Key Takeaway: School climate is a critical component for successful school outcomes. The type of engagement occurring between students, faculty, and the community, the level of safety, and environmental factors all affect school climate. With school-wide programs focusing on specific domains, like School-wide Positive Behavior Interventions and Supports for discipline and social and emotional learning for safety, schools can change the perceptions and overall climate. – Ashley Parnell

A safe, supportive school climate is critical for school effectiveness. From teacher longevity, satisfaction, and stress to student academic achievement, problem behavior, and social-emotional health, the impact of school climate on all stakeholders is well supported by research. 

The association of school climate and key school outcomes supports the need for educators to be concerned with creating and sustaining a healthy school climate. Yet, evidence regarding ways to implement change remains limited and reviews focusing on the effects of intervention to improve school climate have not been conducted.

In this systematic review, Charlton, Moulton, Sabey, and West examined methodological quality and findings from 18 experimental studies evaluating the effects of schoolwide intervention programs on teacher and student perceptions of school climate.

Specifically, school climate refers to the comprehensive social and physical conditions, which involve three critical/core domains (DoE, 2014): 

  • Engagement. Relationships between students, teachers, families, and the broader community.
  • Safety. Schools and school-related activities where students are safe from violence, bullying, harassment, and controlled substance use.
  • Environment. Facilities, resource & technology access, teacher-student ratios, and teacher-student retention.

Researchers summarized and analyzed all available experiential research on the topic while prioritizing the highest quality literature when drawing conclusions.

Evidence identified supports the following key conclusions:

  • Careful, systematic implementation of schoolwide programs is likely to improve multiple domains of school climate, specifically the engagement and environment domains for School-wide Positive Behavior Interventions and Supports (SWPBIS) and social and emotional learning (SEL).
  • Findings suggest that programs targeting specific domains of school climate (e.g.., SWPBIS for discipline, SEL for emotion safety) seem effective in changing perceptions. 
  • School climate improvement is amenable to change. This review identified evidence supporting the malleability of school climate and the finding that schoolwide intervention can improve school climate.

While these findings are encouraging, some limitations and recommendations of the current study as they relate to: a) the quality of literature, b) definitions of independent variables, and c) measures of school climate warrant consideration. 

Summarized Article:

Charlton, C.T., Moulton, S., Sabey, C.V., West, R. (2021). A systematic review of the effects of schoolwide intervention programs on student and teacher perceptions of school climate. Journal of Positive Behavioral Interventions. 23(3), 185-200.

Summary by: Summary by: Ashley M. Parnell—Ashley strives to apply the MARIO Framework to build evidence-based learning environments that support student engagement, empowerment, and passion and is working with a team of educators to grow and share this framework with other educators.

Key Takeaway: In education, we throw around the term evidence-based quite casually, at times without the awareness of whether the evidence we rely on is empirically sound. Bringing clarity to how we ascertain veracity can support our ability to identify high-quality interventions. – Erin Madonna

In this article, Kauffman and Farkas discuss veracity as it pertains to special education, particularly around issues of policy and access to services. The authors outline two types of beliefs that drive educational decisions, Type A or scientifically verifiable beliefs and Type B, opinions or personal assertions which are not scientifically verified. The authors argue that when Type A beliefs have been established as replicable and truthful, they should be given precedence over Type B beliefs when making educational decisions.

In defining Type A and Type B beliefs, the authors provide the example of reading instruction to illustrate the difference between scientific beliefs and those based upon opinion. A Type A belief around reading is that explicit reading instruction of decoding skills works, while a Type B belief around reading is that reading emerges naturally in a literature-rich environment. We know this Type B belief as a “whole language” or a “balanced literacy” approach. The Type A belief has been verified scientifically, replicated, and is determined to be an evidence-based intervention not because we believe it to be, but because it has qualified as such through rigorous testing. This Type A belief can be challenged and reverified or debunked at any point. 

  • A Type A belief is not based on popular opinion, it is based upon the outcome of credible scientific study. 
  • The Type B belief is based upon personal testimonies and is often reinforced by the assertions of an authority figure or by a collective opinion held by a large group. It has not been exposed to the same scientific scrutiny as the Type A belief but is accepted by many because it fits with their personal opinions. This particular Type B belief is based upon flawed research which demonstrates how a Type B belief can be reinforced by data that does not meet the requirements of scientific assessment, but that is accepted anyway, becoming pseudoscience.

With the definition of Type A and Type B beliefs established, the authors go on to discuss practical applications of greater awareness around the two types of belief. “When we claim that something is evidence-based in special education, the matter of Type A belief about it—the empirical evidence—is of enormous consequence.” This is because making educational choices without empirical evidence risks, at best, neutral outcomes and, at worst, potential harm for our students. “Conformity to a personal version of belief, Type B, must not be substituted for a confirmable reality.”

The authors connect the concept of veracity with social justice when they discuss the impact of Type B beliefs on public policy, including the belief of “over-representation of certain racial or ethnic groups” in special education. With only partial veracity, amendments to the Individuals with Disabilities Act (IDEA) aimed at addressing the belief that “over-representation” is occurring have denied Black students badly needed services. “When a social policy or the fate of an individual is in question, as is often the case in special education, then Type A truth matters a great deal.” 

