scholarly journals Trialling the Use of Google Apps Together with Online Marking to Enhance Collaborative Learning and Provide Effective Feedback

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 177
Author(s):  
Nicky J. D. Slee ◽  
Marty H. Jacobs
Keyword(s):  
Group Work ◽  
Final Stage ◽  
Peer Assessment ◽  
Collaborative Work ◽  
Data Set ◽  
New Approach ◽  
Effective Feedback ◽  
Data Collection Process ◽  
Complete Set ◽  
Google Drive

This paper describes a new approach to an ecology practical where the cohort was divided into four groups to collect data. Each group studied a different habitat; the cohort was further subdivided into seven groups to collect field data. Each of the four groups collaborated through Google Drive on descriptions and images of the habitat site, and also collaborated at the subgroup level on their own habitat data. The four groups then shared habitat descriptions with the aim to provide enough information to enable everyone to understand entire data set. Group work was assessed online and feedback was given at both the group and subgroup levels. At the end of the first stage, peer assignment of all the work was carried out on an individual basis to engage students in other habitats. A complete set of data was finally provided to all students, so that individuals could carry out their own analysis of all four habitats; work was again assessed online and feedback given to each individual. The three-stage assignment from group work to peer assessment to individual analysis was a success. The collaborative work through Google Drive enabled students to produce high quality documents that were valuable for the next step. The peer assignment enabled students to gain information on expected Minimum Standards and exposed them to a variety of habitats. The final stage was open ended and challenged students. This approach is recommended but the data collection process needs modification, and students need more guidance when completing the final stage of the assignment.

scholarly journals Trialling the use of Google Apps together with online marking to enhance collaborative learning and provide effective feedback

F1000Research ◽  
2017 ◽  
Vol 4 ◽  
pp. 177
Author(s):  
Nicky J. D. Slee ◽  
Marty H. Jacobs
Keyword(s):  
Large Scale ◽  
Peer Assessment ◽  
Collaborative Work ◽  
Virtual Learning Environment ◽  
Data Sets ◽  
Ecological Data ◽  
Data Set ◽  
Assessment Task ◽  
Google Drive ◽  
Google Apps

This paper describes a new approach to an ecology practical in which 76 Level 4 students were divided into four groups (n = 20 +/-2) to collect data. Each group studied a different habitat and was further divided into seven subgroups (n = 2 or 3) to collect field data. Each of the four groups collaborated through Google Drive on descriptions and images of the habitat site, and also collaborated at the subgroup level on their own habitat data. The four groups then shared habitat descriptions with the aim to provide enough information to enable everyone to understand the entire data set. The three-stage assignment was assessed and feedback issued at group and individual level via the University’s online submission service (FASER), with some additional feedback given via Moodle, the University’s Virtual Learning Environment. Two separate submissions were made to FASER, the first was the group and subgroup work (stage 1), and the second included the peer assessment task (stage 2) and the individual evaluation of the habitats (stage 3). Feedback was given after the second submission had been uploaded to FASER and again when the assessment for the second submission was complete. The group and subgroup data sets were provided to all students via Moodle, so that individuals could carry out their own analysis of all four habitats. The use of Google Drive and Google Apps helped to improve the digital literacy of the staff and students involved. All three stages of the assignment were successful; over 85% of students passed the first two stages, and 82.9% passed stage 3. The collaborative work enabled students to produce high quality descriptive ecology documents valuable for the subsequent stages of the assignment. The peer assessment encouraged students to gain information on expected Undergraduate Minimum Standards, and gave students the opportunity to study multiple habitats. The final stage was open ended and challenged students to make sense of large ecological data sets. There was a positive correlation between levels of success at stages 1 and 3 for students who achieved less than 65% for the independent work, i.e. they benefited from carrying out group work. This collaborative, three-stage approach is recommended especially as it helps lower ability students gain subject knowledge and improve their presentation skills. However, some modifications are recommended: 1) simplifying the sample and data collection, and 2) providing more guidance for the peer assessment task and individual analysis. Learner autonomy enabled self-directed learning to take place and enriched large scale teaching as it encouraged student-student interaction. Significant differences between gender and ability are discussed.

