personalized feedback
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Author(s):  
Kamalaveni Soundararajan ◽  
Karthikadevi Sivakumar ◽  
Andrew Blackmore ◽  
Marina Flynn

The COVID-19 pandemic has affected gynaecology trainees in the United Kingdom by reducing operating theatre experience. Simulators are widely used for operative laparoscopy but not for practising laparoscopic-entry techniques. We devised a low-cost simulator to help trainees achieve the skill. Our aim was to pilot this low-cost simulator to perform Royal College of Obstetricians and Gynaecologists (RCOG) supervised learning events.A single-centre pilot study involving six gynaecology trainees in a structured training session. Interactive PowerPoint teaching was followed by trainees’ demonstration of laparoscopic entry for a supervised learning event and personalized feedback. Participants completed pre- and post-course questionnaires.All the trainees found the training useful to the score of 10 (scale of 1–10) and recommended this to be included in Deanery teaching. Personalized feedback was described as the most useful. The simulator was rated as good as a real-life patient relative to the skill being taught.Gynaecology trainees are affected by lack of hands-on experience in the operating theatre for performing laparoscopic entry. A low-cost abdominal laparoscopy entry simulator can help deliver the RCOG curriculum, enabling trainees to achieve required competencies.


Author(s):  
Wenzhong Xu ◽  
Jun Meng ◽  
S. Kanaga Suba Raja ◽  
M. Padma Priya ◽  
M. Kiruthiga Devi

Artificial Intelligence (AI) systems have evolved with digital learning developments to provide thriving soft groups with digital opportunities in response to feedback. When it comes to learning environments, educators’ training feedback is often used as a response recourse. Through the use of final evaluations, students receive feedback that improves their education abilities. To improve academic achievement and explore knowledge in the learning process, this section provides an AI-assisted personalized feedback system (AI-PFS). An individualized feedback system is implemented to learn more about the student’s lack of academic experience interactivity and different collaboration behaviors. According to their benchmark, PFS aims to establish a personalized and reliable feedback process for each class based on their collaborative process and learn analytics modules. It has been proposed to use multi-objective implementations to evaluate students regarding the learning results and teaching methods. With different series of questions sessions for students, AI-PFS has been designed, and the findings showed that it greatly enhances the performance rate of 95.32% with personalized and reasonable predictive.


2021 ◽  
Vol 2 ◽  
Author(s):  
Christian Riis Forman ◽  
Jens Bo Nielsen ◽  
Jakob Lorentzen

Background: Effective science-based motor rehabilitation requires high volume of individualized, intense physical training, which can be difficult to achieve exclusively through physical 1-on-1 sessions with a therapist. Home-based training, enhanced by technological solutions, could be a tool to help facilitate the important factors for neuroplastic motor improvements.Objectives: This review aimed to discover how the inclusion of modern information and communications technology in home-based training programs can promote key neuroplastic factors associated with motor learning in neurological disabilities and identify which challenges are still needed to overcome.Methods: We conducted a thorough literature search on technological home-based training solutions and categorized the different fundamental approaches that were used. We then analyzed how these approaches can be used to promote certain key factors of neuroplasticity and which challenges still need to be solved or require external personalized input from a therapist.Conclusions: The technological approaches to home-based training were divided into three categories: sensory stimuli training, digital exchange of information training, and telerehabilitation. Generally, some technologies could be characterized as easily applicable, which gave the opportunity to promote flexible scheduling and a larger overall training volume, but limited options for individualized variation and progression. Other technologies included individualization options through personalized feedback that might increase the training effect, but also increases the workload of the therapist. Further development of easily applicable and intelligent solutions, which can return precise feedback and individualized training suggestions, is needed to fully realize the potential of home-based training in motor learning activities.


2021 ◽  
Vol 12 ◽  
Author(s):  
IJsbrand Leertouwer ◽  
Angélique O. J. Cramer ◽  
Jeroen K. Vermunt ◽  
Noémi K. Schuurman

Ecological Momentary Assessment (EMA) in which participants report on their moment-to-moment experiences in their natural environment, is a hot topic. An emerging field in clinical psychology based on either EMA, or what we term Ecological Retrospective Assessment (ERA) as it requires retrospectivity, is the field of personalized feedback. In this field, EMA/ERA-data-driven summaries are presented to participants with the goal of promoting their insight in their experiences. Underlying this procedure are some fundamental assumptions about (i) the relation between true moment-to-moment experiences and retrospective evaluations of those experiences, (ii) the translation of these experiences and evaluations to different types of data, (iii) the comparison of these different types of data, and (iv) the impact of a summary of moment-to-moment experiences on retrospective evaluations of those experiences. We argue that these assumptions deserve further exploration, in order to create a strong evidence-based foundation for the personalized feedback procedure.


