Knowledge construction refers to the ways that students solve problems and construct their own understanding of concepts, phenomena, and situations. In other words, how students learn. The current understanding of knowledge construction in game-based learning environments is limited. While studies have linked the adoption of mobile serious games (digital games for learning) and improvements in learning performance and student engagement few have conclusively shown an improvement in learning outcomes. 

The authors wanted to specifically examine what knowledge-construction behaviors are exhibited by elementary school students when using serious games and how these behaviors differ across academic performance levels.

The Phases of Knowledge Construction

Academics in the field typically divide knowledge construction behavior into phases or types. The IAM model used by the researchers follows the following five phases: 1) sharing or comparing of information about problems 2) discovery and exploration of dissonance or inconsistency among ideas 3) negotiation of meaning or co-construction of knowledge 4) testing and modification of proposed syntheses or co-construction; 5) agreement statements, or applications of newly constructed meanings. Typically, knowledge construction behaviors are low among elementary school students since they are still developing self-regulation skills and have relatively weaker abstract thinking abilities.

Skills Necessary for Knowledge Construction

The study had 83 participants in classes across third, fifth, and sixth grade in an urban public elementary school in Beijing, China. All participants had more than two years of prior mobile technology-enhanced classroom learning experience. The authors and researchers developed an app that would provide a “personalized, game-like and task-driven self-paced learning environment” about the Chinese mid-Autumn festival to collect the needed data. The app was implemented as a self-paced learning material for four weeks and participants were encouraged to go explore in classes. Teachers were present in the room but did not deliver any lectures. 

Performance groups were decided based on participants’ overall accuracy rates when using the app, the high-performing group included the top 25% of students, while the low-performing group the bottom 25%. Differences between the two groups were then analyzed. “The students showed a clear capacity to regulate their learning in a mobile serious game environment.” They demonstrated agency, self-monitoring, and self-evaluation skills. “Results also indicated that, if coupled with feedback, a simple game-like design can empower children to construct their knowledge independently.” 

The data also illustrated an interesting difference between the two performance groups. The low-performing group rarely studied or re-studied learning material after they answered a question incorrectly. Whereas the high-performing group tended to go back to try to renegotiate meaning and re-constructed knowledge to modify errors in previous understandings. The low-performing group also tended to watch learning materials repeatedly, getting stuck in a negotiating-of-meaning cycle as they tried different answers again and again.

Creating Systems To Identify Learning Patterns

Students can self-regulate their learning, as early as elementary school, without intervention by teachers. However, low-performing students may need to adjust their learning strategies around self-monitoring and self-evaluation when in self-paced environments. Designers of such technology can facilitate this by creating systems that can identify certain learning patterns and alert users about them. In addition, they could add app features that facilitate social interaction so that students can engage in collective and shared regulation of learning.

Notable Quotes: 

“One limitation of empirical measurement of learning-behavior patterns is that it cannot capture how students learn in technology-enhanced environments.” 

“To engage in technology-enhanced self-regulated learning effectively individuals must be able to make reasonable determinations of what, when, and how to learn.” 

“…when students used self-monitoring record forms right after they started their learning and before they completed it, their learning outcomes and motivation both increased.”

Personal Takeaway

Students, regardless of age, are capable of self-regulated learning and can construct knowledge through independent self-paced learning. Given that self-regulation and self-directed learning is a continuum, educators may still need to provide support to some students. This could be achieved through explicit instruction in self-monitoring and self-evaluation skills to aid the student in reflecting on their learning process.

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Ayla Reau

Summarized Article:

Sun, Z., Lin, CH., Lv, K. et al. Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences. Education Tech Research Dev 69, 533–551 (2021). https://doi.org/10.1007/s11423-021-09938-x.

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The purpose of the current study was to identify self-regulated learning profiles among middle school students and to investigate whether these profiles related to multiple indicators of academic success and regulatory engagement in mathematics.

What is Self-Regulated Learning?

