Comment on “progress testing anytime, anywhere – does a mobile-learning approach enhance the utility of a large-scale formative assessment tool?”

2020 ◽  
pp. 1-2
Author(s):  
Gustavo Salata Romão ◽  
Marcos Felipe Silva de Sá ◽  
Cesar Eduardo Fernandes ◽  
Agnaldo Lopes da Silva Filho
Keyword(s):  
Formative Assessment ◽  
Mobile Learning ◽  
Large Scale ◽  
Assessment Tool ◽  
Learning Approach ◽  
Progress Testing

Contextual Learning Approach and Performance Assessment in Mathematics Learning

2016 ◽  
Vol 3 (3) ◽  
Author(s):  
I Wayan Eka Mahendra
Keyword(s):  
Formative Assessment ◽  
Performance Assessment ◽  
Junior High School ◽  
Mathematics Learning ◽  
Sampling Technique ◽  
Learning Outcome ◽  
Contextual Learning ◽  
Learning Approach ◽  
Better Than ◽  
A Performance

This study aims to determine the effect of formative assessment and learning approach to the mathematics learning outcome after controlling the numerical aptitude. It was a quasi-experiment with a sample of 186 students obtained by using multistage random sampling technique with 2x2 factorial designs. The data were analyzed by ANCOVA. After controlling the numerical aptitude, the results are: the mathematics learning outcome of the students who followed a contextual approach was better than the ones who followed a conventional learning approach, the mathematics learning outcome of the students who were given a performance assessment was better than the ones who were given a conventional assessment, the interaction between the learning approach and formative assessment affected the students learning outcome for mathematics, the students who followed a contextual learning approach were more suitable to be given a performance assessment, whereas the ones who followed a conventional learning approach were more appropriate to be given a conventional assessment. Based on the research findings, junior high school teachers are suggested to improve their students learning outcome for mathematics. Then, teachers need to use a learning approach and formative assessment accurately and correctly. 

Structured Long Interview and Clinical Examination (SLICE) as Formative Assessment Tool in Clinical Clerkship: Medical Students Perspective

2021 ◽  
pp. 1-16
Author(s):  
Wajiha Shadab ◽  
Amna Ahmed Noor ◽  
Saira Waqqar ◽  
Gul Muhammad Shaikh
Keyword(s):  
Medical Students ◽  
Formative Assessment ◽  
Thematic Analysis ◽  
Assessment Tool ◽  
Clinical Skills ◽  
General Medicine ◽  
Sampling Technique ◽  
Clinical Clerkship ◽  
Primary Investigator ◽  
Skills Learning

Abstract Objective: This study aimed to assess the medical students’ opinions and views on undertaking SLICE as a formative assessment. Methods: This was a qualitative, exploratory study. Purposive sampling technique was used to select final year medical students who have undertaken a formative assessment through SLICE in their clerkship rotation. Total 32 students participated in this study .Four sets of focus group discussions (FGD) were conducted from medical students who had recently gone through their clinical clerkship modules for Pediatrics, General Medicine, General Surgery and Gynecology& Obstetrics. Each recorded FGD was transcribed verbatim. Thematic analysis was conducted manually. Themes were identified from the transcribed data, coded and analyzed. In order to achieve adequate coding and researcher reliability, investigator triangulation was performed. The initial thematic analysis was performed by the primary investigator. Thereafter, two more investigators independently analyzed the data. Before the data was finalized, all the three investigators reached a final consensus upon the themes that had emerged, ensuring triangulation of the analyzed data. Results: A four staged thematic analysis was conducted, in which five major themes and five sub-themes emerged. The main themes being: Purpose, Learning, Timing, Relevancy and Fairness of SLICE. Conclusion: The students generally thought that SLICE was effective in enhancing their clinical skills learning and should be conducted more frequently with minor adjustments. Continuous...

Reducing Nutrient Loads in Northern Irish Catchments: A Modelling Approach Based on Load Apportionment and Flow Pathway Identification

2021 ◽  
Author(s):  
Russell Adams ◽  
Donnacha Doody
Keyword(s):  
Water Quality ◽  
Large Scale ◽  
Agricultural Land ◽  
Assessment Tool ◽  
Ecological Status ◽  
Point Sources ◽  
Load Reduction ◽  
Nutrient Loads ◽  
Flow Pathways ◽  
Flow Pathway