Educators must look beyond the fads or popular movements in education and seek out information about whether the interventions they plan to implement are based upon a Type A belief or a Type B belief. Part of this process for the individual is being willing to adjust their practice if new empirical evidence demonstrates that a previously held belief is not in fact a Type A belief. Adaptability and commitment to relying on scientific evidence provide the best opportunity for delivering a high-quality educational experience for our students. Allowing for external pressures to influence our choice of intervention without evidenced veracity is problematic. 

The authors are careful to express that Type B beliefs can positively influence education. They make clear that Type A and Type B beliefs may not always be in conflict. When one’s personal beliefs allow them to “make better sense of the objective world and/or provide moral guidance or a star to steer by,” that Type B belief can provide the motivation to advocate for special education services or improved policy. The point is not to abandon all Type B beliefs but to become conscious of how they influence our decisions as educators and to always check our Type B beliefs against available evidence before acting upon them. 

Summarized Article:

[Kauffman, J. M., & Farkas, G. (2021). Veracity in Special Education. Exceptionality, 1-14. DOI: 10.1080/09362835.2021.1938066]

Summary by: Erin Madonna—Erin philosophically aligns with the MARIO Framework’s deeply rooted conviction that all learners are capable, and she firmly believes in MARIO’s commitment to the use of evidence-based practices drawn from the field of current multidisciplinary research.

Key Takeaway: DeVries, Knickenberg, and Trygger report complex relationships between student characteristics (ie. the presence of learning differences), and self-perceived inclusion and academic self-regard. Both the novel and supported results reveal a gap, even in inclusive classes, and the need for educator and administrator-implemented inclusion interventions for at-risk students. – Emmy Thamakaison

Jeffrey DeVries (TU Dortmund University), Margarita Knickenberg (University of Bielefeld), and Maria Trygger (Saltsjöbadens Samskolan) share their cross-sectional study examining the association between student characteristics (gender, grade-level, special-education needs (SEN) status, and self-identified academic difficulties) with academic self-concept and perceptions of socio-emotional inclusion among fifth and eighth-grade students in an inclusion school. Additionally, they test the validity of the Perception of Inclusion Questionnaire (PIQ) in measuring emotional inclusion, social inclusion, and academic self-concept. 

Academic self-concept, or the way an individual regards their academic abilities, has conventionally been believed to be lower among students with SEN (ie. cognitive difficulties, learning disabilities), regardless of their inclusive educational context. DeVries et al. find this is the case not only for students with SEN diagnoses (p = 0.004), but also for students with self-reported difficulties yet no formal diagnoses (p = 0.007).

  • Grade level in combination with gender can significantly influence students’ academic self-concept. Regardless of SEN status, lower levels of self-concept were found for female students in eighth grade compared with that of female students in fifth grade. Male students, however, did not display such differences. 
  • In explaining this decline in academic self-concept, the authors cite “a decrease in maths-specific self-concept” for general female students and “different interactions with teachers and classmates”1 and “self-efficacy”2 for females with SEN. 

In terms of social and emotional inclusion, SEN status and grade were found to play an important role in determining students’ relative levels. 

  • Along with lower levels of academic self-concept, students with SEN diagnoses experienced lower levels of emotional inclusion. This cross-sectional data contradict that of a longitudinal study, which demonstrates a “boost to both emotional inclusion and academic self-concept over time” among students with SEN.3 Taken together, this suggests that “effective techniques” that address “the extent of students’ social inclusion in their classes and emotional wellbeing” may alleviate “the effects of SEN on academic self-concept “ and “emotional inclusion” over time.4,5
  • Similar to students with SEN, students with undiagnosed difficulties experienced lower levels of emotional inclusion. Interestingly, they also reported experiencing reduced social inclusion as well—a finding not seen in the SEN population. DeVries et al. suggest that this may demonstrate the comparable “lack of some inclusive support” for students with undiagnosed difficulties. 
  • Additionally, children in eighth grade reported significantly lower levels of social inclusion (p = 0.041). No significant variations due to gender were found for both social and emotional inclusion. 

Fulfilling one of the main objectives of this study, the authors provided further validation for the PIQ as an effective and easily understood tool; this 3-factor model of social inclusion, emotional inclusion, and academic self-concept was described to “demonstrate good psychometric properties,” which included measurement invariance (the extent to which items measure equivalently across different groups) and reliability. 

Ultimately, DeVries et al.‘s research provides useful insights into the relationship between student characteristics and levels of perceived socio-emotional inclusion, or academic self-concept. Much of these results (ie. students with SEN experiencing lower levels of emotional inclusion and self-concept) are supported by pre-existing research and emphasize the importance of interventions in alleviating some of the effects described above. This study’s finding of children with self-reported difficulties feeling less emotionally and socially included, as well as having a lower academic self-concept, poses some novel implications and questions; though “more research is needed to examine the exact nature and causes of these differences,” educators and administrators should “work to ensure that such at-risk learners feel included within the classroom.” 

Summarized Article:

DeVries, J. M., Knickenberg, M., & Trygger, M. (2021). Academic self-concept, perceptions of inclusion, special needs and gender: evidence from inclusive classes in Sweden. European Journal of Special Needs Education, 1–15. https://doi.org/10.1080/08856257.2021.1911523

Summary by: Emmy Thamakaison—Emmy is a recent high school graduate attending Stanford University and is an enthusiastic advocate of MARIO Framework.