A New Approach for the Analysis of Mixture Toxicity Data

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2345-2348 ◽  
Author(s):  
C. N. Haas
Keyword(s):  
Quantitative Analysis ◽  
Mixture Toxicity ◽  
New Method ◽  
Metal Exposure ◽  
Positive Interactions ◽  
Toxicity Data ◽  
Data Set ◽  
New Approach ◽  
Negative Interactions

A new method for the quantitative analysis of multiple toxicity data is described and illustrated using a data set on metal exposure to copepods. Positive interactions are observed for Ni-Pb and Pb-Cr, with weak negative interactions observed for Ni-Cr.

scholarly journals The Whole-Genome Sequence of the Novel Yeast Species Metschnikowia persimmonesis Isolated from Medicinal Plant Diospyros kaki Thunb

2021 ◽  
Author(s):  
Endang Rahmat ◽  
Inkyu Park ◽  
Youngmin Kang
Keyword(s):  
Gc Content ◽  
Whole Genome Sequence ◽  
Diospyros Kaki ◽  
Whole Genome ◽  
The Novel ◽  
Data Set ◽  
Draft Assembly ◽  
Future Possibility ◽  
Complete Set ◽  
New Yeast

Abstract The new yeast Metschnikowia persimmonesis KCTC 12991BP (KIOM G15050 strain) exhibits strong antimicrobial activity against some pathogens. This activity may be related to the medicinal profile of secondary metabolites that could be found in the genome of this species. Therefore, to explore its future possibility of producing some beneficial activities, including medicinal ability, we report high quality whole-genome assembly of M. persimmonesis produced by PacBio RSII sequencer. The final draft assembly consisted of 16 scaffolds with GC content of 45.90% and comprised a fairly complete set (82.8%) of BUSCO result using Saccharomycetales lineage data set. The total length of the genome was 16.473 Mb, with a scaffold N50 of 1.982 Mb. Annotation of the M. persimmonesis genome revealed presence of 7,029 genes and 6,939 functionally annotated proteins. Based on the analysis of phylogenetic relationship and the average nucleotide identities (ANI), M. persimmonesis was proved to a novel species within the Metschnikowia genus. This finding is expected to significantly contribute to the discovery of high-value natural products from M. persimmonesis as well as for genome biology and evolution comparative analysis within Metschnikowia species.

scholarly journals Multiscale Mask R-CNN–Based Lung Tumor Detection Using PET Imaging

2019 ◽  
Vol 18 ◽  
pp. 153601211986353 ◽  
Author(s):  
Rui Zhang ◽  
Chao Cheng ◽  
Xuehua Zhao ◽  
Xuechen Li
Keyword(s):  
Pet Imaging ◽  
Lung Tumor ◽  
Training Data ◽  
Fine Tuning ◽  
Weighted Voting ◽  
Data Set ◽  
New Approach ◽  
Positron Emission ◽  
Voting Strategy ◽  
Aided Diagnosis

Positron emission tomography (PET) imaging serves as one of the most competent methods for the diagnosis of various malignancies, such as lung tumor. However, with an elevation in the utilization of PET scan, radiologists are overburdened considerably. Consequently, a new approach of “computer-aided diagnosis” is being contemplated to curtail the heavy workloads. In this article, we propose a multiscale Mask Region–Based Convolutional Neural Network (Mask R-CNN)–based method that uses PET imaging for the detection of lung tumor. First, we produced 3 models of Mask R-CNN for lung tumor candidate detection. These 3 models were generated by fine-tuning the Mask R-CNN using certain training data that consisted of images from 3 different scales. Each of the training data set included 594 slices with lung tumor. These 3 models of Mask R-CNN models were then integrated using weighted voting strategy to diminish the false-positive outcomes. A total of 134 PET slices were employed as test set in this experiment. The precision, recall, and F score values of our proposed method were 0.90, 1, and 0.95, respectively. Experimental results exhibited strong conviction about the effectiveness of this method in detecting lung tumors, along with the capability of identifying a healthy chest pattern and reducing incorrect identification of tumors to a large extent.

Collaborative Calibrated Peer Assessment in Massive Open Online Courses

2020 ◽  
pp. 1408-1434
Author(s):  
Asma Boudria ◽  
Yacine Lafifi ◽  
Yamina Bordjiba
Keyword(s):  
Open Access ◽  
Collaborative Learning ◽  
Online Courses ◽  
Peer Assessment ◽  
Learning Algorithms ◽  
Massive Open Online Courses ◽  
Major Difficulty ◽  
Massive Open Online ◽  
New Approach

The free nature and open access courses in the Massive Open Online Courses (MOOC) allow the facilities of disseminating information for a large number of participants. However, the “massive” propriety can generate many pedagogical problems, such as the assessment of learners, which is considered as the major difficulty facing in the MOOC. In fact, the immense number of learners who exceeded in some MOOC the hundreds of thousands make the instructors' evaluation of students' production quite impossible. In this work, the authors present a new approach for assessing the learners' production in MOOC. This approach combines the peer assessment with the collaborative learning and the calibrated method. It aims at increasing the degree of trust in peer-assessment. For evaluating the proposed approach, the authors implemented a MOOC dedicated for learning algorithms. In addition, an experiment was conducted during two months for knowing the effects of the proposed approach. The obtained results are presented in this paper. They are judged as very interesting and encouraging.

scholarly journals Method for Monitoring of Managing the Modernization of Regional Economy

