Measures and Effect Sizes Study Group N Academic outcomes Academic anxiety outcomes Measure Measure type Effect size (SE) [95% CI] Measure Measure type Effect size (SE) [95% CI] Awada & Ghaith (2018) T = 25 C = 25 Paideia Active Learning Test Nonstandardized 1.31 (0.70) [−0.06, 2.69] Reading Anxiety Scale (Young, 1999) Standardized −0.63 (0.53) [−1.68, 0.42] Brown (1995) T = 58 T1 = 50 T2 = 35 C = 54 C1 = 46 C2 = 23 Test of Achievement and Proficiency Standardized T1 and C1: −0.21 (0.49) [−1.17, 0.75] T2 and C2: 0.24 (0.53) [−0.80, 1.27] Mathematics Anxiety Rating Scale Standardized T and C: −0.39 (0.29) [−0.96, 0.18] Chen & Chien (2020) T = 17 C = 22 Passage Reading Comprehension Nonstandardized 2.29 (0.76) [0.80, 3.77] Foreign-Language Reading Anxiety Scale (adjusted) Nonstandardized a −0.56 (0.56) [−1.66, 0.54] Chen et al. (2016) T1 = 27 T2 = 30 C = 31 General English Proficiency Test Standardized T1 and C: −0.26 (0.28) [−0.81, 0.28] T2 and C: −0.16 (0.27) [−0.70, 0.37] Foreign-Language Reading Anxiety Scale Standardized T1 and C: −0.71 (0.28) [−1.26, −0.15] T2 and C: −0.19 (0.26) [−0.70, 0.31] Chu & Leighton (2019) T1 = 106 T2 = 53 T3 = 62 C = 47 Post Enhancement Question Score Nonstandardized T1 and C: 0.47 (0.71) [−0.92, 1.85] T2 and C: −0.05 (0.66) [−1.35, 1.25] T3 and C: 0.12 (0.66) [−1.18, 1.43] Post Enhancement Question Score Nonstandardized T1 and C: 2.19 (0.53) [1.15, 3.22] T2 and C: 1.34 (0.50) [0.37, 2.32] T3 and C: 6.00.91 (0.63) [4.77, 7.24] Frampton (2009) T = 180 b C = 124 c ISEA DSTP-Science Assessments Standardized 8.39 (0.53) [7.34, 9.43] 0.99 (0.28) [0.44, 1.53] ATSI Standardized −0.21 (0.21) [−0.63, 0.20] Franklin (1991) T = 78 C = 67 P10 Overall Standardized 0.36 (0.17) [0.03, 0.69] CASA Overall Standardized 0.01 (0.17) [−0.32, 0.33] Green (2014) T = 31 C = 31 Holt McDougal Textbook Math Assessment Nonstandardized 1.50 (0.69) [0.14, 2.86] Mathematics Anxiety Scale for Children Standardized −1.12 (0.52) [−2.15, −0.10] Hwang et al. (2017) T = 39 C = 38 General English Proficiency Test Standardized 0.94 (0.24) [0.48, 1.41] Modified Foreign Language Classroom Anxiety Scale Standardized −0.29 (0.23) [−0.74, 0.15] Idris (2006) T = 54 C = 55 Mathematics Achievement Test Nonstandardized 0.78 (0.49) [−0.18, 1.73] Mathematics Anxiety Test Nonstandardized −0.95 (0.37) [−1.68, −0.21] Jegede et al. (1990) T = 29 C = 22 Biology Achievement from West African Exam Council Standardized 2.07 (0.72) [0.65, 3.49] Affect Adjective Checklist Standardized −0.78 (0.54) [−1.83, 0.28] Khairiatin Nida et al. (2020) T1 = 85 T2 = 85 C = 76 Mathematics Creative Thinking Skills Nonstandardized T1 and C: 0.77 (0.40) [−0.01, 1.55] T2 and C: 1.09 (0.40) [0.30, 1.89] Math Anxiety Nonstandardized T1 and C: 0.65 (0.30) [0.05, 1.24] T2 and C: 0.21 (0.30) [−0.38, 0.80] Lim & Chapman (2015) T = 51 C = 52 Techniques of differentiation Application of differentiation Integration Vectors posttest Binomial theorem Series and sequences Maclaurin’s series Overall retention test 1 d Overall retention test 2 e Nonstandardized 0.54 (0.20) [0.15, 0.93] 0.58 (0.20) [0.18, 0.97] 0.78 (0.20) [0.38, 1.18] 0.49 (0.20) [0.10, 0.88] 0.71 (0.20) [0.31, 1.11] 1.24 (0.22) [0.81, 1.66] 0.67 (0.20) [0.28, 1.07] 0.32 (0.20) [−0.07, 0.71] 0.48 (0.20) [0.09, 0.87] Fennema-Sherman Mathematics Anxiety Subscale Standardized 0.16 (0.20) [−0.23, 0.55] Moyer (2012) T = 131 f C = 92 g Energy Concept Inventory Standardized 0.17 (0.14) [−0.10, 0.44] ATSI Standardized −0.38 (0.15) [−0.67, −0.09] Orabuchi (2013) T = 90 h C = 44 i Texas Assessment of Knowledge and Skills Standardized 0.05 (0.70) [−1.32, 1.41] MARS Standardized −0.25 (0.52) [−1.27, 0.77] −0.06 (0.52) [−1.08, 0.96] Positively Formed Negatively Formed Samuelsson (2008) T1 = 41 T2 = 39 C = 39 Calculation Quantitative Concept Nonstandardized T1 & C: 0.18 (0.50) [−0.80, 1.15] T2 & C: −0.02 (0.50) [−0.99, 0.96] T1 & C: −0.56 (0.50) [−1.54, 0.42] T2 & C: −0.04 (0.50) [−1.01, 0.94] Math Anxiety Nonstandardized T1 and C: 0.05 (0.39) [−0.71, 0.80] T2 and C: −0.12 (0.39) [−0.88, 0.64] Wei (2010) T = 60 C = 68 MathGirls assessment of math learning Standardized 0.03 (0.20) [−0.35, 0.41] Revised MARS Standardized 0.15 (0.18) [−0.20, 0.50] Yang et al. (2020) T = 25 C = 26 English Vocabulary Nonstandardized 0.61 (0.69) [−0.75, 1.97] English Anxiety Nonstandardized 0.95 (0.54) [−0.10, 2.01] Yu & Pan (2014) T = 32 C = 34 Civics and Citizenship Student Question Generation Nonstandardized 0.51 (0.68) [−0.82, 1.84] 0.83 (0.68) [−0.50, 2.17] Learning Anxiety Scale Standardized 0.12 (0.51) [−0.88, 1.12] Open in a new tab Note. N = sample size; SE = standard error; CI = confidence interval; T = treatment; C = comparison; T1 = Treatment 1; T2 = Treatment 2; C1 = Comparison 1; C2 = Comparison 2; T3 = Treatment 3; ISEA = Integrated Summative Energy Assessment; DSTP = Delaware State Testing Program; ATSI = Attitude Toward Science Inventory; CASA = Czerniak Assessment of Science Anxiety; MARS = Mathematics Anxiety Rating Scale. a It was an adapted version. b Only 178 participants had DSTP Science scores and 172 participants had ATSI scores. c Only 116 participants had ATSI scores. d Administered 4 months after the last intervention session. e Administered 1 year after the last intervention session. f Only 115 participants had ATSI scores. g Only 78 participants had ATSI scores. h Only 88 participants had MARS scores. i Only 39 participants had MARS scores. Table 4. Pooled Estimates for Selective Reporting by Outcome Type: Hedges’s g Effect Sizes Cut point Academic outcomes: No control variables Anxiety outcomes: No control variables Academic outcomes: With control variables Anxiety outcomes: With control variables Unadjusted a 0.55 −0.03 0.13 0.15 p = .025 0.45 −0.19 0.08 0.04 p = .025, .05 0.45 −0.20 0.09 0.02 Open in a new tab Note. Control variables include research design, research quality, standardized measure, and region. For the anxiety outcomes, a negative value indicates that the treatment students outperformed their control peers (negative value indicating less anxiety, which reflects a good outcome). When the mean intervention effects are downwardly adjusted, it suggests that the statistically significant positive effects were more likely to be reported than nonsignificant results. a The unadjusted result from the selective reporting results that does not account for dependence among effect sizes. Table 5. Overall Average Effects Outcome k N ES g ( SE ) df 95% CI τ 2 Q 95% PI All effects without control variables Academic outcomes 19 37 0.66 16.40 (0.17) *** [0.31, 1.01] 0.25 268.34 *** [−0.11, 1.43] Academic anxiety outcomes 19 25 −0.13 (0.11) 13.46 [−0.36, 0.10] 0.05 268.34 *** [−0.30, 0.04] All effects with control variables Academic outcomes 19 37 0.66 (0.22) ** 10.71 [0.16, 1.15] 0.32 258.57 *** [−0.25, 1.57] Academic anxiety outcomes 19 25 −0.03 (0.11) 3.93 [−0.34, 0.28] 0.002 258.57 *** [−0.12, 0.05] Low-quality studies removed without control variables Academic outcomes 11 26 0.52 (0.19) ** 8.68 [0.10, 0.95] 0.20 142.46 *** [0.03, 1.01] Academic anxiety outcomes 11 16 0.03 (0.17) 8.49 [−0.36, 0.42] 0.14 142.46 *** [−0.34, 0.40] Low-quality studies removed with control variables Academic outcomes 11 26 0.27 (0.14) 3.40 [−0.15, 0.68] 0.08 127.11 *** [0.02, 0.52] Academic anxiety outcomes 11 16 0.10 (0.10) 2.61 [−0.35, 0.37] 0.007 127.11 *** [−0.10, 0.12] Open in a new tab Note . Control variables include research design, research quality, standardized measure, and region. k = number of studies; N ES = number of effects; g = Hedges’s g (average pooled effect); SE = standard error; df = degrees of freedom; CI = confidence interval; τ 2 = between study heterogeneity; Q = test statistic for the test of residual heterogeneity; PI = prediction interval. ** p < .01 *** p < .001. Figure 2. Strength of the Standardized Mean Difference Between Treatment and Comparison on Academic Outcomes. Open in a new tab Note . The estimate and 95% confidence interval for the overall pooled effect from the subgroup correlated effects model is displayed in the first row. CI = confidence interval; T1 = Treatment 1; C1 = Comparison 1; T2 = Treatment 2; C2 = Comparison 2; C = comparison; T3 = Treatment 3; ISEA = Integrated Summative Energy Assessment. Figure 3. Strength of the Standardized Mean Difference Between Treatment and Comparison on Anxiety Outcomes. Open in a new tab Note . The estimate and 95% confidence interval for the overall