Motivation

Online Learning

This study aimed to explore how task instructions, framed with need-supportive statements, can support motivation and the mechanisms that could explain the potential effect of such motivation on task performance. Specifically, the authors examined whether need-supportive task instructions can enhance the situational intrinsic motivation of secondary school students on an online language learning task.

Intrinsic Motivation is Inherently Present in Most People

Self-determination theory (SDT) posits that individuals are inherently driven by curiosity and a desire for learning, meaning people are naturally intrinsically motivated. Studies have shown that when students’ basic psychological needs are supported in digital learning environments, they are more likely to persist and remain motivated in their learning tasks. One approach to achieving this is by framing task instructions in a way that supports students’ needs for relatedness, competence, and autonomy.

Intrinsic motivation acts as a self-directed drive behind behavior. Research indicates that self-regulated learning strategies, such as self-assessment, serve as mediators linking motivation to achievement and are considered essential 21st-century skills.

Increased Intrinsic Motivation is Linked to Increased Task Performance

The study involved 106 secondary school students from a public science high school in Central Luzon, Philippines. Participants attended a brief Zoom meeting outlining the procedure, followed by completing a Qualtrics survey. They were asked about self-assessment, intrinsic motivation for the assignment, and the extent to which their basic psychological needs were met. The study tested a theoretically informed intervention to foster intrinsic motivation for an online learning task in two ways: (a) by including need-supportive statements within task instructions and (b) by assessing whether increased intrinsic motivation improves task performance, directly or indirectly, through self-assessment practice.

The results demonstrated that students who received need-supportive task instructions showed significantly higher intrinsic motivation than those with default instructions. The effect size of the need-supportive task instructions on intrinsic motivation was medium, while accounting for pre-test intrinsic motivation.

Although intrinsic motivation did not directly affect task performance, the study found that intrinsic motivation had an indirect effect through self-assessment practice. Specifically, increased intrinsic motivation led to more frequent self-assessment, which in turn improved task performance.

These findings suggest that while intrinsic motivation alone may not directly improve performance, self-assessment practices as a learning strategy can leverage intrinsic motivation to indirectly enhance task performance. In conclusion, need-supportive task instructions positively influence students’ intrinsic motivation for online learning tasks, offering new insights into the integration of self-determination theory, wise interventions, and self-assessment practice in online learning.

Online Task Instructions Can Be Modified to Respond to Students’ Needs

A key implication of this study is that online task instructions can be adjusted to be more responsive to students’ basic psychological needs. Reading plain, default task instructions may negatively impact students’ intrinsic motivation, especially outside traditional classroom settings. Based on the study’s findings, task instructions could be modified to:

  1. Provide a clear task rationale
  2. Use invitational language
  3. Offer encouragement
  4. Acknowledge students’ perspectives
  5. Show unconditional positive regard

Another practical takeaway is the importance of fostering self-assessment practices among students. Students can be taught to:

  1. Understand the assessment criteria or how their work will be evaluated
  2. Observe the overall task and seek feedback from external sources
  3. Ask questions or request clarifications
  4. Revisit and revise their tasks to find areas for improvement

Notable Quotes: 

“Although such intrinsic motivation had no direct effect on task performance, it yielded significant indirect effects via self-assessment practice.”

“Evidence suggests that students’ perception of a need-supportive learning context optimizes various students’ learning outcomes (e.g., achievement, motivation, engagement, well-being.”

“First, given that the intervention is brief and minimal scale, its effectiveness in increasing students’ intrinsic motivation can be put into question. It could be argued that merely adding phrases designed to communicate support for autonomy, competence, and relatedness needs may not consistently and sustainably impact intrinsic motivation.”

Personal Takeaway: 

While the research offers useful insights into how need-supportive task instructions can boost intrinsic motivation, much of it aligns with what we already know about motivation and self-assessment. One thing that stood out is that self-assessment tends to be most effective for older students, which makes sense given the demographic in this study. Also, research shows that teacher feedback and modeling are key when it comes to self-assessment, though that wasn’t explored much here. —Matt Browne

Mendoza, N. B., Yan, Z., & King, R. B. (2023). Supporting students’ intrinsic motivation for online learning tasks: The effect of need-supportive task instructions on motivation, self-assessment, and task performance. Computers & Education, 193, 104663.

Investigation of tutor and tutee’s perception of challenges faced in virtual tutoring. Secondly, reasons for service refusal were investigated to inform future planning and training.

The perceptions of online peer tutoring

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The skills necessary to be an effective tutor

Research shows that peer tutoring can be beneficial for all participants. Yet, it is essential that tutors possess the technological and pedagogical skills necessary for community building and engaging teaching in an online medium. Online tutoring is only successful if tutees engage with the content and speak during sessions to ensure they progress in their language learning.

Tutors need to be taught

Email interviews were conducted to collect qualitative data from both tutors and tutees. Their responses were then coded, and the main themes were identified for the analysis. It is recommended that tutors receive thorough instructions on how to teach in an online medium to ensure the success of peer tutoring. Moreover, different communication channels could be employed to share schedules and the effectiveness of the available services. Nonetheless, more research must be done to investigate the program’s long-term impact on students’ academic performance.

Necessary steps to support tutors

Peer tutoring can benefit the tutor and tutee in an online medium. However, tutors require clear guidance from core professors and must receive support in planning their sessions to ensure student progress. 

Online teaching and tutoring will play a more significant role in education, not just during the pandemic. Therefore providing adequate training for educators and tutors is essential in delivering peer tutoring to students. Nonetheless, other factors may contribute to the success or failure of a program. Struggling students reported they required support in managing their time and attending virtual sessions. As a result, peer-tutoring schedules should be flexible.

Notable Quotes: 

Peer tutoring is a very effective approach to fostering learning when used in an inclusive and collaborative atmosphere.

Indeed, the teaching session is much more comprehensive and coherent for both tutors and tutees with the guided method and material. 

The results of this paper are valuable not only for the stakeholders in the studied institution but also for any educational institutions that are considering this student support service.

Personal Takeaway: 

I have seen peer tutoring to be successful in person. This article is helpful in considering how peer tutoring can be offered virtually to effectively meet the needs and context of tutees. Equally, it highlights that tutors require guidance before starting peer tutoring. Some of the recommendations will help me to adapt peer tutoring for my students.—Frankie

Quoc Luong, B., Thi Thu Tran, H., & Thi Minh Nguyen, N. (2022, March). Online Peer Tutoring in Online English Courses: Perceptions of Tutors and Tutees. In 2022 3rd International Conference on Education Development and Studies (pp. 58-63).

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.

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.