“Self-regulated learning (SRL) refers to a process of managing one’s thoughts, actions, and environment during learning or pursuit of goals.” SRL processes include goal-setting, planning, monitoring, and reflecting on one’s learning. Substantial research supports a positive relationship between these SRL processes, student achievement, and academic skills. Furthermore, SRL theorists have also posited that SRL is a context- and task-specific phenomenon. In other words, SRL can be influenced by contextual factors (e.g., quality of instruction and teacher support) and students’ perceptions of those contexts.

Measuring Levels of Self-Regulated Learning  

Three hundred and sixty-three middle school students participated in this study. Students completed self-report inventories and scales to measure perceived use of regulatory strategies, self-efficacy beliefs to engage in SRL, perception of teacher support, and feelings of connection with the school. Based on the results, researchers identified five cluster groups/profiles varying across two dimensions (i.e., SRL and perceived contextual supports) in the math classroom. Researchers then examined whether these profiles (i.e., High SRL- High Support, Solid SRL- Low Support, Low SRL- Supported, Very Low SRL- Low Support) differentially predict class engagement (i.e., measured by teacher rating scales) and math achievement (i.e., measured by report cards and standardized test scores). Students who do not feel supported or connected to school contexts and who demonstrate weak SRL skills (i.e., strategic & motivational) exhibited low levels of SRL in the classroom and were more likely to exhibit poor academic performance as reflected on standardized tests. On the other hand, students who reported frequent use of SRL skills, regardless of their level of perceived teacher support, exhibited stronger mathematics grades than those who did not frequently use SRL strategies.

Regardless of the level of support or connections that students felt in school, the groups who reported engaging more frequently in strategic and motivated behaviors for work outside of school were more likely to display adaptive SRL within the classroom, based on teacher reports.

Predictors of Student Behaviour and Academic Performance  

Research on the whole suggests that perceptions of the learning environment affect learners’ school-based functioning, but identifies SRL skills and motivational beliefs as stronger predictors of student behavior and academic performance.To best support learners who struggle in school, practitioners must understand the factors that most directly influence achievement while also recognizing that many of these factors concurrently operate and intersect within individuals in particular contexts. 

Given that SRL is a malleable, context-specific phenomena, efforts should be made to identify students exhibiting less adaptive function across both SRL skills and perceived contextual support and then provide relevant support targeting both dimensions  for these students. This is particularly important in an online learning environment, which necessitates a higher level of student responsibility and may increase the likelihood that students feel socially isolated and disconnected from teachers and others.

Notable Quote: 

“When viewed collectively, research suggests that students’ perceptions of learning contexts and teacher support are important when assessing students’ school‐based functioning, but that SRL and specific motivational beliefs may play a larger role in student behavior and academic performance.”

Personal Takeaway

Self-directed learning involves motivational, strategic, and contextual factors which uniquely and interactively influence learner achievement and engagement. An understanding of these dynamic influences and student profiles will help practitioners recognize the most at-risk students and, in turn, provide relevant supports targeting SRL skills and perceived contextual support to enhance engagement and achievement.

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Ashley Parnell

Summarized Article:

Cleary, T. J., Slemp, J., & Pawlo, E. R. (2021). Linking student self‐regulated learning profiles to achievement and engagement in mathematics. Psychology in the Schools, 58(3), 443-457.

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Key Takeaway
Geometry is an essential topic in mathematics, fundamental to young children’s mathematical learning and development. Results of the current study suggest that fostering self-regulation skills positively impacts the learning of early geometry skills. Accordingly, teachers should be prepared to effectively support and prioritize self-regulation skills within the context of geometric tasks and experiences.
https://marioframework.com/wp-content/uploads/2022/04/Screen-Shot-2022-04-29-at-1.18.05-PM.pngSelf-Regulation and Early Geometric Skills in Young Learners 
Ashley Parnell

Mathematics and Geometry in Early Childhood

“Early mathematical skills are important for young children as such skills establish a foundation for later mathematics learning and are predictive of later school success.”1,2 More specifically, “young children’s abilities to engage in geometric thought and spatial reasoning can support their overall mathematical and cognitive development”.3 Key aspects of geometry in the early grades include:  

  • Naming, comparing, and drawing geometric shapes
  • Describing characteristics of and establishing relationships between shapes
  • Composing, decomposing, and manipulating geometric figures

Self-Regulation and Geometry Skills

Self-regulation skills play a foundational role in learning and early mathematics. While a large body of research supports the relationship between self-regulation and mathematics, most of this research has focused on numbers and operations rather than geometry. 