pooled effect from the subgroup correlated effects model is displayed in the first row. CI = confidence interval; T1 = Treatment 1; C = comparison; T2 = Treatment 2; T3 = Treatment 3; MARS = Mathematics Anxiety Rating Scale. Population and Setting Collectively, the 19 studies provided academic interventions to a total of 2,377 participants in nine countries across four continents. Eight studies took place in North America ( Brown, 1995 ; Chu & Leighton, 2019 ; Frampton, 2009 ; Franklin, 1991 ; Green, 2014 ; Moyer, 2012 ; Orabuchi, 2013 ; Wei, 2010 ), one in Europe ( Samuelsson, 2008 ), nine in Asia ( Awada & Ghaith, 2018 ; Chen & Chien, 2020 ; Chen et al., 2016 ; Hwang et al., 2017 ; Idris, 2006 ; Khairiatin Nida et al., 2020 ; Lim & Chapman, 2015 ; Yang et al., 2020 ; Yu & Pan, 2014 ), and one in Africa ( Jegede et al., 1990 ). Of the 19 studies, 12 reported a gender breakdown of participants. The percentage of males ranged from 32% to 58.8% ( M = 47.6%). Six of the studies reported socioeconomic backgrounds of the participants. Of those, four reported students from economically disadvantaged households ( Awada & Ghaith, 2018 ; Franklin, 1991 ; Moyer, 2012 ; Orabuchi, 2013 ), and the other two reported middle and average economic status ( Frampton, 2009 ; Samuelsson, 2008 ). Seven studies reported whether students were academically at risk (i.e., whether there was academic difficulty, as reported in the study), with four reporting typically achieving students (i.e., no risk; Franklin, 1991 ; Jegede et al., 1990 ; Lim & Chapman, 2015 ; Yang et al., 2020 ), one reporting students with limited English proficiency ( Awada & Ghaith, 2018 ), one reporting a mix of students with and without disabilities ( Moyer, 2012 ), and one reporting mathematically at-risk students (i.e., students who did not pass algebra in middle school; Wei, 2010 ). Intervention Foci and Characteristics Of the 19 studies, eight focused on math, five focused on science, five focused on language and literacy, and one focused on social studies. In 17 of the 19 studies, the purpose was examining the effects of an academic intervention, no anxiety management component was embedded within the intervention, and improved academic and academic anxiety outcomes were hypothesized. Two studies integrated an anxiety management component with the academic intervention, with improved academic and academic anxiety outcomes hypothesized (these interventions are described later in the Discussion section; Chen et al., 2016 ; Wei, 2010 ). All interventions occurred during regular school hours. Twelve studies were led by teachers ( Brown, 1995 ; Chu & Leighton, 2019 ; Frampton, 2009 ; Green, 2014 ; Hwang et al., 2017 ; Idris, 2006 ; Khairiatin Nida et al., 2020 ; Moyer, 2012 ; Orabuchi, 2013 ; Samuelsson, 2008 ; Yang et al., 2020 ; Yu & Pan, 2014 ), five by researchers ( Awada & Ghaith, 2018 ; Chen & Chien, 2020 ; Franklin, 1991 ; Jegede et al., 1990 ; Lim & Chapman, 2015 ), and two by other (i.e., digital delivery; Chen et al., 2016 ; Wei, 2010 ). The group sizes ranged from individual to whole-class interventions. The length of the interventions ranged from 1 hour to 100 hours total (see Table 2 ). Measures Twenty-eight different academic achievement measures were used across all studies. Ten of the measures were standardized; otherwise, the measures were nonstandardized. Fifteen self-report measures of academic anxiety were reported across all 19 studies. Seven focused on math anxiety, three focused on anxiety related to a foreign language, two included general learning anxiety scales, and three focused on anxiety toward science (with one assessing anxiety regarding virtual science lab work). Ten of the academic anxiety measures used across the corpus were standardized (see Table 3 ). Meta-Analytic Findings Effects of Academic Interventions on Academic Achievement Outcomes We estimated the overall average effect size for academic outcomes to be 0.66 ( SE = 0.17, 95% CI [0.31, 1.01], 95% PI [−0.11, 1.43]), which was statistically significant and different from zero ( t = 3.99, df = 16.40, p = .001). When adjusting for standardized measure, research quality, and location, the average effect size estimate remained