2019 ◽  
Vol 71 ◽  
pp. 04004
Author(s):  
T. Krasnova ◽  
T. Plotnikova ◽  
A. Pozdnyakov ◽  
A. Vilgelm
Keyword(s):  
Spatial Distribution ◽  
Economic Activity ◽  
Urban Areas ◽  
Regional Economy ◽  
Regional Level ◽  
Market Potential ◽  
New Economic Geography ◽  
Local Market ◽  
Data Set ◽  
New Approach

This paper proposes a new approach for monitoring of managing the modernisation of regional economic. The model built on proposed methodology will make it possible to smooth out the influence of non-urban areas on the unevenness of economic activity in spatial development. This paper has two goals. The first is to provide a new compilation of data on spatial distribution of economic activity at the sub-regional level. This data set allows us to monitoring of different indicators within macroregions such as Siberia. The second goal is to construct an instrument that helps to overcome the endogeneity problem using new economic geography hypothesis about the mechanisms of distribution of economic activity. Section 2 describes the data and method that we have proposed, discusses the construction of the Theil indexes using these data at the sub-federal and the sub-regional level. Section 3 presents the correlations between spatial distribution of economic activity and local market potential, discusses the robustness of the results; and the last section concludes.

scholarly journals The Austrian Corona Panel Project: monitoring individual and societal dynamics amidst the COVID-19 crisis

2020 ◽  
Author(s):  
Bernhard Kittel ◽  
Sylvia Kritzinger ◽  
Hajo Boomgaarden ◽  
Barbara Prainsack ◽  
Jakob-Moritz Eberl ◽  
...  
Keyword(s):  
Data Access ◽  
Economic Consequences ◽  
Process Data ◽  
Data Set ◽  
Domain Specific ◽  
Data Collection Process ◽  
The Social ◽  
The Individual ◽  
Austrian Population ◽  
Societal Dynamics

Abstract Systematic and openly accessible data are vital to the scientific understanding of the social, political, and economic consequences of the COVID-19 pandemic. This article introduces the Austrian Corona Panel Project (ACPP), which has generated a unique, publicly available data set from late March 2020 onwards. ACPP has been designed to capture the social, political, and economic impact of the COVID-19 crisis on the Austrian population on a weekly basis. The thematic scope of the study covers several core dimensions related to the individual and societal impact of the COVID-19 crisis. The panel survey has a sample size of approximately 1500 respondents per wave. It contains questions that are asked every week, complemented by domain-specific modules to explore specific topics in more detail. The article presents details on the data collection process, data quality, the potential for analysis, and the modalities of data access pertaining to the first ten waves of the study.

Fiber-optic distributed acoustic sensing of microseismicity, strain and temperature during hydraulic fracturing

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. D11-D23 ◽  
Author(s):  
Martin Karrenbach ◽  
Steve Cole ◽  
Andrew Ridge ◽  
Kevin Boone ◽  
Dan Kahn ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
Hydraulic Fracture ◽  
Fiber Optic ◽  
Data Set ◽  
New Approach ◽  
Acoustic Sensing ◽  
Fiber Optic Technology ◽  
Physical Effects ◽  
Distributed Acoustic Sensing ◽  
Temperature Strain

Hydraulic fracturing operations in unconventional reservoirs are typically monitored using geophones located either at the surface or in the adjacent wellbores. A new approach to record hydraulic stimulations uses fiber-optic distributed acoustic sensing (DAS). A fiber-optic cable was installed in a treatment well in the Meramec formation to monitor the hydraulic fracture stimulation of an unconventional reservoir. A variety of physical effects, such as temperature, strain, and microseismicity are measured and correlated with the treatment program during hydraulic fracturing of the well containing the fiber and also an adjacent well. The analysis of this DAS data set demonstrates that current fiber-optic technology provides enough sensitivity to detect a considerable number of microseismic events and that these events can be integrated with temperature and strain measurements for comprehensive hydraulic fracture monitoring.

A New Approach for Supervised Dimensionality Reduction

2018 ◽  
Vol 14 (4) ◽  
pp. 20-37 ◽  
Author(s):  
Yinglei Song ◽  
Yongzhong Li ◽  
Junfeng Qu
Keyword(s):  
Eigenvalue Problem ◽  
Dimensionality Reduction ◽  
Image Databases ◽  
Data Sets ◽  
Data Set ◽  
New Approach ◽  
Local Structures ◽  
Benchmark Data ◽  
Global And Local

This article develops a new approach for supervised dimensionality reduction. This approach considers both global and local structures of a labelled data set and maximizes a new objective that includes the effects from both of them. The objective can be approximately optimized by solving an eigenvalue problem. The approach is evaluated based on a few benchmark data sets and image databases. Its performance is also compared with a few other existing approaches for dimensionality reduction. Testing results show that, on average, this new approach can achieve more accurate results for dimensionality reduction than existing approaches.

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