Additional References:

  1. Oga-Baldwin, W. L. Q., & Nakata, Y. (2017). Engagement, gender, and motivation: A predictive model for Japanese young language learners. System, 65, 151–163. https://doi.org/10.1016/j.system.2017.01.011
  2. Huang, C. (2012). Gender differences in academic self-efficacy: a meta-analysis. European Journal of Psychology of Education, 28(1), 1–35. https://doi.org/10.1007/s10212-011-0097-y
  3. DeVries, J. M., Voß, S., & Gebhardt, M. (2018). Do learners with special education needs really feel included? Evidence from the Perception of Inclusion Questionnaire and Strengths and Difficulties Questionnaire. Research in Developmental Disabilities, 83, 28–36. https://doi.org/10.1016/j.ridd.2018.07.007
  4. Haeberlin, U., U. Moser, G. Bless, and R. Klaghofer (1989). Questionnaire for Assessing Dimensions of Integration of Students. Integration in Die Schulklasse. Fragebogen Zur Erfassung Von Dimensionen Der Integration Von Schülern FDI 4–6
  5. Hascher, T., and G. Hagenauer (2011). Schulisches Wohlbefinden Im Jugendalter– Verläufe Und Einflussfaktoren. Jahrbuch Jugendforschung: 10, 15–45.

Key Takeaway: The study explores factors that affect data-based decision making (DBDM), which has been established as an essential part to student progress, particularly for those with learning differences. The article outlines the importance of effective, frequent training to allow educators to build confidence and experience in analyzing data and transforming this data into meaningful adapted instructions for their students to ensure progress. The lack of training and universal rules of application hamper the potential of DBDM in education. —Frankie Garbutt

In this study, Oslund, Elleman and Wallace (Middle Tennessee State University) argue that to evaluate the effectiveness of “tiered instructional systems,” one must essentially rely on the correlation between frequent assessment of students with academic difficulties and educators’ skills to “make decisions using student data.”

In most states across the United States, it is legally mandated that schools implement multi-tier instructional systems. However, “data-based decision making is being adopted worldwide, yet relatively little research exists on the relations among variables impacting teachers’ ability to read, interpret, and inform instruction,” argue Oslund, Elleman, and Wallace. In their research, they analyzed teachers’ ability to interpret “student progress-monitoring data presented graphically (i.e. graph literacy).” They also investigated whether a teacher’s confidence in interpreting data, experience, or targeted pre- or inservice training on data-based decision making (DBDM) impacted their graph literacy to improve student achievement. 

In their findings they discovered the following:

  • Teacher experience had impacted their graph literacy, yet did not impact their confidence in analyzing data. 
  • Training had a large effect on teacher confidence, which confirmed previous studies referred to in the article. Professional Development “is one possible way to directly influence their confidence and potentially indirectly influence their use of data.”
  • Training increased teacher confidence “but had no impact on their assessment knowledge.” 
  • “Teachers who are skilled at DBDM are more likely to adapt instruction to meet student needs.”
  • “Unless and until teachers are properly equipped with DBDM knowledge, the effectiveness of tiered instruction may lag behind its potential.”

Therefore, the study suggested further research into what format and frequency of training would be required to increase effective use of data-based decision making.

The results of the findings also highlighted the limitations of the research. Admittedly, the data collected was “susceptible to bias” and “sample size is too small to examine differences beyond basic descriptives” relating to implementation of tier support systems within or across states. Moreover, the lack of universal rules for DBDM can result in two different teachers looking at the same graph and making different decisions.

Overall, it was concluded that “the promise of DBDM is established, but the need to further develop models and create consistency is an urgent and productive step toward increasing its effectiveness.” 

Article summarized: 

Oslund, E. L., Elleman, A. M., & Wallace, K. (2021). Factors Related to Data-Based Decision-Making: Examining Experience, Professional Development, and the Mediating Effect of Confidence on Teacher Graph Literacy. Journal of Learning Disabilities, 54(4), 243–255. https://doi.org/10.1177/0022219420972187

Summary by: Frankie Garbutt—Frankie believes that the MARIO Framework encourages students to become reflective, independent learners who progress at their own rate. 

Key Takeaway: All students should have access to a range of program options that will be appropriately challenging and help them to develop the skills, attitudes, and experience needed to be successful post-school. For some students, such as those with moderate to severe learning difficulties, this would include access to vocational preparation and work experience—highlighting the importance and continued need for dedicated special education programs in schools. —Ayla Reau 

Garry Hornby from the University of Plymouth’s Institute of Education examines which type of educational setting and programs have the best outcomes post-school for students with learning and/or behavior differences. To do so, Hornby conducted a comparative analysis of his findings from three long-term follow-up studies of students with special needs over a period of 30 years.

Generally, most countries follow some of these types of educational settings for children with special needs:

  1. “being educated in a mainstream classroom with support from a teacher’s aide; 
  2. being educated in a mainstream classroom with an additional support teacher;
  3. being educated in a special class within a mainstream school; 
  4. being educated in a segregated special school, including one attached to a mainstream school.”

Hornby was interested in the levels of inclusion achieved in their communities post-school for students who had been in special education (pull-out dedicated special education programs) and/or inclusive education interventions (full inclusion into mainstream programs). He followed three sets of students over his study period (30 years): 

  • A special education class for young people with moderate learning differences (MLD) within a mainstream secondary school in New Zealand.
  • 29 students with MLD transferred from a special education school into mainstream programs in the North of England. 
  • And students from a residential special school for children with emotional or behavioural difficulties (EBD) in New Zealand.

