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:  Students with a deep approach to learning tend to have character traits associated with openness, conscientiousness, and a “steady temperament.” Educators can focus on fostering these traits in the classroom to increase students’ self-awareness and self-management skills, which students use to motivate themselves, set achievable personal and academic goals, and develop a growth mindset. —Shekufeh Monadjem

In the first study of its kind, the author Paulo Moreira, together with a group of researchers, investigated how different personality traits influenced students’ attitudes towards learning. The research was conducted with a study group of 686 adolescents with different approaches to learning.  

Two major approaches to learning were identified—the deep approach and the surface approach. 

The Deep Approach: “When a student adopts a deep approach to an academic task, this is to say that their underlying guiding intention is to maximize intellectual understanding and extract meaning from the task. There is also an intrinsic motivation to learn.” The qualities of openness, conscientiousness, and a “steady temperament” have also been linked to personalities that show a deep approach. Studies have shown a positive association between the deep approach and academic performance.1,2 

The Surface Approach: Academic performance is typically lower in those students who display a surface approach to their learning.3.4 “When a student adopts a surface approach, the guiding motivation is extrinsic to the task. The resulting strategies for a given task under this approach, such as rote learning, are characterized by low investment and low effort.”

Furthemore, other traits were identified in the study group:

  • Novelty seeking—seeking new experiences with intense emotional sensations
  • Harm avoidance—a tendency to respond intensely to negative stimuli
  • Reward dependance—a positive response and maintenance of behaviour in response to rewards
  • Persistence—the tendency to continue with a behaviour despite the absence of a reward

Students identified as having a deep approach to learning showed low harm avoidance, low novelty seeking, and high persistence, as well as high cooperativeness and high self-directedness. Whereas, those that adopted a surface approach to their learning showed an opposite pattern of high harm avoidance and low self-directedness as well as neuroticism. These self-regulatory aspects of personality are important for helping students gain a more adaptive approach to learning. 

Students showing high persistence in their personalities were also found to be “ambitious, enthusiastic, and tireless overachievers.”5 

Because character is changeable, it can be developed and improved with the help of interventions to gain a more mature outlook. Adolescents with a mature character might be described as “responsible, resourceful, socially tolerant, empathic, principled, patient, and creative.”6 “Consequently, one practical implication of the study is that teachers and schools may be able to use character-development interventions with certain types of students, (i.e., those with a steady temperament profile) to encourage more adaptive approaches to learning and their associated positive academic outcomes.”

Mindfulness-based interventions are also an option that can be used to influence students to strengthen self-esteem and sense of mastery (i.e., self-directedness). Likewise, the results of the study also suggested that different types of interventions would be effective for students with different personality types. Educator awareness of character traits associated with deep learning allows for evidence-informed interventions focusing on fostering these traits to be harnessed in the classroom. 

Article Summarized

Moreira, P. A., Inman, R. A., Rosa, I., Cloninger, K., Duarte, A., & Robert Cloninger, C. (2021). The psychobiological model of personality and its association with student approaches to learning: Integrating temperament and character. Scandinavian Journal of Educational Research, 65(4), 693-709.

Summary by: Shekufeh Monadjem—Shekufeh believes that the MARIO Framework builds relationships that enables students to view the world in a positive light as well as enabling them to create plans that ultimately lead to their success.

Additional References:

  1. Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. https://doi.org/10.1037/a0026838
  2. Watkins, D. (2001). Correlates of approaches to learning: A cross-cultural meta-analysis. In R. J. Sternberg, & L. F.
  3. Diseth, A. (2003). Personality and approaches to learning as predictors of academic achievement. European Journal of Personality, 17(2), 143–155. https://doi.org/10.1002/per.469 
  4. Diseth, A. (2013). Personality as an indirect predictor of academic achievement via student course experience and approach to learning. Social Behavior and Personality, 41(8), 1297–1308. https://doi.org/10.2224/sbp.2013.41.8. 1297
  5. Cloninger, C. R., Zohar, A. H., Hirschmann, S., & Dahan, D. (2012). The psychological costs and benefits of being highly persistent: Personality profiles distinguish mood disorders from anxiety disorders. Journal of Affective Disorders, 136(3), 758–766.
  6. Cloninger, C. R. (2004). Feeling good: The science of well-being. Oxford University Press.