Given the importance of geometry for young children, the present study investigated the relationship between early geometric skills and behavioral self-regulation skills. Participants included 202 children between the ages of 5 and 6. Trained undergraduate students administered direct measures of self-regulation and geometric skills scales to children. The mothers and teachers were asked to fill in the self-regulation skills scales on behalf of their children. The following aspects of self-regulation were measured:

  • Working memory (e.g., remembers the plans made or instructions given)
  • Inhibitory control (e.g., identify causes and consequences of others’ feelings; expresses feelings and thoughts)
  • Attention (e.g., follows rules even if they delay pleasure or conflict with his/her wishes. 

Findings from this study include:

  • “Teacher-reported self-regulation skills were positively correlated with geometric skills and behavioral self-regulation.”
  • “Higher behavioral and teacher-reported self-regulation skills of children were effective in determining the children who were in the higher geometric skills group.”
  • “A weak association among mother-reported self regulation skills, age and income with geometric skills and behavioral self-regulation skills.”
  • A significant relationship existed between age and self-regulation, but not between income levels.

Implications for practitioners include:

  • Teachers should know how to effectively support and incorporate self-regulation skills in the context of geometry experiences in early childhood settings (e.g., representing shapes through different media, drawing and constructing structures with blocks).
  • “Policy makers should prioritize and facilitate the implementation of self-regulation intervention programs and early mathematics curriculum with a strong emphasis on geometry tasks in early childhood classrooms.”

Summarized Article:

İvrendi, A., Erol, A., & Atan, A. (2021). Children’s geometric skills: Any ties to self-regulation skills?. The Journal of Educational Research, 1-10.

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.

Additional References:

  1. Ivrendi, A. (2016). Investigating kindergarteners number sense and self-regulation score in relation to their mathematics and Turkish scores in middle school. Mathematics Education Research Journal, 28(3), 405–420. https://doi.org/10.1007/s13394-016-0172-4
  2. National Association for the Education of Young Children (NAEYC) & the National Council of Teachers of Mathematics (NCTM). (2010).Early childhood mathematics: Promoting good beginnings. [Online] Retrieved September 23, 2013, from http://www.naeyc.org/files/naeyc/file/positions/psmath.pdf
  3. Clements, D. H., Sarama, J., Swaminathan, S., Weber, D., & Trawick-Smith, J. (2018). Teaching and learning geometry: Early foundations. Quadrante, 27(2), 7-31. 

Key Takeaway:

The more frequently and longer students spend time online, the lower the ratings of self-regulation in digital contexts. Yet, parental control and explicit teaching of digital skills can positively impact self-regulation. Technology in the classroom can enhance motivation, collaboration with peers, and engagement; however, it is not clear if the tools hamper skills like attention and self-regulation. —Frankie Garbutt

It is argued that social-emotional skills such as empathy, perspective, self-control and self-regulation are essential skills in the 21st century. However, one must consider how teaching these skills might need to be adapted at home and in school when today’s children have constant access to social media and the internet, requiring a new approach to self-regulation and self-control. The author of this study set out to find “what benefits for, and risks to, students’ cognitive and social and emotional skills are created by ubiquitous access.”

Self-rating is a means to examine the development of social-emotional skills. It “measures extraversion (sociability, engages in class activities), agreeableness  (empathy, wants to help), conscientiousness (self-regulation, perseveres at activities), neuroticism (framed positively as emotional stability), and openness to experience (curiosity, appreciates new experiences).” Technology in the classroom can enhance motivation, collaboration with peers, and engagement, but it is not clear if the tools hamper skills like attention and self-regulation. At home, students’ use of digital tools is largely impacted by “time spent online, types of activities, and parental guidance.” 