To evaluate the effectiveness of the education provided in these different settings, Hornby needed evidence from all stakeholders involved. 

  • Parents: Hornby concludes that parents are neither overwhelmingly for nor against the practice of inclusion into mainstream education. 
  • Teachers: When looking at “teachers’ attitudes toward inclusion and their views regarding the extent to which they can effectively provide for children with learning or behavioural difficulties in their classes,” Hornby concludes that many teachers have a critical view of inclusion and advocate for the necessity for special education expertise and teacher training in this area. 
  • Students: Hornby found that students who had attended a residential special school for children with emotional and behavioral difficulties were overwhelmingly positive about their experiences. However, students who started their education in a special education program/school and then enrolled in a mainstream school were consistently negative about their experiences.

Overall, his findings suggest that students who completed their education in a special education setting, as opposed to a mainstream school, yielded better outcomes post-school. The success could be attributed to the vocational curriculum and work experience offered to students in special education settings/programs in the years leading to their transitioning out of school. “This suggests that a policy of full inclusion, with the closure of special classes and special schools, will result in less community inclusion post-school for young people with moderate to severe levels of learning or behavioural difficulties.” 

Ultimately, “effective specialized instruction, vocational curricula and work experience, as part of a planned transition from school to post-school life, are of greater importance for optimizing outcomes for young people with moderate to severe levels of learning or behavioral difficulties than simply being included in mainstream secondary schools that are attempting to be as inclusive as possible.”

Hornby does recognize that his finding derived from three studies that were small in scale. They were conducted without the use of control or comparison groups and, to some extent, relied on the interpretations of the author himself. It is important to note that the findings should be viewed tentatively and more studies should be conducted before definitive conclusions are made.  

Summarized Article:

Hornby, G. Are Inclusive Education or Special Education Programs More Likely to Result in Inclusion Post-School? Educ. Sci. 2021, 11, 304. https://doi.org/10.3390/educsci11060304 

Summary by: Ayla Reau—Ayla is excited to help continue to grow the MARIO Framework, seeing the potential for it to impact all students across any educational context.

Research author Garry Hornby, Ph.D., contributed to the final version of this article.

Key Takeaway: Currently, there are many sound, evidence-based reading comprehension interventions. However, not all students will demonstrate an adequate response to these interventions. Therefore, as special educators, we need to be aware of how and why students respond to reading comprehension interventions and how attention affects reading comprehension. —Michael Ho

In their study, Amanda Martinez-Lincoln, Marcia A. Barnes, and Nathan H. Clemens (2021) used moderation analysis to investigate for whom and under what conditions reading comprehension interventions are most effective. The authors investigated the following research question: Do language status and pre-intervention levels of anxiety, mind-wandering, and mindset influence the effects of a computer-delivered or teacher-delivered inferential reading comprehension intervention in struggling middle school readers?

The study aimed to:

1) Determine whether students’ mind-wandering, anxiety, and language status were associated with a differential response to an inferential reading comprehension intervention among struggling middle school readers

2) Examine whether these effects varied across instructional delivery systems: teacher-led instruction, computer-led instruction, and a control group (program based on what struggling middle school readers typically receive)

Here are the major takeaways from the article:

  • Inference-making, the ability to infer information that is not explicitly stated in the text, is a vital component to reading comprehension. Difficulties in making inferences to connect parts of the text1 and to associate texts with background knowledge2 have been linked to poor reading comprehension. 
  • Attention is a core component of engagement and is crucial to academic achievement, including reading comprehension. Martinez-Lincoln et al.  (2021) refer to the 2016 study of Rabiner et al.3 and emphasize that “poor attention can negatively influence students’ long-term academic outcomes in reading and math and can increase risk for not graduating from high school.”
  • The purpose of this study was to test the effects of three factors of attention—Mind-Wandering, Anxiety, and Mindset—across three instructional delivery systems in reading: Teacher-led Instruction, Computer-led Instruction, and a Control Group.
  • In the study, 67 students in Grade 6 to 8 from three middle schools in the southwest USA were included. A stratified randomized procedure was implemented and students were assigned to one of the three groups: Teacher-led Instruction, Computer-led Instruction, and a Control Group.
  • Measures in reading assessment included Test of Word Reading Efficiency 2nd ed., Sight Word Efficiency, Connect-IT Inferential Reading Comprehension Assessment, Bridging Inference Task, and the Wechsler Individual Achievement Test 3rd ed., Reading Comprehension. Measures in attention included Mind-Wandering Questionnaire, Multidimensional Anxiety Scale for Children 2nd Edition, and Mindset Survey.
  • Among students with similar high levels of mind-wandering, students in the computer-delivered intervention were able to make better inferences on the Bridge-IT Near task, a part of the Bridging Inference Task. Mind-wandering did not have an effect in the teacher-led intervention; this may be due to verbal praise and encouragement reducing the influence of mind-wandering in this group.
  • Compared to similarly anxious peers in the control group, Martinez-Lincoln et al., (2021) found that “students in the computer-led intervention performed better on a comprehension test that required them to make several different types of inferences.” It is important to note that higher levels of anxiety were positively correlated with higher levels of reported mind-wandering.
  • The effects of mindset on inferential reading comprehension intervention were found to be similar. This could be due to the small sample size or to the general mindset measures not being as sensitive as reading-specific mindset measures.
  • Martinez-Lincoln et al. (2021) found that English Learners (ELs) “scored lower overall than non-ELs on all reading measures.” ELs scored higher in the control group compared to ELs in the computer-led instruction. More notably, in the teacher-led instruction, the ELs’ performance did not significantly differ from those of non-ELs. This may be due to more in-depth feedback and additional examples provided in the teacher-led instruction.
  • This study had some limitations, such that the sample size was relatively small and that it was not realistic to include and control all of the factors that may influence students’ responses to reading instruction. In addition, participants read and answered the student engagement questionnaires silently. Although an interventionist was present, it is possible that a student may have misread or misunderstood the statements in the questionnaire. Finally, not all students were receiving reading instruction in the control group. 
  • The inclusion of student characteristics and instructional elements, such as group size and delivery by a computer or a teacher, in future research may be essential for developing effective reading comprehension instruction for struggling middle school readers, especially those who are ELs, have high levels of mind-wandering, or have high levels of anxiety.

