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.
“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.”
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.
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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Identifying the interrelationships between self-regulation, emotion, grit and student performance by using the Cyclical Self-Regulated learning model, which is associated with a K-12 math tutoring program.
What happens when students fail in their academic tasks?
When students repeatedly fail at a task, their self-confidence in the subject goes down, and their anxiety and frustration goes up. They build a resentment towards the subject and start to believe that their time in class is not useful.
The concept and long-term nature of grit helps students maintain consistent and focused interest for important and challenging goals.
Improving the level of grit in students
Two groups of middle schoolers who were participating in a weekly Mathspring Intervention program were chosen, one based in the US, and the other in Argentina. The levels of grit and expectation of success were measured in these groups. The research team argues for the need to work on improving the level of grit in students, by validating hard work ethic, strengthening student setbacks, and encouraging diligence. It was also found that grit in the early phase of learning predicts success in later stages of learning.
More research needs to be done on student experience
This study is just the tip of the iceberg on representing types of student learning. More research needs to be done on the experiences of students with diverse learning patterns, and patterns found for students with disabilities, gifted students, easily-frustrated students and so on.
“In education settings, students who exhibit self-regulation in learning behaviours are able to direct their efforts toward achieving academic goals.”
“There are certain traits and dimensions of character other than intelligence that are strong determinants of a person’s unique path towards success despite setbacks.”
“The long-term nature of grit is what differentiates it from similar constructs such as self-control and conscientiousness.”
Reading this research has opened my mind to the importance of building grit in students, especially those in Learning Support, as this skill will strongly contribute to their future success in life and allow them to persevere through the challenges of life with more ease.
Kooken, J. W., Zaini, R., & Arroyo, I. (2021). Simulating the dynamics of self-regulation, emotion, grit, and student performance in cyber-learning environments. Metacognition and Learning, 16(2), 367-405.
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In this article, authors Adi, Artini & Wahyuni (2021) “analyze teachers’ perceptions of self-directed learning (SDL) and the identifiable components of SDL from online learning activities assigned by teachers during the COVID-19 pandemic.”
The involvement of students in learning decisions
This article did not include a literature review. However, it was interesting to note that despite the fact that educators recognize the value of SDL, many hesitate to involve students in decisions related to their learning, including both content and assessment decisions.
Measuring perceptions of self-directed learning
As a part of the data collection methods, the researchers provided a self-rated questionnaire, measuring teachers’ “perceptions of SDL content knowledge, perceptions of SDL implementation, and perceptions of the impact of SDL on students.”
The study surveyed one high school English as an Additional Language (EAL) teacher in Indonesia. Overall, the findings revealed that while teachers recognize the importance of providing opportunities for self-directed learning for student success, “the application of SDL has not been maximized.” Rather, “teachers tend to use classical strategies that do not focus on student-centered learning” and “are hesitant to involve students” in decisions related to their learning, including content and assessments.
How teachers can help students grow in independence
The results suggest that “teachers and school authorities need to develop the most appropriate curriculum and assignments to encourage students to be more independent and independent in learning.” The study showed that English teachers consider themselves “knowledgeable” about SDL content knowledge and “anticipate” in applying SDL, yet “rarely do pre-activities and post-activities” to promote student autonomy. However, the research is limited by the survey size (only one teacher was surveyed) and additional participants need to be surveyed in order to be able to generalize the findings to a wider population of teachers.
“Teachers felt that SDL strategies could significantly motivate students in learning, stimulate them to recognize their learning goals, and assist them in determining appropriate sources and ways of learning.”
Self-directed learning (SDL) is “a process in which students take the initiative to identify their learning needs, formulate learning objectives, determine learning resources, apply appropriate learning strategies and evaluate their learning outcomes, with or without the help of others” (Hill et al., 2020; Suryadewi, Wiyasa, & Sujana, 2020).
According to the questionnaire, teachers “generally agree that SDL is important for students” as it “can give autonomy to students in learning and increase students’ initiative in monitoring their learning.”
As the world shifts back to in-person school, evaluating perceptions of self-directed learning will be an important part of determining next steps for teacher instruction in this new phase of the pandemic.
Adi, N. L. M. P., Artini, L. P., & Wahyuni, L. G. E. (2021). Self-Directed Learning in EFL During Covid-19 Pandemic: Teacher’s Perception and Students’ Learning Autonomy. Indonesian Journal of Educational Research and Review, 5(1), 80-90. DOI: http://dx.doi.org/10.23887/ijerr.v4i1.
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A meta-analysis of factors which impact student motivation.
What factors have the greatest impact on motivation?
Students’ need for competence and teachers’ autonomy support for students has the greatest impact on student motivation. Other factors like quality feedback also impact student motivation, supporting Hattie‘s claim that teachers are at the forefront of student achievement.
The most influential factors on student motivation
The authors thoroughly identified studies which matched strict criteria and designed a coding spreadsheet to find correlation between factors, identifying how each factor impacted student motivation. This allowed them to identify 144 studies with over 79,000 students ranging from primary to university age. The study also examined the impact of teacher and parent autonomy support as aspects that impact motivation. The need for competence and autonomy support from teachers were found to be the most influential factors on student motivation. The authors also highlight other aspects within schools such as low pressure and quality feedback as correlating aspects influencing student motivation. The study also supports Hattie‘s claim that teachers are essential in student achievement.