the same, g = 0.66 ( SE = 0.22, 95% CI [0.16, 1.15], 95% PI [−0.25, 1.57]), which was statistically significant and different from zero ( t = 2.93, df = 10.71, p = .01). For the sensitivity analysis, which removed low-quality studies, we estimated the average effect size for the academic effects to be 0.52 ( SE = 0.19, 95% CI [0.10, 0.95], 95% PI [0.03, 1.01]), which was statistically significant and different from zero ( t = 2.79, df = 8.68, p = .02). Lastly, when adjusting for control variables (standardized measure, research quality, and location) for the studies with medium or high quality, we estimated the average academic outcome effect size to be 0.27 ( SE = 0.14, 95% CI [−0.15, 0.68], 95% PI [0.08, 0.52]), which was not statistically significant or different from zero ( t = 1.92, df = 3.40, p = .14). For the academic effect, the degrees of freedom were less than 4, indicating that there was inadequate power to include the controls (i.e., df < 4; Tipton, 2015 ); thus, these findings should be interpreted with caution. Additionally, the simpler model, where the academic and academic anxiety types were not correlated, resulted in an average effect size for the academic effects of 0.67 ( SE = 0.23, 95% CI [0.17, 1.17], 95% PI [0.42, 0.92]), which was statistically significant and different from zero ( t = 2.96, df = 10.56, p = .01). The effect size estimates for academic interventions on academic outcomes were heterogeneous, with a between-study variance of τ 2 = 0.32 after including nonfocal control variables, suggesting that the inclusion of the control variables did not explain all the variation in the effect sizes. Effects of Academic Interventions on Academic Anxiety Outcomes We estimated the overall average effect size for academic anxiety outcomes to be −0.13 ( SE = 0.11, 95% CI [−0.36, 0.10], 95% PI [−0.30, 0.04]), which was not statistically significant or different from zero ( t = −1.24, df = 13.46, p = .24). When adjusting for standardized measure, research quality, and location, we estimated the average effect size for the academic anxiety effects to be −0.03 ( SE = 0.11, 95% CI [−0.34, 0.28], 95% PI [−0.12, 0.05]), which was not statistically significant or different from zero ( t = −0.30, df = 3.93, p = .78). After we removed low-quality studies, we estimate the average effect size for academic anxiety to be 0.03 ( SE = 0.17, 95% CI [−0.36, 0.42], 95% PI [−0.34, 0.40]), which is not statistically significant and different from zero ( t = 0.17, df = 8.49, p = .87). Lastly, when adjusting for nonfocal control variables (standardized measure, research quality, and location) for the studies with medium or high quality, we estimated the average academic anxiety outcome effect size to be 0.01 ( SE = 0.10, 95% CI [−0.35, 0.37], 95% PI [−0.10, 0.12]), which was not statistically significant or different from zero ( t = 0.10, df = 2.61, p = .93). For the academic anxiety effect, the degrees of freedom were less than 3, indicating that there may not have been enough power to include the control variables. The working model sensitivity analysis where the academic and academic anxiety types were not correlated resulted in an average effect size for the academic anxiety effects of −0.01 ( SE = 0.11, 95% CI [−0.33, 0.30], 95% PI [−0.13, 0.10]), adjusting for standardized measure, research quality, and location, which was not statistically significant or different from zero ( t = −0.12, df = 3.83, p = .91). The effect size estimates for academic interventions on academic anxiety outcomes had little variation, with τ 2 = 0.05 before including control variables and τ 2 = 0.00 after including control variables. Moderator Analyses For the moderator analysis, we looked at the outcome types separately using the CHE model. Because the CHE model accounted for dependent effects, to our knowledge, there were only power approximations for overall pooled effects ( Vembye et al., 2023 ), so results should be interpreted with caution, as our moderator analysis could be underpowered. We were primarily interested in whether the academic content area explained the variance in the academic and anxiety effects separately. First, we ran a Wald test to see whether there were