Changes in Self-Regulation and Social Skills Due to Technology Use

Initially, the results related to social skills showed a downward trend in the skills of self-regulation in digital and non-digital context, whereas the other skills seemed to not be affected because “ratings of the dimensions most clearly related to social skills, extraversion, and agreeableness did not have a consistent trend.“ The results of the study showed five trends:

  • “Self-regulation in digital contexts was significantly lower (M = 3.05, UL = 3.19) than the equivalent measures in non-digital.“
  • “This pattern of lower self-regulation in digital contexts compared with non-digital contexts was consistent across the ages.”
  • “Ratings of social skills tended to be higher than those for self-regulation.”
  • “Last, ratings of self-regulation in digital contexts appeared to be unrelated to personality dimensions and social skills generally.” 

Implications for Schools

The authors discussed that schools can be beneficial when teaching children about self-regulation in a digital context because metacognitive skills and self-regulation are skills consistently taught, which can respectively support the students’ use of digital tools. 

Moreover, “like self-regulation, the community of practice involving parents, teachers, and students had a focus on positive online interactions. In contrast, engaging in social media activities at home was associated with higher ratings of social skills in digital (but not in non-digital) contexts.”

Overall, schools are an environment in which students can learn valuable skills, such as self-regulation and social skills, in the ever increasing digital world when complimented by parental involvement and guidance at home. The authors suggest that further research should investigate how parents and schools can respectively support the building of these skills. 

Summarized Article:

McNaughton, S., Zhu, T., Rosedale, N., Jesson, R., Oldehaver, J., & Williamson, R. (2022). In school and out of school digital use and the development of children’s self‐regulation and social skills. British Journal of Educational Psychology, 92(1), 236-257.

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:

The change from onsite learning to online can cause students to lose motivation and efficiency in their learning. Having self-regulation skills and the use of preferred low or high-impact strategies can also affect student learning. It is crucial to understand these factors and support students by helping them with self-regulation skills and deciding on study strategies that work best for them. —Nika Espinosa

This study primarily focuses on the shelter-in-place adaptations of students in a doctor of chiropractic program. Forty-nine percent of the 105 students enrolled participated in the data collection. The researchers focused on primary study strategies, technology use, motivation and efficacy, study space and time, metacognitive planning, monitoring, and evaluating. Part of the study required the participants to give sufficient evidence.

Primary Study Strategy

When it comes to study strategies, the most frequently chosen study strategy by the students was repeated reading (low-impact) and completing practice problems (high-impact). A majority of the respondents (82%) did say that they didn’t use the same strategies during shelter-in-place that they used when they were onsite learning. Low-impact strategies such as highlighting and memorizing were frequently chosen by the respondents, whereas high-impact strategies were not as preferred. The survey also showed that the chosen primary strategies that participants used were low-impact. “These data imply that although a student selects a high- or low-impact study strategy from a list, it may not reflect the true study approach but rather indicate the 1st step in the approach.“

Technology Use

A majority of the students (86%) reported that there wasn’t much difference in their use of technology when the switch to shelter-in learning was made. Twelve out of the fifty-two students did say their adaptations to technology were more significant.

Study Space

“Sixty-one percent (31/51) of respondents indicated a range in level of challenge and adaptability in finding a new study space.” Part of the challenges included not having a separate work-home space, noise, and distractions, and a lack of social interaction to support learning. “Eight respondents who selected low-impact study strategies and 4 respondents who selected high-impact study strategies as their primary strategy described positive adaptations.” 

Study Time

“Ninety-four percent (48/52) of respondents reported that they did not use the same amount of time studying during shelter-in-place orders as in prior academic terms in the program.” The biggest influencers were motivation and efficiency. Students’ motivation had gone down due to reasons such as pandemic stressors, lack of social interaction, and the structural shift in teaching and learning. Some reported that the work-life balance had become difficult, and a few students mentioned only finding accountability in deadlines and that their motivation was only to pass. Some students however became more efficient in their studies when they found ways to manage their own time. 

