scholarly journals Mining interactions in immersive learning environments for real-time student feedback

2013 ◽  
Vol 29 (2) ◽  
Author(s):  
Gregor Kennedy ◽  
Ioanna Ioannou ◽  
Yun Zhou ◽  
James Bailey ◽  
Stephen O'Leary
Keyword(s):  
Real Time ◽  
Learning Environments ◽  
Learning Analytics ◽  
Learning Systems ◽  
Student Feedback ◽  
Formative Feedback ◽  
Digital Data ◽  
Online Interactions ◽  
Use Of Data ◽  
Hidden Patterns

<p>The analysis and use of data generated by students’ interactions with learning systems or programs – learning analytics – has recently gained widespread attention in the educational technology community. Part of the reason for this interest is based on the potential of learning analytic techniques such as data mining to find hidden patterns in students’ online interactions that can be meaningfully interpreted and then fed back to students in a way that supports their learning. In this paper we present an investigation of how the digital data records of students’ interactions within an immersive 3D environment can be mined, modeled and analysed, to provide real-time formative feedback to students as they complete simulated surgical tasks. The issues that emerged in this investigation as well as areas for further research and development are discussed.</p>

Effects of timing of formative feedback in computer‐assisted learning environments

2020 ◽  
Vol 36 (5) ◽  
pp. 718-728
Author(s):  
Carmen Candel ◽  
Eduardo Vidal‐Abarca ◽  
Raquel Cerdán ◽  
Marie Lippmann ◽  
Susanne Narciss
Keyword(s):  
Learning Environments ◽  
Formative Feedback ◽  
Computer Assisted ◽  
Computer Assisted Learning

A comparison of select trigger algorithms for automated global seismic phase and event detection

1998 ◽  
Vol 88 (1) ◽  
pp. 95-106 ◽  
Author(s):  
Mitchell Withers ◽  
Richard Aster ◽  
Christopher Young ◽  
Judy Beiriger ◽  
Mark Harris ◽  
...  
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Event Detection ◽  
Spectral Characteristics ◽  
Digital Data ◽  
Robust Detection ◽  
Location System ◽  
Global Correlation ◽  
Seismic Coda ◽  
Wide Range

Abstract Digital algorithms for robust detection of phase arrivals in the presence of stationary and nonstationary noise have a long history in seismology and have been exploited primarily to reduce the amount of data recorded by data logging systems to manageable levels. In the present era of inexpensive digital storage, however, such algorithms are increasingly being used to flag signal segments in continuously recorded digital data streams for subsequent processing by automatic and/or expert interpretation systems. In the course of our development of an automated, near-real-time, waveform correlation event-detection and location system (WCEDS), we have surveyed the abilities of such algorithms to enhance seismic phase arrivals in teleseismic data streams. Specifically, we have considered envelopes generated by energy transient (STA/LTA), Z-statistic, frequency transient, and polarization algorithms. The WCEDS system requires a set of input data streams that have a smooth, low-amplitude response to background noise and seismic coda and that contain peaks at times corresponding to phase arrivals. The algorithm used to generate these input streams from raw seismograms must perform well under a wide range of source, path, receiver, and noise scenarios. Present computational capabilities allow the application of considerably more robust algorithms than have been historically used in real time. However, highly complex calculations can still be computationally prohibitive for current workstations when the number of data streams become large. While no algorithm was clearly optimal under all source, receiver, path, and noise conditions tested, an STA/LTA algorithm incorporating adaptive window lengths controlled by nonstationary seismogram spectral characteristics was found to provide an output that best met the requirements of a global correlation-based event-detection and location system.

scholarly journals A State-wide Health IT Infrastructure for Population Health: Building a Community-wide Electronic Platform for Maryland’s All-Payer Global Budget

2017 ◽  
Vol 9 (3) ◽  
Author(s):  
Elham Hatef ◽  
Hadi Kharrazi ◽  
Ed VanBaak ◽  
Marc Falcone ◽  
Lindsey Ferris ◽  
...  
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Population Health ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Smoking Status ◽  
Digital Data ◽  
Health It ◽  
Global Budget ◽  
Health Initiatives

Maryland Department of Health (MDH) has been preparing for alignment of its population health initiatives with Maryland’s unique All-Payer hospital global budget program. In order to operationalize population health initiatives, it is required to identify a starter set of measures addressing community level health interventions and to collect interoperable data for those measures. The broad adoption of electronic health records (EHRs) with ongoing data collection on almost all patients in the state, combined with hospital participation in health information exchange (HIE) initiatives, provides an unprecedented opportunity for near real-time assessment of the health of the communities. MDH’s EHR-based monitoring complements, and perhaps replaces, ad-hoc assessments based on limited surveys, billing, and other administrative data. This article explores the potential expansion of health IT capacity as a method to improve population health across Maryland.First, we propose a progression plan for four selected community-wide population health measures: body mass index, blood pressure, smoking status, and falls-related injuries. We then present an assessment of the current and near real-time availability of digital data in Maryland including the geographic granularity on which each measure can be assessed statewide. Finally, we provide general recommendations to improve interoperable data collection for selected measures over time via the Maryland HIE. This paper is intended to serve as a high- level guiding framework for communities across the US that are undergoing healthcare transformation toward integrated models of care using universal interoperable EHRs.

scholarly journals Using student feedback and professor-developed multimedia to improve instructor presence and student learning

2014 ◽  
pp. 12-30 ◽  
Author(s):  
William Albert Young II ◽  
Brett H. Hicks ◽  
Danielle Villa-Lobos ◽  
Teresa J. Franklin
Keyword(s):  
Real Time ◽  
Social Presence ◽  
Learning Experience ◽  
Student Feedback ◽  
Learning Needs ◽  
Time Data ◽  
Online Distance Education ◽  
Learning Content ◽  
Online Distance ◽  
Feedback Data