differences in the content areas. We failed to reject the null and found that none of the content areas were significantly different from the rest for academic outcomes in the model that included control variables ( F (2, 7.62) = 1.06, p = .39) and the model without control variables ( F (2, 7.51) = 0.45, p = .66). This remained true for the academic anxiety outcomes as well, where we failed to reject the null and found that none of the different content areas affected academic anxiety outcomes in the model that included controls ( F (2, 7.26) = 0.19, p = .83) and the model without control variables ( F (2, 7.63) = 0.18, p = .84). Publication Bias We evaluated publication bias through Egger’s regression and selective models. For Egger’s regression, we used the CHE working model. Also, because the standard error for standardized mean difference was correlated with the effect size estimates, we used (n1+n2)/(n1n2) for precision here instead (this assumes equal group sizes; Pustejovsky & Rodgers, 2019 ). For the Egger’s regression analysis, the academic outcomes showed no evidence of publication bias without control variables (= 3.29, SE = 4.27, t (8.81) = 0.77, p = .46) or with control variables (= 2.90, SE = 4.06, t (6.41) = 0.72, p = .50). The same was true for anxiety outcomes without control variables (= −3.70, SE = 2.69, t (8.52) = −1.37, p = .20) or with control variables (= −4.04, SE = 2.78, t (6.19) = −1.45, p = .20). As a sensitivity analysis, we also evaluated publication bias through selective modeling (reported in Table 4 ). This approach does not account for dependence, so the aim was to evaluate the stability of the overall pooled effect across different p -value thresholds. When the overall pooled effects are downwardly adjusted, it suggests that the statistically significant positive effects were more likely to be reported than nonsignificant results. For the academic outcomes with no control variables, when comparing the unadjusted effect size to those below, the overall pooled estimates remained stable, the sign was consistent, and the magnitude did not drastically change, even when we compared across the evaluated thresholds. For the overall pooled effects with control variables, the unadjusted pooled effect for academic outcomes using the selective reporting working model not accounting for dependent effects, the magnitude of the overall pooled effect was much smaller than our results. The effect for the academic outcomes remained stable and near the value of the unadjusted average effect. For the academic anxiety outcomes with control variables, the valence flipped after evaluating different cut points, but because we did not have a significant overall average effect size, values below zero were expected as well. Discussion This meta-analysis sought to examine the effects of academic interventions on academic and anxiety outcomes in studies addressing secondary students. Prior research has identified that academic interventions had a positive effect on academic outcomes but a more variable impact on academic anxiety outcomes ( Fishstrom et al., 2022 ; Codding et al., 2023 ). In a study with elementary and secondary students, math interventions improved both math achievement and math anxiety outcomes ( Codding et al., 2023 ). Fishstrom and colleagues (2022) found that for elementary-grade students, academic interventions did not significantly affect academic anxiety. We wondered whether these findings would be different for secondary students because the relation between academic anxiety and achievement has been found to be stronger for older students ( Hembree, 1988 ; Hill et al., 2016 ). Effects of Academic Interventions on Outcomes As hypothesized, the results of this meta-analysis showed that academic interventions have a significant effect ( g = 0.66) on academic achievement outcomes in secondary students but no significant effect on academic anxiety outcomes ( g = −0.13; negative reflects reduction in anxiety). These results were consistent with previous research that has found generally positive results of school-based interventions on academic outcomes (e.g., for secondary students; Fishstrom et al., 2022 ). Similarly, the lack of a