Autonomy support vs reward and punishment
The study emphasized that teachers ought to be allowed to practice autonomy-support methods rather than use rewards or punishment. Yet these methods are also effective even when they are applied in a non-high-pressure environment where testing or result-driven pay is not prevalent.
“Results show that teacher autonomy support predicts students’ need satisfaction and self-determined motivation more strongly than parental autonomy support. Specifically, they show that regardless of age, school level, nationality, or gender, autonomy support predicts autonomous types of motivation, thereby providing support for existing interventions designed to increase student need satisfaction and motivation through autonomy-supportive practices. These results concur with Hattie’s (2009) meta-analysis of 800+ student achievement predictors showing that teachers are at the forefront of learning experiences for students and are likely to have the strongest influence on student motivation.”
It resonated with me because I believe teachers have a great influence on student motivation and achieving competence. Moreover, the correlation between teacher support and quality feedback is very interesting. I will aim to embed more autonomy support in my teaching and avoid rewards and punishment where possible because my setting would allow for such autonomy-support methods to be implemented. Furthermore, it aligns with the MARIO framework principle of developing student self-efficacy and self-directed learning using the MARIO approach in all that we do in the classroom.
Bureau, J. S., Howard, J. L., Chong, J. X., & Guay, F. (2021). Pathways to student motivation: A meta-analysis of antecedents of autonomous and controlled motivations. Review of Educational Research, 92(1), 46–72. https://doi.org/10.3102/00346543211042426
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.
Objective & Intervention Strategies
Current concerns hypothesis: Mind wandering occurs when personal concerns and goals are more valued than the primary task
Increase 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 control
Use 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.
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.
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.
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.
As educators, we know that learning is always more meaningful when there is student involvement and ownership. When designing Tier 2 behavior interventions, student participation and feedback in the process increases effectiveness as student investment increases with involvement. —Nika Espinosa
Tiers of Behavioral Support
The paper by Mallory et al. dives into the importance of student involvement in the design of Tier 2 behavior interventions and provides a framework to help educators involve students.
“Positive behavior interventions and supports is a multilevel approach to behavior support implementation that involves three tiers of interventions targeting students’ various levels of needs.”
Whole-school or class-wide implementation is found at the Tier 1 level, where agreed-upon behavioral expectations are defined and implemented. Students are presented with opportunities to develop these skills.
Tier 2 support is for those who need targeted intervention or need something more than what can be provided by the Tier 1 level if at-risk behaviors manifest. This could look like individual or small-group sessions, with more opportunities for reinforcement and/or support plans.
Tier 3 students need intensive reinforcement, with an intervention team constantly monitoring, as well as assessing.
Involving Students in Behavior Interventions
“It has been argued that if children are not involved in the design and implementation of interventions, then the student will be less likely to commit to, or be compliant with, the treatment.”1
The authors provide the key stages to help design tier 2 intervention. Starting with determining the function of challenging behavior, the student can either complete a functional assessment interview or do a self-assessment of recorded unexpected behavior.
The authors believe that a primary source for data should be the student. Then, the intervention team and the student need to determine the target skill or behavior that should be optimized.
The likelihood of a student being motivated to change the behavior increases when they themselves identify what behaviors they see as an opportunity for growth. “If a student believes that a behavior is not worth changing, it may be difficult to get them to make significant changes in the behavior.”
Once the behavior is identified, the goal criteria are determined. “Essentially, goals should follow the Goldilocks principle: They should be neither too easy nor too difficult to achieve; they should provide a challenge without being overwhelming for the student. It is at this level of difficulty in which learning is optimal.”1
Once that is in place, the student needs to be involved in choosing reinforcers. These reinforcers need to be highly motivating. Student involvement is crucial as it boosts their chances of working towards desired behaviors.
When collecting the data, it is imperative that the student is also present. According to the authors, the easiest way to collect data is to have the student do so. “The student can be taught to identify and record data when they are engaging in the target behaviors, using a number of self-management principles, thereby decreasing the reliance on external prompts and increasing awareness of their own behaviors”.
As the student’s involvement increases in Tier 2 interventions, so does their self-awareness, self-advocacy, and self-determination in achieving desired targets.
Mallory, P. J., Hampshire, P. K., & Carter, D. R. (2021). Tier 2 Behavior Interventions: By the Student, for the Student. Intervention in School and Clinic, 1053451221994812.
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.
Kennedy, E. K. (2015). The Revised SEND Code of Practice 0-25: Effective practice in engaging children and young people in decision-making about interventions for social, emotional and mental health needs. Support for Learning, 30(4), 364–380. https://doi.org/10.1111/1467-9604.12106
Researcher Patrick Mallory participated in the final version of this summary.
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.
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.