Summarized Article:

Martinez-Lincoln, A., Barnes, M.A. & Clemens, N.H. Correction to: Differential Effectiveness of an Inferential Reading Comprehension Intervention for Struggling Middle School Readers in Relation to Mind-wandering, Anxiety, Mindset, and English Learner Status. Ann. of Dyslexia 71, 346 (2021). https://doi.org/10.1007/s11881-021-00215-3

Summary by: Michael Ho—Michael supports the MARIO Framework because it empowers learners to take full control of their personalized learning journey, ensuring an impactful and meaningful experience.

Additional References:

  1. Cain, K., & Oakhill, J. V. (1999). Inference making ability and its relation to comprehension failure in young children. Reading and Writing, 11, 489–503. https://doi.org/10.1023/A:1008084120205.
  2. Cain, K., Oakhill, J. V., Barnes, M. A., & Bryant, P. E. (2001). Comprehension skill, inference-making ability, and their relation to knowledge. Memory & Cognition, 29, 850–859. https://doi.org/10.3758/BF03196414.
  3. Rabiner, D. L., Godwin, J., & Dodge, K. A. (2016). Predicting academic achievement and attainment: the contribution of early academic skills, attention difficulties, and social competence. School Psychology Review, 45, 250–267. https://doi.org/10.17105/SPR45-2.250-267.

Key Takeaway: Across three studies, students’ belief in a growth mindset only predicted increased engagement in math learning for those students who also had sufficient metacognitive skills to monitor their own learning.  Thus, metacognitive skills, when paired with a growth mindset, provide complementary skill sets and may be particularly beneficial for students in low socioeconomic school settings. However, the impact of these interventions could vary depending on contextual factors, such as socioeconomic status and teacher-student relationships, and should be taken into consideration. —Kristin Simmers 

In their article, “More Than Growth Mindset: Individual and Interactive Links Among Socioeconomically Disadvantaged Adolescents’ Ability Mindsets, Metacognitive Skills, and Math Engagement,” Wang et. al (2021) (University of Pittsburgh) emphasize the following key ideas in relation to Self-Regulation: 

  • Self-Regulated Learning (SRL) shows motivation can help learners; however, metacognitive skills are likely needed for students to fully engage with learning and monitor their overall progress.
  • Recent research suggests the impact of growth mindset may be context specific. Students from low socio-economic status (SES) contexts are more likely to demonstrate fixed mindsets about academic ability and are more likely to benefit from developing growth mindsets. 
  • If students lack sufficient metacognitive skills, a growth mindset alone may not increase learner engagement. As Wang et. al states, “Metacognitive skills may be necessary for students to realize their growth mindset.”
  • Positive teacher-student relationships are likely a significant factor in supporting the development of metacognitive skills and a growth mindset, as well as promoting academic engagement. 
  • Teachers should create environments that support metacognition and growth mindset within their specific contexts. 

Self-Regulated Learning (SRL)

To help further understand the lens of SRL in the context of metacognition and growth mindset, Zimmerman and Moylan’s (2009)1 SRL model proposes three phases of the learning process: forethought (before learning), performance (during learning), and reflection (after learning). In this model, metacognition is present in each stage, and it is plausible that students who are metacognitively able to monitor their learning process may also be more motivated to persevere and demonstrate a growth mindset. Conversely, if a student does not have sufficient metacognitive skills, simply believing in a growth mindset may not significantly improve student learning engagement. 

Math Metacognitive Skills & Growth Mindset

Flavell (1987)2 defines metacognition as the awareness and regulation of one’s thoughts, and Zimmerman & Moylan (2009)1 identify planning, monitoring and evaluating as three skills generally involved in metacognitive regulation. Meanwhile, Dweck (2000)3 defines growth mindset as a belief that intelligence is malleable, rather than fixed. Thus, the study shared in the article suggests that motivation may be beneficial to students, but metacognitive skills are also likely needed in order for students to optimally engage with math learning.4

Ultimately, academically vulnerable students may particularly benefit from metacognition & mindset interventions.4,5 

Summarized Article

Wang, M. T., Zepeda, C. D., Qin, X., Del Toro, J., & Binning, K. R. (2021). More Than Growth Mindset: Individual and Interactive Links Among Socioeconomically Disadvantaged Adolescents’ Ability Mindsets, Metacognitive Skills, and Math Engagement. Child Development. https://doi.org/10.1111/cdev.13560

Summary By: Kristin Simmers—Kristin supports the MARIO Framework’s efforts to connect teachers and researchers to improve student learning.