Author(s):  
Emma I. Brett ◽  
Thad R. Leffingwell ◽  
Susanna V. Lopez ◽  
Eleanor L. S. Leavens ◽  
Delaney S. Dunn

2021 ◽  
Vol 12 (1) ◽  
pp. 172-190
Author(s):  
Rháleff N. R. Oliveira ◽  
Rafaela V. Rocha ◽  
Denise H. Goya

Serious Games (SGs) are used to support knowledge acquisition and skill development. For this, there is a need to measure the results achieved (both during and after students play) to ensure the game effectiveness. In this context, the aim is to develop and evaluate the AvaliaJS, a conceptual model to structure, guide and support the planning of the design and execution of the student's performance assessment in SGs. AvaliaJS has two artifacts: a canvas model, for high-level planning, and an assessment project document, for more detailed specifications of the canvas. To analyze and exemplify the use of the model, the artifacts were applied to three ready-made games as a proof of concept. In addition, the quality of AvaliaJS was evaluated by experts in SGs development and assessment using a questionnaire. The results of experts' answers confirm a good internal consistency (Cronbach's alpha α = 0.87) which indicates that AvaliaJS is correct, authentic, consistent, clear, unambiguous and flexible. However, the model will need to be validated during the process of creating a new game to ensure its usability and efficiency. In general, AvaliaJS can be used to support the team in the planning, documentation and development of artifacts and data collection in SGs, as well as in the execution of the assessment, learning measurement and constant and personalized feedback for students.


2021 ◽  
pp. 100484
Author(s):  
John A. Cunningham ◽  
Christina Schell ◽  
Nicolas Bertholet ◽  
Jeffrey D. Wardell ◽  
Lena C. Quilty ◽  
...  

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 450
Author(s):  
Yancy Vance Paredes ◽  
I-Han Hsiao

Providing feedback to students is one of the most effective ways to enhance their learning. With the advancement of technology, many tools have been developed to provide personalized feedback. However, these systems are only beneficial when interactions are done on digital platforms. As paper-based assessment is still a dominantly preferred evaluation method, particularly in large blended-instruction classes, the sole use of electronic educational systems presents a gap between how students learn the subject from the physical and digital world. This has motivated the design and the development of a new educational technology that facilitates the digitization, grading, and distribution of paper-based assessments to support blended-instruction classes. With the aid of this technology, different learning analytics can be readily captured. A retrospective analysis was conducted to understand the students’ behaviors in an Object-Oriented Programming and Data Structures class from a public university. Their behavioral differences and the associated learning impacts were analyzed by leveraging their digital footprints. Results showed that students made significant efforts in reviewing their examinations. Notably, the high-achieving and the improving students spent more time reviewing their mistakes and started doing so as soon as the assessment became available. Finally, when students were guided in the reviewing process, they were able to identify items where they had misconceptions.


Author(s):  
Niklas Hopfgartner ◽  
Michael Auer ◽  
Tiago Santos ◽  
Denis Helic ◽  
Mark D. Griffiths

AbstractIn order to protect gamblers, gambling operators have introduced a wide range of responsible gambling (RG) tools. Mandatory play breaks (i.e., forced termination of a gambling session) and personalized feedback about the gambling expenditure are two RG tools that are frequently used. While the motivation behind mandatory play breaks is simple (i.e., gambling operators expect gamblers to reduce their gambling significantly as a result of an enforced break in play), empirical evidence supporting the efficacy of the mandatory breaks is still limited. The present study comprised a real-world experiment with the clientele of Norwegian gambling operator Norsk Tipping. On the Norsk Tipping gambling website, which offers slots, bingo and sports-betting, forced termination occurs if gamblers have played continuously for a one-hour period. The study tested the effect of different lengths of mandatory play breaks (90 s, 5 min, 15 min) on subsequent gambling behavior, as well as the effect of combined personalized feedback concerning money wagered, won, and net win/loss. In total 21,129 online players (61% male; mean age = 47.4 years) experienced at least one play break between April 17 and May 21 (2020) with 156,989 mandatory play breaks in total. Results indicated that a 15-min mandatory play break led to a disproportionately longer voluntary play pause compared to 5-min and 90-s mandatory play breaks. Personalized feedback appeared to have no additional effect on subsequent gambling and none of the mandatory play breaks appeared to affect the increase or decrease in money wagered once players started to gamble again.


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