significant effect on anxiety outcomes is also consistent with previous research, revealing that academic interventions affect the primary aim of the intervention, with the largest effects associated with proximal outcomes ( Codding et al., 2023 ; Dietrichson et al., 2017 ). Academic interventions are less likely to address secondary aims or distal outcomes. For example, in a recent article addressing reading comprehension, Clemens and Fuchs (2022) argued that although standardized distal reading comprehension measures are important, they are uncommonly associated with statistically significant effects. Thus, near-transfer measures are also necessary because they are straightforward and can provide answers to simpler hypotheses, and distal outcomes are more complex. Another example includes a systematic review that investigated the effects of reading interventions on students with both reading and behavior difficulties ( Roberts et al., 2020 ). The findings included significant effects on reading outcomes ( g = .86) but no association with improved behavior outcomes. Taken together, it appears that it is common, and perhaps generalizable, that academic interventions will directly address primary aims, yet spillover effects into other areas that are less directly related are minimal. For this reason, and considering the prevalence of anxiety in adolescence ( National Center for Education Statistics, 2022 ), it seems that anxiety may need to be addressed directly. One way to do this could be to weave academic interventions and anxiety management strategies together to accomplish both targets ( Fletcher & Grigorenko, 2017 ; Francis et al., 2019 ; Grills et al., 2014 ; Namkung et al., 2019 ). Though we had hoped to examine the effects of solely academic interventions compared to academic interventions that embedded an anxiety management component, this was not possible because only two studies embedded an anxiety management component within the academic interventions, resulting in inadequate effects. One study, by Wei (2010) , used a computer-based pedagogical agent to teach content and also provide math anxiety messages to ninth-grade students in an algebra class. The approach provides an awareness of math anxiety and how to cope with symptoms alongside problem solving and reviewing of math concepts. The findings revealed no significant effects on either math or math-anxiety outcomes. However, analysis of students with high math anxiety demonstrated a reduction of anxiety symptoms. A second study by Chen and colleagues (2016) used an intervention to support learning English as a second language based on student behavior as tracked by a digital annotation database for seventh-grade students. This system was found to decrease second-language reading anxiety, particularly for male students, and increase reading performance. In the introduction, we described three models explaining the connection between anxiety achievement: (a) poor achievement leading to anxiety, (b) anxiety influencing achievement negatively, or (c) a bidirectional relationship between academic achievement and anxiety. Our findings that academic interventions improved academic outcomes but not academic anxiety outcomes do not lend credence to the hypothesis that academic anxiety solely stems from poor achievement. If poor achievement were the predominant cause of academic anxiety, we might expect improvements in academic achievement to correspond with reduced anxiety. However, there is likely much more nuance to the relation between achievement and academic anxiety. For example, a recent meta-analysis by Codding and colleagues (2023) , which included two studies that were also included in our corpus of studies ( Orabuchi, 2013 ; Wei, 2010 ), found that effect sizes are stronger when the intervention approach aligns with the outcome (i.e., improved academic outcomes from an academic intervention), which corroborates our findings. Codding and colleagues also found that although skill-based interventions yielded improvements in math anxiety, therapeutic interventions did not yield improved math achievement. This finding suggested that a weighted bidirectional relation may exist, where academic