Planning as a Metacognitive Strategy 

Eighteen of the participants said that the most common plan they used during shelter-in learning was to create task lists and a study space to structure their learning. All of the participants that provided evidence also said that in order to set new goals, they needed to use high-impact strategies, regardless of if their primary strategy was low or high impact. “Forty-five percent (14/31) of respondents who selected a low-impact study strategy as their primary strategy described a positive or solutions-oriented plan moving forward, while 71% (15/21) of respondents who selected a high-impact study strategy as their primary strategy described a positive or solutions-oriented plan moving forward.” Those who did not provide sufficient evidence described the challenges of remote learning. 

Monitoring as a Metacognitive Strategy

A majority of the participants provided evidence for monitoring their learning. Some of them however mentioned decreased confidence in studying due to either pandemic stressors or the lack of hands-on experience. A student was quoted that they relied very much on the school structure for learning. Uncertainty about the impact of their study habits was mentioned by six of the participants.

Evaluating as a Metacognitive Strategy

Seventy-seven percent of the participants expressed that high-impact strategies were more effective, but the rest described resorting to low-impact strategies due to pandemic stressors.

Summarized Article:

Williams, C. A., Nordeen, J., Browne, C., & Marshall, B. (2022). Exploring student perceptions of their learning adaptions during the COVID-19 pandemic. Journal of Chiropractic Education. https://doi.org/10.7899/jce-21-11

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.

Key Takeaway: 

Temptation can hamper engagement and perseverance directed towards a specific task and cause distractions that can impact the learning process of a student. One way to maintain motivation for a given task is to allow students to choose their tasks and activities based on their interests. Another way is to foster self-efficacy, which enables the student to believe that they are capable of maintaining a high level of motivation and focus. —Shekufeh Monadjem

Attractive Alternatives: Temptation vs Engagement

When working on important tasks, there are always attractive alternatives that tempt us away from our work, be it social media, talking to a friend or even cleaning the house. In their study, Kim,Y., (Washington University), Yu, S.L., (Ohio State University) and Shin, J. (Seoul National University) explored how the effects of self-efficacy can impact the notion of temptation over a period of time. “As students’ learning does not happen in a vacuum, target tasks should be examined in relation to the distracting tasks to better depict motivational challenges that students face within the educational context.”1

“When the attractiveness of an alternative exceeds that of the current task, students feel tempted, and the motivation for the alternative rises.”2 Even if students have high motivation for a certain academic task, they may not engage in the learning if there is another task that is more motivating or attractive to them. 

Researchers suggest that the presence of temptation can hamper engagement and perseverance towards a given task by distracting the student to the extent that it will adversely affect their learning process. Milyavskaya and Inzlicht (2017) “found that simply experiencing temptation led to depletion and lower goal attainment.”3 Fries and Dietz (2007) “suggested that the negative impact of temptations comes from lowering motivation for the learning activity. Students often succumb to temptation and fall into the trap of task-switching or procrastination.”4

Self-Regulated Learning and Student Motivation

Self-regulated learning (SRL) can improve “the ability to concentrate on the target task in the presence of tempting alternatives”5 Self-regulated learners are more likely to maintain their motivation and sustain their engagement on a current task, instead of being distracted by other alternatives.

The current study focused on the aspect of self-efficacy for SRL, which is a crucial aspect of SRL. “Abundant evidence suggests the strong link between self-efficacy, motivation, and performance. If students perceive themselves as capable of planning, managing, and regulating their own academic activities, they are more likely to have higher confidence in learning and mastering their activities.” Previous research suggests that higher levels of self-efficacy for SRL can contribute to “higher academic self-efficacy, higher achievement, and less school dropout.”6

One way to maintain student motivation is to allow students to make their own choices and decisions. “It is important to provide meaningful choice opportunities to students to promote their interest, on-task engagement, and persistence.”7 Teachers have also realised that choice provides students a sense of responsibility and self-control, thus making students more involved and engaged in academic activities. This is especially important and effective for students with low interest or SRL skills. 