<p>Northern Ireland has been somewhat overlooked in terms of water quality modelling in the past. Many of its catchments have consistently failed to meet Water Framework Directive targets especially due to high levels of dissolved nutrients and poor ecological status. A catchment based modelling study to address this issue has not been undertaken here previously and the approach described here uses two water quality models to achieve this aim. The objectives of the modelling were firstly to identify the total load reductions (in terms of Phosphorus (P)) required to reduce in-stream loadings sufficiently for concentrations of soluble reactive P (SRP) to be reduced to achieve the WFD “Good” status levels, and secondly to split these loadings into diffuse and point components. The third objective was to identify the most likely flow pathways for the transport of the diffuse component of P to the watercourses particularly for the agricultural (mostly intensive grassland farming) land use which dominates in almost all NI catchments.</p><p>The first model applied is the Source Load Apportionment Model (SLAM) developed by the Irish EPA. This model provides a large-scale assessment of the point and diffuse load components across catchments where multiple pressures are occurring. The second model us the Catchment Runoff Flux Assessment Tool (CRAFT) which is able to back-calculate nutrient loads associated with three major flow pathways. SLAM is a static model which uses averaged loadings from diffuse agriculture and non-agricultural land uses, and point sources (where information can be obtained from various sources) to calculate N and P exports. For P, the agricultural diffuse load component uses an enhanced version of the export coefficient approach based on combining the sources of P from applied nutrients (slurry and fertiliser) and soil P. A modelling tool allows the user to evaluate load reduction scenarios where one or several components of P (both point and diffuse) are adjusted downwards to achieve the catchment’s required load reduction. The CRAFT model works on a dynamic (daily) modelling scale and has simulated sub-catchments where the SLAM model has identified the need for significant load reductions. It identifies the different reductions (P export) that are required for each flow pathway, which will then inform on the type of additional measures (e.g. sediment traps, riparian buffer strips and wetlands) that may also be required.</p><p>The initial aim of this study is to complete a pilot application to the trans-border (UK and ROI) Blackwater catchment (1360 km<sup>2</sup>). Through a review of alternative modelling options for the whole area of NI, an assessment of whether this approach is suitable for application to the entire territory can be made.</p>

The Development of Mobile Learning (M-Learning) Apps Adik-Adik Jawi to Enhance Learning Experience in Arabic Calligraphy

2021 ◽  
Vol 02 (01) ◽  
Author(s):  
Mohd Hatta Mohamed Ali ◽  
◽  
Anwar Hafidzi ◽  
Juliana Mohamed ◽  
Mariam Abdul Hamid ◽  
...  
Keyword(s):  
Mobile Learning ◽  
Teaching And Learning ◽  
Development Process ◽  
Learning Experience ◽  
Learning Activities ◽  
Learning Approach ◽  
Application Development ◽  
Research Article ◽  
Arabic Calligraphy

History has proven the development of Jawi calligraphy is in line with the development of Islam in the archipelago. It is the root of the nation’s identity that must be defended and maintained. As with other subjects, the challenge to learning Jawi calligraphy at this time is that the whole world including Malaysia is affected by the COVID 19 pandemic. Therefore, all learning activities are now geared towards teaching and learning from home (PdPR) as methods to ensure the continuity of education. Therefore, it is very important that Jawi calligraphy is given a new breath in teaching and learning. This research article will discuss the mobile learning approach (M-Learning) for Jawi calligraphy. Important elements discussed include the application development process according to the needs of teaching and learning activities. The features of the application that contribute to the improvement of students’ learning experience as well as the results of tests performed on students are also stated. The success of this M-Learning application for learning Jawi calligraphy will certainly be able to be further expanded to the learning of other subjects in various fields.

scholarly journals DeepMAsED: evaluating the quality of metagenomic assemblies

2020 ◽  
Vol 36 (10) ◽  
pp. 3011-3017 ◽  
Author(s):  
Olga Mineeva ◽  
Mateo Rojas-Carulla ◽  
Ruth E Ley ◽  
Bernhard Schölkopf ◽  
Nicholas D Youngblut
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Ground Truth ◽  
Supplementary Information ◽  
Learning Approach ◽  
Wide Range ◽  
Metagenome Assembly ◽  
Model Training ◽  
Reference Genomes

Abstract Motivation Methodological advances in metagenome assembly are rapidly increasing in the number of published metagenome assemblies. However, identifying misassemblies is challenging due to a lack of closely related reference genomes that can act as pseudo ground truth. Existing reference-free methods are no longer maintained, can make strong assumptions that may not hold across a diversity of research projects, and have not been validated on large-scale metagenome assemblies. Results We present DeepMAsED, a deep learning approach for identifying misassembled contigs without the need for reference genomes. Moreover, we provide an in silico pipeline for generating large-scale, realistic metagenome assemblies for comprehensive model training and testing. DeepMAsED accuracy substantially exceeds the state-of-the-art when applied to large and complex metagenome assemblies. Our model estimates a 1% contig misassembly rate in two recent large-scale metagenome assembly publications. Conclusions DeepMAsED accurately identifies misassemblies in metagenome-assembled contigs from a broad diversity of bacteria and archaea without the need for reference genomes or strong modeling assumptions. Running DeepMAsED is straight-forward, as well as is model re-training with our dataset generation pipeline. Therefore, DeepMAsED is a flexible misassembly classifier that can be applied to a wide range of metagenome assembly projects. Availability and implementation DeepMAsED is available from GitHub at https://github.com/leylabmpi/DeepMAsED. Supplementary information Supplementary data are available at Bioinformatics online.

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