The known universe of reflection guidance: a literature review

Fessl, Angela; Blunk, Oliver; Prilla, Michael; Pammer, Viktoria

doi:10.1504/ijtel.2017.084491

Cited by 13 publications

(1 citation statement)

References 40 publications

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“…In addition to the coaching program Working on Resilience, results from earlier studies on self-tracking and e-coaching for resilience training were also used during the design of the BringBalance app [26,27,34]. Other sources for creating the design of the BringBalance app were provided by the literature on reflection [10,12,15,24,[35][36][37], coaching techniques [38][39][40][41][42][43], and persuasive design elements that can support the reflection process, such as visualization and personalization [17,22,44,45].…”

Section: The Bringbalance Appmentioning

confidence: 99%

How Reflective Automated e-Coaching Can Help Employees Improve Their Capacity for Resilience: Mixed Methods Study

Lentferink¹,

Oldenhuis²,

Velthuijsen³

et al. 2023

JMIR Hum Factors

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Background An eHealth tool that coaches employees through the process of reflection has the potential to support employees with moderate levels of stress to increase their capacity for resilience. Most eHealth tools that include self-tracking summarize the collected data for the users. However, users need to gain a deeper understanding of the data and decide upon the next step to take through self-reflection. Objective In this study, we aimed to examine the perceived effectiveness of the guidance offered by an automated e-Coach during employees’ self-reflection process in gaining insights into their situation and on their perceived stress and resilience capacities and the usefulness of the design elements of the e-Coach during this process. Methods Of the 28 participants, 14 (50%) completed the 6-week BringBalance program that allowed participants to perform reflection via four phases: identification, strategy generation, experimentation, and evaluation. Data collection consisted of log data, ecological momentary assessment (EMA) questionnaires for reflection provided by the e-Coach, in-depth interviews, and a pre- and posttest survey (including the Brief Resilience Scale and the Perceived Stress Scale). The posttest survey also asked about the utility of the elements of the e-Coach for reflection. A mixed methods approach was followed. Results Pre- and posttest scores on perceived stress and resilience were not much different among completers (no statistical test performed). The automated e-Coach did enable users to gain an understanding of factors that influenced their stress levels and capacity for resilience (identification phase) and to learn the principles of useful strategies to improve their capacity for resilience (strategy generation phase). Design elements of the e-Coach reduced the reflection process into smaller steps to re-evaluate situations and helped them to observe a trend (identification phase). However, users experienced difficulties integrating the chosen strategies into their daily life (experimentation phase). Moreover, the identified events related to stress and resilience were too specific through the guidance offered by the e-Coach (identification phase), and the events did not recur, which consequently left users unable to sufficiently practice (strategy generation phase), experiment (experimentation phase), and evaluate (evaluation phase) the techniques during meaningful events. Conclusions Participants were able to perform self-reflection under the guidance of the automated e-Coach, which often led toward gaining new insights. To improve the reflection process, more guidance should be offered by the e-Coach that would aid employees to identify events that recur in daily life. Future research could study the effects of the suggested improvements on the quality of reflection via an automated e-Coach.

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Section: The Bringbalance Appmentioning

confidence: 99%

How Reflective Automated e-Coaching Can Help Employees Improve Their Capacity for Resilience: Mixed Methods Study

Lentferink¹,

Oldenhuis²,

Velthuijsen³

et al. 2023

JMIR Hum Factors

View full text Add to dashboard Cite

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How Does Collaborative Reflection Unfold in Online Communities? An Analysis of Two Data Sets

Prilla

Blunk

Chounta

2020

Comput Supported Coop Work

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People can learn a lot through (collaborative) reflection at work: In organizations, staff debate experiences and due to issues every day, thus reflecting together and learning from each other. While this is desirable, it is often hindered by differences in time and space. Online discussions in community-like systems may provide a means to overcome this issue and enable staff to share experiences and learn from them. In this paper, we study two different data sets from two software systems to support online collaborative reflection in order to analyze what possible factors influence the occurrence of aspects of reflection in online discussions, like experiences, suggestions and learning. Our results include findings that partially approve existing models of (collaborative) reflection, but also others that add to or even challenge these models. Overall, we found that collaborative reflection may take different paths towards learning. From this we derive that facilitation mechanisms need to take up these paths, and that facilitation may be successful in different ways than anticipated from existing literature. We describe possible facilitation mechanisms and discuss their implementation.

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Students’ Mathematical Learning Behaviour in Teaching-for-Understanding Classes: Senior High School and First-Year University Students’ Views

Velzen

2020

The Educational Forum

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The known universe of reflection guidance: a literature review

Cited by 13 publications

References 40 publications

How Reflective Automated e-Coaching Can Help Employees Improve Their Capacity for Resilience: Mixed Methods Study

How Reflective Automated e-Coaching Can Help Employees Improve Their Capacity for Resilience: Mixed Methods Study

How Does Collaborative Reflection Unfold in Online Communities? An Analysis of Two Data Sets

Students’ Mathematical Learning Behaviour in Teaching-for-Understanding Classes: Senior High School and First-Year University Students’ Views

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