Summarized Article: 

Kim, Y. E., Yu, S. L., & Shin, J. (2021). How temptation changes across time: effects of self-efficacy for self-regulated learning and autonomy support. Educational Psychology, 1-18.

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. 

Academic researcher Yeo-eun Kim participated in the final version of this summary. 

Additional References:

  1. Hofer, M. (2010). Adolescents’ development of individual interests: A product of multiple goal regulation? Educational Psychologist, 45(3), 149–166.
  2. Hofer, M. (2007). Goal conflicts and self-regulation: A new look at pupils’ off-task behaviour in the classroom. Educational Research Review, 2(1), 28–38.
  3. Milyavskaya, M., & Inzlicht, M. (2017). What’s so great about self-control? Examining the importance of effortful self-control and temptation in predicting real-life depletion and goal attainment. Social Psychological and Personality Science, 8(6), 603–611.
  4. Fries, S., & Dietz, F. (2007). Learning in the face of temptation: The case of motivational interference. The Journal of Experimental Education, 76(1), 93–112.
  5. Baumann, N., & Kuhl, J. (2005). How to resist temptation: The effects of external control versus autonomy support on self-regulatory dynamics. Journal of Personality, 73(2), 443–470.
  6. Caprara, G. V., Fida, R., Vecchione, M., Del Bove, G., Vecchio, G. M., Barbaranelli, C., & Bandura, A. (2008). Longitudinal analysis of the role of perceived self-efficacy for self-regulated learning in academic continuance and achievement. Journal of Educational Psychology, 100(3), 525–534.
  7. Black, A. E., & Deci, E. L. (2000). The effects of instructors’ autonomy support and students’ autonomous motivation on learning organic chemistry: A self-determination theory perspective. Science Education, 84(6), 740–756.

Key Takeaway:

Mind wandering has the potential to negatively impact the process of learning and has become more prevalent with the increased practice of online learning. Self-regulation interventions may be able to decrease mind wandering and should be widely taught to students. —Ashley M. Parnell

Self Directed Learning and Mind Wandering 

“Mind wandering, the direction of attention away from a primary task, has the potential to interfere with learning, especially in increasingly common self-directed, online learning environments.” Given the prevalence and negative consequences of mind wandering, this shift towards “self-directed learning environments with minimal supervision and maximal learner control has escalated the importance of the self-regulation of attention to ensure successful learning.”1  

Self Regulation to Combat Mind Wandering

Self-regulation can be defined as the ability to manage one’s thoughts, feelings, and actions to achieve a learning goal. “Decades of empirical evidence supports self-regulation’s role in enhancing learning, as well as strategies that may be taught and used to combat mind wandering and encourage on-task focus.” 

In response, the current study sought to examine the extent to which mind wandering harms training outcomes in self-directed learning environments, as well as to compare various strategies to prevent off-task thought. Drawing from three core theoretical perspectives on the causes of mind wandering, researchers created three intervention conditions, each focusing on more than one self-regulation strategy as summarized below.

Theoretical PerspectivesObjective & Intervention Strategies
Current concerns hypothesis: Mind wandering occurs when personal concerns and goals are more valued than the primary taskIncrease value of the task and decrease other concerns/distractors by:Goal settingEnvironmental structuring (i.e., identification & removal of environmental distractions)
Executive failure hypothesis: Mind wandering is a failure of executive controlUse proactive executive control to direct focus on-task through:Planning of learning activities & objectivesMetacognitive monitoring (constant evaluation of one’s learning progress)Use reactive executive control to suppress cues that trigger mind wandering through:Implementation intentions (i.e., If-then self statements)Time management Environmental structuring  
Meta-awareness hypothesis: Mind wandering results from not being aware of the contents of consciousness.Increase awareness of consciousness through:Mindfulness (attention to & awareness/ acceptance of the present moment)Metacognitive monitoring 

Researchers tested these three interventions in two experiments: a field study with 133 working adults and a lab study with 175 college students where participants completed a self-directed online Excel training. While self-regulation interventions and excel training conditions remained the same across studies, setting, timing, and participants differed.