performance may have a greater influence on the relation. This would indicate that although academic interventions are imperative and have more weight, it could be that for some students with higher levels of academic anxiety, a combined intervention approach is needed ( Codding et al., 2023 ). Barroso and colleagues (2021) found that the strength of the negative relation between math performance and math anxiety was weaker for students with low math ability, so it could be that the students with high anxiety who may benefit from a combined approach might not be the students with low math ability. Future research is needed in this area. It is also worth considering that the studies in this corpus examined the effects of interventions on anxiety immediately after intervention. It is possible that transfer from improvements in academic performance to reduced anxiety take more time, as students may not have internalized their improved academic skills directly following the intervention (i.e., sleeper effects; van Aar et al., 2017 ). Van Aar and colleagues (2017) conducted a meta-analysis of parenting interventions on child behavior and found that some studies showed enlarged positive effects at follow-up (i.e., sleeper effects) because of the time it takes for some of the intervention effects to materialize but that this appeared to be circumstantial (i.e., was present for only some interventions). In this study, it could be that some intervention effects need time to materialize or that some students need time to internalize their academic improvement. Though we did not find statistically significant differences on anxiety outcomes, it is worth noting that the directional effects of intervention on anxiety outcomes were negative with and without control variables. It may be that longer academic interventions are necessary to see significant reductions in academic anxiety. Perhaps time would allow students to internalize their academic improvements—therefore reducing their academic anxiety. The range of academic interventions included in this meta-analysis varied greatly (i.e., from 1 to 100 hours; see Table 2 ). We also investigated whether the instructional content areas influenced the outcomes differently. This exploratory analysis was influenced by the idea that intervention in a specific domain (e.g., math) may be more associated with reductions in anxiety than other domains (e.g., reading). Due to the nature of the corpus, our moderator analyses were restricted to examining three broad content areas: math, science, and language and literacy. The effects did not appear to vary across content areas. Given the substantial research focus on math anxiety reduction as a function of math interventions (e.g., that math skill training could reduce math anxiety; Ramirez et al., 2018 ; Supekar et al., 2015 ), and that almost half of our studies focused on math interventions, one might have anticipated larger effect sizes for both improvements in math performance and decreases in math anxiety. However, this was not evident in our findings. Limitations This review included a limited number of studies with secondary students that met our inclusion criteria. Also, there were a range of types of academic anxiety and a range of measure types, which may influence comparability of studies. Furthermore, many of the academic and academic anxiety measures in this study were unstandardized, which may have resulted in a larger overall effect size than if the measures had all been standardized ( Scammacca et al., 2015 ). Also, future research may consider psychometric information reported on the instruments when determining study quality, which we did not do and can further help with interpretation of findings ( Kim, 2009 ). Lastly, a major limitation was that to conduct our moderator analysis, we combined foreign language anxiety and reading anxiety in a foreign language into a language and literacy category. However, it should be noted that no studies that met inclusion criteria addressed reading anxiety in a first language. There is a need for reading intervention research in a first language that considers anxiety, especially because the two have been identified as related ( Francis