This paper explores the use of Professor-Developed Multimedia Content (PDMC) in online, distance education to build a community of inquiry (CoI) through enhanced social presence and real-time, student-driven, adaption of the learning content. The foundation of higher education has long been, developing curriculum to meet educational objectives. Most often faculty relies on assessment information gained at the end of each course. Then assessments, formative and summative, are re-designed based on student feedback/data from end of course surveys and educational materials such as textbooks, articles, and test banks are updated with newer editions. In the distance-learning environment, PDMC provides a creative, innovative, and interactive ways to engage the student for real-time learning. Still, the ability to target PDMC materials to the correct sub-sections of our classroom cohort can produce a richer, more immerse learning experience and perhaps become the closet recreation of in-seat, traditional classroom learning in a distance/online environment. By using PDMC with corresponding surveys, educators can obtain real-time data and metrics to alter content in the classroom immediately, and develop media content welcoming sub-sets of learners with desired content based on learning needs, desires, and feedback.

Detecting Pressure Anomalies While Drilling Using a Machine Learning Hybrid Approach

2021 ◽  
Author(s):  
Aurore Lafond ◽  
Maurice Ringer ◽  
Florian Le Blay ◽  
Jiaxu Liu ◽  
Ekaterina Millan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Large Scale ◽  
Hybrid Approach ◽  
Physical Models ◽  
Training Data ◽  
Digital Data ◽  
Machine Learning Techniques ◽  
New System

Abstract Abnormal surface pressure is typically the first indicator of a number of problematic events, including kicks, losses, washouts and stuck pipe. These events account for 60–70% of all drilling-related nonproductive time, so their early and accurate detection has the potential to save the industry billions of dollars. Detecting these events today requires an expert user watching multiple curves, which can be costly, and subject to human errors. The solution presented in this paper is aiming at augmenting traditional models with new machine learning techniques, which enable to detect these events automatically and help the monitoring of the drilling well. Today’s real-time monitoring systems employ complex physical models to estimate surface standpipe pressure while drilling. These require many inputs and are difficult to calibrate. Machine learning is an alternative method to predict pump pressure, but this alone needs significant labelled training data, which is often lacking in the drilling world. The new system combines these approaches: a machine learning framework is used to enable automated learning while the physical models work to compensate any gaps in the training data. The system uses only standard surface measurements, is fully automated, and is continuously retrained while drilling to ensure the most accurate pressure prediction. In addition, a stochastic (Bayesian) machine learning technique is used, which enables not only a prediction of the pressure, but also the uncertainty and confidence of this prediction. Last, the new system includes a data quality control workflow. It discards periods of low data quality for the pressure anomaly detection and enables to have a smarter real-time events analysis. The new system has been tested on historical wells using a new test and validation framework. The framework runs the system automatically on large volumes of both historical and simulated data, to enable cross-referencing the results with observations. In this paper, we show the results of the automated test framework as well as the capabilities of the new system in two specific case studies, one on land and another offshore. Moreover, large scale statistics enlighten the reliability and the efficiency of this new detection workflow. The new system builds on the trend in our industry to better capture and utilize digital data for optimizing drilling.

Interactive E-Textbook Platform Based on Block Editing Model in Crowdsourcing E-Learning Environments

2021 ◽  
Vol 17 (1) ◽  
pp. 50-66
Author(s):  
Ja-Ryound Choi ◽  
Soon-Bum Lim
Keyword(s):  
Programming Languages ◽  
Learning Environments ◽  
Visual Programming ◽  
Student Feedback ◽  
Feedback Information ◽  
Feedback Analysis ◽  
Digital Textbooks ◽  
E Learning ◽  
Visual Programming Language

Instructors can now work with students to create various textbooks based on crowdsourcing. In particular, as feedback provided by students is essential for determining the quality and direction of classes, instructors should interact with students who are currently participating in classes by exchanging feedback. This paper proposes a block editing model that can reflect student feedback. The block editing model is an interactive e-textbook editing model that is dynamically updated based on the feedback provided by students in real time without modifying the structure of digital textbooks. In particular, in order for even non-developers who do not know web programming languages to be able to produce interactive digital textbooks easily, the authors developed an editor that could help implement them based on Blockly, a visual programming language. This paper enables instructors to improve the direction and quality of classes depending on the learning achievement of students and understanding based on feedback information provided by students and feedback analysis.

Gamification and Smart Feedback

Gamification ◽  
2015 ◽  
pp. 970-982
Author(s):  
Michael D. Kickmeier-Rust ◽  
Eva C. Hillemann ◽  
Dietrich Albert
Keyword(s):  
Educational Technology ◽  
Gender Difference ◽  
Learning Analytics ◽  
Formative Feedback ◽  
Development Effort ◽  
Light Weight ◽  
Game Based Learning ◽  
Knowledge Space ◽  
Positive Effects ◽  
Motivational Aspect

Gamification is a recent trend in the field of game-based learning that accounts for development effort, costs, and effectiveness concerns of games. Another trend in educational technology is learning analytics and formative feedback. In the context of a European project the developed a light weight tool for learning and practicing divisions named Sonic Divider. This simple app is based on features of gamification. More importantly, it features formative assessment and feedback functions based on Competence-based Knowledge Space Theory. The authors applied and evaluated the tool in Austrian classrooms and found some evidence for the motivational aspect of the gamification elements, in particular scoring. They also found positive effects of an individualized and meaningful feedback about errors. Finally, there occurred certain gender difference, for example, girls were much less attracted by competition elements (e.g., by comparing high scores) then boys, however, more attentive towards feedback coming from the tool.

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