Findings

Researchers reported the following findings based on the two studies conducted:

  • Mind wandering during training negatively impacts self-directed learning outcomes including knowledge, self-efficacy, and trainee reactions to training.
    • The negative effects of mind wandering were notably stronger in Study 2, which incorporated less self-pacing and reported lower motivation levels.  
    • Short, one-time, online intervention was not enough to alter use of self-regulation strategies.
  • Interventions largely failed to impact trainees’ self-regulation, mind wandering, or learning relative to the control group. However, the ineffectiveness of the self-regulation interventions does not indicate that the selected self-regulatory strategies were ineffective in deterring mind wandering. 
    • Correlational results indicated that strategies strongly associated with decreased mind wandering include:  “a) practicing mindfulness by being present in the moment, b) forming and utilizing implementation intentions, c) intermittently monitoring performance using self-directed evaluative questions, and d) structuring the learning environment to minimize distractions.” 

Considerations & Implications for Practice

Results warranted consideration of the following implications for practice:  

  • Motivation levels matter in training/learning. Designing and delivering self-directed learning in ways that do not bore or overwhelm learners, and incorporating motivational incentives, may decrease mind wandering and, subsequently, the harmful effects of mind wandering.
  • Initial, albeit limited, results identify strategies that may decrease mind wandering: mindfulness, metacognitive monitoring, implementation intentions, and environmental structuring. Given self-regulation’s inherent role in online learning, efforts to develop effective interventions to teach and develop these self-regulation strategies and skills should continue. 

Summarized Article:

Randall, J., Hanson, M., & Nassrelgrgawi, A. (2021). Staying focused when nobody is watching: Self‐regulatory strategies to reduce mind wandering during self‐directed learning. Applied Psychology. 10.1111/apps.12366

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.

Academic researcher Jason Randall participated in the final version of this summary. 

Additional References:

  1. Johnson, R. D., & Randall, J. G. (2018). A review of design considerations in e-learning. In D. L. Stone & J. H. Dulebohn (Eds.), Research in human resource management (pp. 141– 188). Information Age Publishing.

Key Takeaway:

In an experiment conducted over two semesters (Fall 2019 and Winter 2020), research indicated how time management training increases self-control and time spent on activities, leading to more academic success. Not surprisingly, however, during the pandemic when time structures dissolved and learning went online, there was an increase in leisure time. —Matt Piercy

The study aimed to answer the question:

Might time management training (TMT) have an effect on student behavior when students transition from in-person to online learning? 

Authors of the article, Tabvuma et al., state that “overall, our results indicate that it is not enough to have technology available and optimized for online learning.  Students need to receive training and develop skills that will enable them to learn and work effectively in an online environment to overcome the challenges of learning in a less structured environment.”

Here are the major takeaways from the article:

  1. The pandemic resulted in a great deal of change for students as established schedules and routines all but dissolved. 
  2. Social and physical distancing, lockdowns, and reduced or eliminated work commitments resulted in much more unscheduled time. With time constraints and the norms associated with campus learning removed, students had more locus of control on how they might manage their time.  In effect, a new “game” was being played. 
  3. “Leisure media (e.g., YouTube, Netflix) provide unscheduled on-demand entertainment experiences that people can access at any time of their choosing.” This often leads to overuse. The authors argue that time management strategies can improve self-control in this area. 
  4. “A large literature has found that time management and time management training have a positive impact on individual wellbeing and performance, including students.”1

However, numerous limitations were noted. 

For example, the control of gathering data specific to the impact of time management training (TMT) was interrupted as a result of COVID-19. Data was self-reported by students and further, students were all first-year university students in an introductory business course. Only three sessions of time management training were implemented and divided over the course of one semester.

Summarized Article:

Tabvuma, V., Carter-Rogers, K., Brophy, T., Smith, S. M., & Sutherland, S. (2021). Transitioning from in person to online learning during a pandemic: an experimental study of the impact of time management training. Higher Education Research & Development, 1-17.

Summary by: Matt Piercy—Matt appreciates how at the heart of the MARIO Framework is a passion to develop relationships and a desire to empower students to uncover their purpose while building upon strengths  Further, Matt is inspired by how the MARIO team supports educators and is quickly and nobly becoming a collaborative force in pursuit of educational equity. 