Additional References:

  1. Zimmerman, B. J., & Moylan, A. R. (2009). Self-regulation: Where metacognition and motivation intersect. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 299–316). New York, NY: Routledge. 
  2. Flavell, J. H. (1987). Speculation about the nature and development of metacognition. En F. Weinert y R. Kluwe (Eds.), Metacognition, motivation, and understanding (pp. 21-29).
  3. Dweck, C. S. (2000). Self-theories: Their role in motivation, personality, and development. East Sussex, UK: Psychology Press.
  4. Rosenzweig, E. Q., & Wigfield, A. (2016). STEM motiva- tion interventions for adolescents: A promising start, but further to go. Educational Psychologist, 51, 146–163. https://doi.org/10.1080/00461520.2016.1154792 
  5. Schneider, W., & Artelt, C. (2010). Metacognition and mathematics education. ZDM-International Journal on Mathematics Education, 42 (2), 149–161.

Key Takeaway: Moriña & Biagiotti (2021) have completed a systematic review of literature to identify a number of key personal and external factors that help students with disabilities be successful at university:

  • Personal factors include “self-advocacy, self-awareness, self-determination, self-esteem and executive functioning” 
  • External factors include “family, disability offices, staff and faculty members, and peers”

Identifying these internal and external factors can help universities ensure that they have the necessary resources in place to support students with disabilities. Additionally, knowing these factors can help students with disabilities make informed decisions as to their choice of university. —Matt Barker

Moriña & Biagiotti (2021) from the Universidad De Sevilla identify that there is a move from focusing on facilitating access to education to focusing on improving the quality of learning, and that this shift requires “education systems to guarantee equitable access and permanence, resources, and teaching and learning processes for all.” Although there is improving access to higher education (HE), this has also resulted in challenges with increasing access for non-traditional students.1,2 The result is that university dropout rates are higher among students with disabilities than among other students and that “the former face multiple barriers to staying and successfully completing their studies.”3,4

Kutcher and Tuckwillet (2019)5 identify the following internal factors for academic success: “setting clear objectives, being proactive, knowing how to make decisions and not give up in the face of difficulties, using strategies that can help with the disability itself and believing in one’s abilities.” Moriña & Biagiotti (2021) further cite Gow, Mostert, and Dreyer (2020)6 and Milsom and Sackett (2018),7 who identify “self-determination, self-advocacy, self-awareness, self-discipline, self-esteem and executive functions” as common traits among students with disabilities who are able to successfully finish their studies. Russak and Hellwing (2019)8 in their study added that graduates saw their disability as part of their self-image, one that enabled them to learn about their strengths and weaknesses. 

Additionally, external factors are those that have a source of support external to the individual. Gow, Monster, and Dreyer’s (2020)6 study recognises that support from family and friends is critical. Cotán et al. (2021)9 identify staff and faculty who have provided “support, understanding and compassion” have helped the students be successful. Orr and Goodman (2010)10 recognise that peers help the students set goals and can support access to academic resources. Kutcher and Tuckwillet (2019)5 also identify that “high expectations, accessible campuses, appropriate accommodations and administrative support” are all factors that support academic success for students with disabilities. 

The authors identify six personal factors and traits of students with disabilities who are demonstrating success at university:

  • Self-advocacy
  • Self-awareness
  • Self-determination
  • Self-discipline
  • Self-esteem
  • Executive functioning

The authors also identify five external factors influencing the academic success of students with disabilities:

  • Family support (“moral, financial and social”)
  • The university
  • The impact of disability support services
  • The effectiveness of academic support staff and faculty
  • Peers

Identifying these internal and external factors can help universities ensure that they have the necessary resources in place to support students with disabilities. Additionally, understanding these factors can help students with disabilities make informed decisions as to their choice of university. As the authors note, “when people have a range of personal skills and institutions provide the necessary opportunities, it is possible for students with disabilities to remain and succeed academically.”

Furthermore, the authors note that academic success is dependent “on factors related to the personal, contextual and external environments.” The students in the studies who persisted in their goals saw themselves as having a sense of “freedom and independence.” Disability was regarded as an opportunity to overcome challenges and develop resilience, with the goal of gaining work post graduation. 

Given the six personal factors and traits of students with disabilities who are demonstrating success at university, Moriña & Biagiotti (2021) note the importance of preparing the students in these competences before they attend university, as well as whilst they are at university, since “such competences are essential to access and have educational, social and working success.” Additionally, the authors stress that both disciplinary and personal competences need to be developed, possibly through “active and student centred-teaching methodologies, such as cooperative learning, projects and case studies.”

In terms of university based support, the authors explain that “coaching, tutoring, accommodations and disability services . . . improve the quality of education and enhance the psychosocial well-being of students.” Additionally, it is noted that the application of Universal Design for Learning to offer multiple means of expression, representation and involvement should also be explored as a means to enhance inclusion practices.11 It is thus important for faculty to have training in inclusive practices. 

Summarized Article:

Moriña, A., & Biagiotti, G. (2021). Academic success factors in university students with disabilities: a systematic review. European Journal of Special Needs Education, 1-18.

Summary by: Matt Barker—Matt loves how the MARIO Framework empowers learners to make meaningful choices to drive their personalized learning journeys.