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
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
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
Key Takeaway: The pandemic has challenged educators to transform their teaching practices to suit a new learning environment—one where meaningful learning can take place with or without the presence of a teacher. Moving towards learner-centered instruction and well-designed online teaching should encourage students to remain motivated and engaged by providing diverse, collaborative learning activities and creating a space where students are empowered to take control over their own learning. —Taryn McBrayne
In his article, author John Andrew Cohen (Division of Learning and Teaching, Charles Sturt University) discusses the role that the COVID-19 pandemic has played in encouraging educators to re-evaluate their pedagogical approaches to teaching and learning. Cohen argues that while many companies and organizations needed to quickly transform their face-to-face classrooms to remain in business, by implementing the same instructional methods used in the physical classroom in an online setting, they may not be meeting the needs of their learners.
In an online classroom, teachers often have the flexibility to deliver instruction synchronously or asynchronously, meaning that the teacher may not always be physically present in the virtual class. Cohen cites Mottus et al. (2018)1 in emphasizing that while a teacher’s role as a “content delivery expert may be reduced in ubiquitous learning environments [such as online learning environments], the need for their pedagogical skills in effective facilitation has, if anything, increased in importance.” Cohen argues that online teaching needs to ensure that learning can occur, even without a teacher’s presence. Thus, as Cohen explains, traditional lecture-style teaching approaches may not be suitable.
The author highlights “Learner-Centered Teaching”2 as a useful framework for fostering productive learning environments without the direct presence of a teacher. Through sharing the power between the student and teacher, learners are “empowered to make decisions about when they learn, how they learn, where they learn, with whom they learn and on some occasions what they learn and how they are assessed.” In addition, researchers such as Weimer (2002)2 highlight the importance of sharing power, stating that “student motivation, confidence and enthusiasm for learning are all adversely affected when teaching staff control the process through which they learn.” Researchers Weimer (2002)2 and Shearer et al. (2019)3 also suggest that “learners are highly autonomous” and as a result, “instructors are facilitators, negotiators, and guides.” Here, the author recommends a shift in teaching design from direct instruction to self-direction, emphasizing the learning experience as opposed to solely the delivery of content.
Thus, Cohen explains that educators can build a strong student-centered online learning environment by providing a wide range of activities, ways for students to manage their own learning, and multiple opportunities to check for understanding. Ultimately, the author emphasizes that “learning design should aid the facilitation of learning—they should influence each other symmetrically, in a ‘hand in glove’ manner.”
Summarized Article: Cohen, J.A. (2021). A fit for purpose pedagogy: online learning designing and teaching, Development and Learning in Organizations: An International Journal, Vol. 35 (4), pp. 15-17. https://doi.org/10.1108/DLO-08-2020-0174
Summary by: Taryn McBrayne—Taryn believes in the power of student voice and, through the MARIO Framework, strives to create more opportunities for both educators and students to regularly make use of this power.
1. Mottus, A., Kinshuk, N., Sabine, G., Uthman, A. and Ahmed, A. (2018), “Teacher facilitation support in ubiquitous learning environments”, Technology, Pedagogy and Education, Vol. 27 No. 5, pp. 549-570.
2. Weimer, M. (2002), Learner-Centered Teaching: Five Key Changes to Practice, Jossey-Bass, San Francisco.
3. Shearer, R., Aldemirb L., Hitchcock T., Resig, J.J., Driver, J. and Kohler, M. (2019), “What students want: a vision of a future online learning experience grounded in distance education theory”, American Journal of Distance Education, Vol. 34 No. 1, pp. 36-52.
Design thinking is generally defined as an analytic and creative process that engages a person in opportunities to experiment, create and prototype models, gather feedback, and redesign. Several characteristics (e.g., visualization, creativity) that a good design thinker should possess have been identified from the literature. The primary purpose of this article is to summarize and synthesize the research on design thinking to (a) better understand its characteristics and processes, as well as the differences between novice and expert design thinkers, and (b) apply the findings from the literature regarding the application of design thinking to our educational system. The authors’ overarching goal is to identify the features and characteristics of design thinking and discuss its importance in promoting students’ problem-solving skills in the 21st century.
Razzouk and Shute’s study articulates how design thinking might be applied in educational settings. MARIO embraces this study’s exploration of how the incorporation of design thinking can influence student responses to challenge.
Erik de Corte describes a progression in which earlier behaviorism gave way increasingly to cognitive psychology with learning understood as information processing rather than as responding to stimuli. More active concepts of learning took hold (“constructivism”), and with “social constructivism” the terrain is not restricted to what takes place within individual minds but as the interaction between learners and their contextual situation. There has been a parallel move for research to shift from artificial exercises/situations to real-life learning in classrooms and hence to become much more relevant for education. The current understanding of learning, aimed at promoting 21st century or “adaptive” competence, is characterized as “CSSC learning”: “constructive” as learners actively construct their knowledge and skills; “self-regulated” with people actively using strategies to learn; “situated” and best understood in context rather than abstracted from environment; and “collaborative” not a solo activity.
De Corte’s work defines how learning is currently understood to be an active, self-regulated, social experience rooted in authentic context. MARIO, in all aspects, espouses this view of learning. It is fundamental to how MARIO defines the learner’s role.