Additional References:  

  1. Aeon, B., & Aguinis, H. (2017). It’s about time: New perspectives and insights on time management. Academy of Management Perspectives, 31(4), 309–330. https://doi.org/10.5465/amp.2016.0166 

Key Takeaway

Research suggests that teacher reprimands do not decrease students’ future disruptive behavior or increase their engagement levels. Instead, teachers should focus on proactive classroom management strategies, such as explicitly teaching classroom expectations, using behavior-specific praise, and reinforcing positive behavior as a way to encourage desired behavioral outcomes in the classroom. —Jay Lingo 

Students with Emotional and Behavioral Disorders (EBD)

“Many teachers resort to using reprimands in attempts to stop disruptive student behavior,” particularly amongst those students with emotional or behavioral challenges. 

Students with emotional and behavioral disorders (EBD) may experience many challenges in school and often present commonly identified characteristics including aggression, attention and academic problems, antisocial behavior, low classroom engagement, high rates of disruptive behaviors, and mental health challenges. 

“The ways in which teachers and students interact can affect outcomes for students with EBD. There can be positive outcomes if the teacher–student interactions are positive and teachers have been able to increase the on-task behavior, or engagement, and decrease disruptions of students in their classrooms.” 

While teacher reprimands may suppress misbehavior momentarily, they do not appear to be effective in decreasing students’ disruptive behavior or increasing their engagement over time. Limitations and implications are also discussed. 

Reprimands: How Effective Are They?

Caldarella et al.’s study emphasizes that the “ways in which teachers and students interact can affect outcomes for students with EBD. Teachers who deliver low rates of negative feedback (e.g., reprimands) and high rates of positive feedback (e.g., praise) may be particularly effective with students with EBD when providing multiple teaching and learning opportunities that enhance students’ engagement.”

Furthermore, reprimands have been linked to escape-motivated behaviors, aggression, and further disruptive behavior. The use of reprimands for students with or at risk for EBD can be especially problematic, given the specific challenges faced by these students. The current study found that teacher reprimands did not appear to decrease future disruptive behavior or increase future engagement for students at risk for EBD, or vice versa. 

The results of the study show that although they may temporarily suppress misbehavior they do not result in long-term positive behavior change. This might be because reprimands do not directly teach students the skills needed to improve their behavior, and thus, students may continue to exhibit negative behavior and continue receiving reprimands. Another problem is that reprimands are reactive: a student acts disruptively and a teacher reprimands the student. 

The Alternative to Reprimands

Instead, the focus should be on effective teaching techniques and proactive behavior management strategies to decrease disruptions and increase engagement.

“Reprimands are meant to stop misbehavior. However, in the current study, teacher reprimands did not appear to help decrease future classroom disruptions or increase future engagement of students at risk for EBD.” This should not be surprising, as harsh reprimands in schools have been associated with negative side effects such as anger, fear, escape, and avoidance rather than improved student behavior. In addition to being harmful to teachers and their students, reprimands prove less effective than positive classroom behavior management strategies. “Teachers who use reprimands also report higher levels of emotional exhaustion than their peers who do not.” 

Given the findings of the current study, along with those of previous researchers, it is recommended that teachers replace reprimands with proactive classroom management strategies, such as clearly teaching classroom expectations, reinforcing positive student behavior, and using behavior-specific praise, as primary responses to student misbehavior and disengagement.

Summarized Article:

Caldarella, P., Larsen, R., Williams, L.,  Wills, H., & Wehby, J. (2020). “Stop Doing That!”: Effects of Teacher Reprimands on Student Disruptive Behavior and Engagement. Journal of Positive Behavior Interventions, Vol. 23 (2). DOI:  10.1177/1098300720935101.

Summary by: Jerome Lingo— Jerome believes the MARIO Framework is providing structure and common meaning to learning support programs across the globe. Backed up with current research on the best practices in inclusion and general education, we can reimagine education…together.

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.