Additional References:

  1. Carballo, R., B. Morgado, and M. D. Cortés-Vega. 2021. “Transforming Faculty Conceptions of Disability and Inclusive Education through a Training Programme.” International Journal of Inclusive Education 25 (7): 843–859 doi:10.1080/13603116.2019.1579874.
  2. Fernández-Gámez, M. A., P. Guzmán-Sánchez, J. Molina-Gómez, and P. Mercade-Mele. 2020. “Innovative Interventions and Provisions of Accommodations to Students with Disabilities.” European Journal of Special Needs Education 1–10. doi:10.1080/08856257.2020.1792715.
  3. Bell, S., C. Devecchi, C. M. Guckin, and M. Shevlin. 2017. “Making the Transition to Post-secondary Education: Opportunities and Challenges Experienced by Students with ASD in the Republic of Ireland.” European Journal of Special Needs Education 32 (1): 54–70. doi:10.1080/08856257.2016.1254972.
  4. Munir, N. 2021. “Factors Influencing Enrolments and Study Completion of Persons with Physical Impairments in Universities.” International Journal of Inclusive Education 1–16. doi:10.1080/13603116.2021.1879959.
  5. Kutcher, E. L., and E. D. Tuckwillet. 2019. “Persistence in Higher Education for Students with Disabilities: A Mixed Systematic Review.” Journal of Diversity in Higher Education 12 (2): 136–155. doi:10.1037/dhe0000088.
  6. Gow, M. A., Y. Mostert, and L. Dreyer. 2020. “The Promise of Equal Education Not Kept: Specific Learning Disabilities – The Invisible Disability.” African Journal of Disability 9 a647. doi:10.4102/ajod.v9i0.647.
  7. Milsom, A., and C. Sackett. 2018. “Experiences of Students with Disabilities Transitioning from 2-year to 4-year Institutions.” Community College Journal of Research and Practice 42 (1): 20–31.doi:10.1080/10668926.2016.1251352.
  8. Russak, S., and A. D. Hellwing. 2019. “University Graduates with Learning Disabilities Define Success and the Factors that Promote It.” International Journal of Disability, Development and Education 66 (4): 409–423. doi:10.1080/1034912X.2019.1585524.
  9. Cotán, A., A. Aguirre, B. Morgado, and N. Melero. 2021. “Methodological Strategies of Faculty Members: Moving toward Inclusive Pedagogy in Higher Education.” Sustainability 13 (6): 3031. doi:10.3390/su13063031.
  10. Orr, A. C., and N. Goodman. 2010. “People like Me Don’t Go to College: The Legacy of a Learning Disability.” Journal of Ethnographic and Qualitative Research 4 (4): 213–225. https://eric.ed.gov/? id=EJ902542 .
  11. Fleming, A. R., W. Coduti, and J. T. Herbert. 2018. “Development of a First Year Success Seminar for College Students with Disabilities.” Journal of Postsecondary Education and Disability 31 (4): 309–320. https://eric.ed.gov/?id=EJ1214190 .

Key Takeaway: Teacher language within general and special education classrooms differs for students with autism, resulting in potentially negative impacts. Numerous studies have shown that open-ended questioning and language-rich environments are linked to positive academic achievement and communication development, especially for students with disabilities like autism who may struggle in these areas. —Amanda Jenkins

By analyzing six types of teacher language (open-ended questions, language models, close-ended questions, directives, indirect requests, and fill-ins), Sparapani et al. (2021) found that teachers generally use more directives and close-ended questions when interacting with students with autism, “potentially limiting their opportunities to engage in rich exchanges that support learning and development.”  

The study looked at teacher language in kindergarten to 2nd grade general and special education classrooms and found that while special education classrooms had more language usage overall, both settings had language that consisted primarily of close-ended questions and directives (69% in special education classes, 60% in general education). Open-ended questions were rarely asked in either setting to students with or without autism. Numerous studies and research have shown open-ended questioning fosters active engagement, improves communication skills, decreases problem behaviors, and increases academic growth. 

As Sparapani et al. state, “These data might suggest a need for teachers to include scaffolds, modifications, materials, and/or other adaptations into classroom activities rather than rely on oral language, such as the use of directives and/or close-ended questions, for students with limited language and lower cognitive skills.” More research and development needs to be done to provide teachers with an understanding of the impact their language and questioning practices have on their students.

The authors also indicated that teacher language is related to the individual student’s symptom severity, vocabulary skills, and cognitive ability. The study used multiple standardized tests to determine base-line levels of functioning and skills of the individual participants. Then the researchers focused on the individual student experiences in general and special education settings through the use of video observations and analysis. In both settings, students exhibiting more severe autism symptoms were addressed with mostly directives and significantly less open-ended questions. Special education teachers were more likely to address individual students and general education teachers addressed students in groups more often. As Sparapani et al. state in the findings, “the language environment within special education classrooms may not adequately prepare students for the linguistic and social pragmatic directives within general education classrooms . . . [and] may create an instructional barrier for learners with autism who transition between settings.”  

As special education policy focuses on creating a least restrictive environment and as inclusion/collaborative classroom models increasingly become the norm, students with autism are spending more of their academic time in the general education setting.  This study highlights that it is the teachers and paraprofessionals responsibility to monitor the language used in their teaching practices and to ensure a language-rich classroom experience. Best practices, such as using open-ended questioning and language models, give all students the opportunity to develop academic and communication skills vital to success.