et al., 2019 ; Grills et al., 2013 ; Macdonald et al., 2021 ; Ramirez et al., 2019 ; Zbornik & Wallbrown, 1991 ). Implications Considering that a relation between reading achievement and anxiety has been identified ( Francis et al., 2019 ), there is a need for intervention research in the area of literacy (i.e., reading) in a first language that considers anxiety outcomes. Moreover, the variety of distinct academic anxiety measures underscores the necessity for future research to create and implement standardized norm-referenced measures to enhance comparability across studies. Also, as mentioned earlier, longer academic interventions measuring the impact on academic anxiety may provide additional information regarding whether improvements in academic achievement influence academic anxiety after a significant amount of time (e.g., delayed emergence of effects). Conclusion This review provides guidance related to the association between academic achievement and academic anxiety symptoms. Our findings revealed that for secondary students, academic interventions positively affect academic outcomes but do not appear to have the same effect on academic anxiety. This finding is consistent with a prior meta-analysis with elementary students ( Fishstrom et al., 2022 ). As such, practitioners should consider that academic anxiety might need to be addressed more directly. One way to do this may be to weave anxiety management into academic interventions ( Vaughn et al., 2022 ; Francis et al., 2022 ). School-based anxiety interventions have been found to effectively lower anxiety symptoms (e.g., through cognitive behavioral therapy or self-esteem enhancements; Tutsch et al., 2019 ). These findings, along with a growing body of research on the importance of considering that anxiety might interfere with academic achievement ( Fletcher & Grigorenko, 2017 ), suggest that educators might screen for academic anxiety, especially if students are struggling academically (e.g., students with reading difficulty should be assessed for anxiety; Francis et al., 2021 ), and use this information when prescribing effective interventions ( Fletcher & Grigorenko, 2017 ). Table 6. Results of Moderator Analyses Academic outcomes Acadlemic anxiety outcomes Moderator k N ES b ( SE ) Wald tests g ( SE ) 95% CI k N ES b ( SE ) Wald tests g ( SE ) 95% CI Measure type: No control variables standardized 9 12 -- 0.51 (0.26) [−0.10, 1.11] 13 15 -- −0.23 (0.09) * [−0.43, −0.04] Not standardized 10 25 0.26 (0.31 0.76 (0.18) ** [0.35, 1.18] 6 10 0.26 (0.31) 0.20 (0.34) [−0.72, 1.11] Measure type: With control variables Standardized 9 12 -- 0.23 (0.26) [−0.45, 0.91] 13 15 -- 0.09 (0.18) [−0.39, 0.56] Not standardized 10 25 0.32 (0.43 0.55 (0.36) [−0.36, 1.46] 6 10 0.32 (0.43) 0.57 (0.39) [−0.36, 1.49] Academic content area: No control variables LL 5 6 F (2, 7.51) = 0.45, p = .66 0.76 (0.44) [−0.55, 2.07] 5 6 F (2, 7.63) = 0.18, p = .84 −0.24 (0.21) [−0.88, 0.39] Math 8 21 0.45 (0.17) * [0.03, 0.87] 8 11 −0.15 (0.18) [−0.57, 0.27] Science 5 8 0.84 (0.42) [−0.37, 2.05] 5 7 −0.02 (0.28) [−0.84, 0.79] Academic content area: With control variables LL 5 6 F (2, 7.62) = 1.06, p = .3 −0.02 (0.64) [−1.51, 1.47] 5 6 F (2, 7.26) = 0.19, p = .83 0.28 (0.51) [−0.90, 1.45] Math 8 21 −0.25 (0.35) [−1.37, 0.86] 8 21 0.05 (0.27) [−0.76, 0.87] Science 5 8 0.47 (0.42) [−0.68, 1.61] 5 8 0.19 (0.25) [−0.49, 0.87] Open in a new tab Note . Ran separate models. Control variables include research design, research quality, standardized measure, and region. k = number of studies; N ES = number of effects; b = unstandardized regression slope coefficient (moderator effect); SE = standard error; g = Hedges’s g (average pooled effect); CI = confidence interval; LL = language and literacy. * p < .05 ** p <.001. Acknowledgments This research was supported in part by grants 1R01HD087706-01A1 and P50 HD052117-07 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). The content is solely the responsibility of the authors and does not necessarily represent the official views of NICHD or the National Institutes of Health. Footnotes Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. 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