Summarized Article:

Sparapani, N., Reinhardt, V. P., Hooker, J. L., Morgan, L., Schatschneider, C., & Wetherby, A. M. (2021). Evaluating Teacher Language Within General and Special Education Classrooms Serving Elementary Students with Autism. Journal of Autism and Developmental Disorders. Published. https://doi.org/10.1007/s10803-021-05115-4

Summary by: Amanda Jenkins—Amanda strives to help students effectively communicate their strengths, weaknesses, and goals, and believes the MARIO Framework provides the structure and foundational skills for students to take ownership of their learning, inside and outside of school.

Key Takeaway: In the past two years, education all over the world has been forced to adapt and embrace online learning. Students and teachers alike had to become more proficient in using technology—some navigating with ease, and others finding it more challenging. However, just as educator presence and student self-efficacy is important and impactful in the classroom, these two factors are also crucial to successful online learning. —Nika Espinosa

Lim et al.’s (2021) study, “Making online learning more satisfying: The effects of online-learning self-efficacy, social presence, and content structure” is the first to consider how social presence may matter more when learners have lower online learning self-efficacy and, separately, when the content is less structured. Here, the authors analysed readily available research on topics such as online learning, learning satisfaction, social presence, and online learning efficacy to help guide their hypotheses and research questions. 

This study was conducted with university students in Singapore. In order to establish variables, the researchers focused on a single discipline, manipulated instructor presence through the use of vocal tone, and utilized the life events of a historical figure, which provided the authors with both structured and unstructured content. The authors also used four different videos that included one of the following factors: 

  • high instructor presence and structured content
  • low instructor presence and structured content
  • high instructor presence and unstructured content
  • low instructor presence and unstructured content 

The authors measured variables using 7-point scales, adapted to fit the context. The different hypotheses and research question studied are listed below:

  • Hypothesis 1 (H1): Online learning satisfaction is higher when instructor presence is high versus low.
    • The results show that there is a positive correlation between high instructor presence and online learning satisfaction, which is consistent with studies already published. It is clear that the students appreciated social presence during the lesson, especially when the lessons are unstructured. Lim et. al quotes Rosenthal and Walker (2020).1 and Wilson et.al (2018),2 “Instructor presence does not necessarily lead to more learning, but students have greater preference and liking of online formats with higher levels of instructor presence and find it easier to pay attention to those formats.”
  • Hypothesis 2 (H2): Online learning self-efficacy is positively associated with online learning satisfaction.
    • The authors also found that students with high online self-efficacy were observed to have more learning satisfaction. The consideration to develop online learning efficacy in students also aligns with the findings of Artino (2008),3 Lim (2001),4 and Womble (2007).5 
  • Hypothesis 3 (H3): The effect of instructor social presence on learning satisfaction is more positive for students with lower online learning self-efficacy.
    • The third hypothesis, however, did not prove to be statistically significant. Again, this connects to considerations for developing online learning self-efficacy in students in order to increase learning satisfaction. 
  • Hypothesis 4 (H4): The relationship between instructor presence and learning satisfaction is more positive for unstructured content than for structured content.
    • “The pedagogical takeaway here is that, even with highly structured content, instructor presence can enhance the learning experience, but it has more benefit for less structured content.” 
  • Research Question 1: Does learning satisfaction differ between unstructured and structured content?
    • The researchers found that there was no difference in learning satisfaction between the differences in content, and this could be attributed to different learning styles and preferences of students.

In conclusion, the findings suggest that we need to develop learner online self-efficacy and enhance instructor presence during online learning in order to develop self-directed learners that will benefit greatly from virtual lessons. Just as we develop our students’ self-efficacy and acknowledge the importance of our social presence during face-to-face learning, as the world continues to shift and technology becomes more prominent, we need to consider further enhancing our pedagogical practices for online learning.

Summarized Article:

Lim, J. R. N., Rosenthal, S., Sim, Y. J. M., Lim, Z.-Y., & Oh, K. R. (2021). Making online learning more satisfying: The effects of online-learning self-efficacy, social presence, and content structure. Technology, Pedagogy and Education, 1–14. https://doi.org/10.1080/1475939x.2021.1934102

Summary by: Nika Espinosa – Nika believes that personalized learning is at the heart of special education and strives to collaborate with educators in providing a holistic, personalized approach to supporting all learners through the MARIO Framework.

Research author Sonny Rosenthal, Ph.D., was involved in the final version of this summary.

Additional References:

  1. Rosenthal, S., & Walker, Z. (2020). Experiencing live composite video lectures: Comparisons with traditional lectures and common video lecture methods. International Journal for the Scholarship of Teaching and Learning, 14(1), A08. https:// doi.org/10.20429/ijsotl.2020.140108
  2. Wilson, K. E., Martinez, M., Mills, C., D’Mello, S., Smilek, D., & Risko, E. F. (2018). Instructor presence effect: Liking does not always lead to learning. Computers & Education, 122, 205–220. https://doi.org/10.1016/j.compedu.2018.03.011
  3. Artino, A. R. (2008). Motivational beliefs and perceptions of instructional quality: Predicting satisfaction with online training. Journal of Computer Assisted Learning, 24(3), 260–270. https://doi.org/10.1111/j.1365-2729.2007.00258.x
  4. Lim, C. K. (2001). Computer self-efficacy, academic self-concept, and other predictors of satisfaction and future participation of adult distance learners. American Journal of Distance Education, 15(2), 41–51. https://doi.org/10.1080/08923640109527083
  5. Womble, J. C. (2007). E-learning: The relationship among learner satisfaction, self-efficacy, and usefulness. Alliant International University. https://www.learntechlib.org/p/119496