scholarly journals Introducing Low-Cost Sensors into the Classroom Settings: Improving the Assessment in Agile Practices with Multimodal Learning Analytics

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3291 ◽  
Author(s):  
Hector Cornide-Reyes ◽  
René Noël ◽  
Fabián Riquelme ◽  
Matías Gajardo ◽  
Cristian Cechinel ◽  
...  
Keyword(s):  
Low Cost ◽  
Learning Activity ◽  
Attractive Alternative ◽  
Multimodal Learning ◽  
Analysis Techniques ◽  
Core Skills ◽  
Educational Challenges ◽  
Collaboration And Communication ◽  
Multimodal Learning Analytics ◽  
Learning Analysis

Currently, the improvement of core skills appears as one of the most significant educational challenges of this century. However, assessing the development of such skills is still a challenge in real classroom environments. In this context, Multimodal Learning Analysis techniques appear as an attractive alternative to complement the development and evaluation of core skills. This article presents an exploratory study that analyzes the collaboration and communication of students in a Software Engineering course, who perform a learning activity simulating Scrum with Lego® bricks. Data from the Scrum process was captured, and multidirectional microphones were used in the retrospective ceremonies. Social network analysis techniques were applied, and a correlational analysis was carried out with all the registered information. The results obtained allowed the detection of important relationships and characteristics of the collaborative and Non-Collaborative groups, with productivity, effort, and predominant personality styles in the groups. From all the above, we can conclude that the Multimodal Learning Analysis techniques offer considerable feasibilities to support the process of skills development in students.

scholarly journals Multimodal Learning Analytics and Education Data Mining: using computational technologies to measure complex learning tasks

2016 ◽  
Vol 3 (2) ◽  
pp. 220-238 ◽  
Author(s):  
Paulo Blikstein ◽  
Marcelo Worsley
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Online Courses ◽  
Educational Data Mining ◽  
Multimodal Learning ◽  
Learning Tasks ◽  
Education Data ◽  
Multimodal Learning Analytics ◽  
High Degree ◽  
Learning Analysis

New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics and educational data mining has been focused on online courses and cognitive tutors, both of which provide a high degree of structure to the tasks, and are restricted to interactions that occur in front of a computer screen. In this paper, we argue that multimodal learning analytics can offer new insights into students’ learning trajectories in more complex and open-ended learning environments. We present several examples of this work and its educational application.

scholarly journals Multimodal Learning Analytics to Inform Learning Design: Lessons Learned from Computing Education

2020 ◽  
Vol 7 (3) ◽  
pp. 79-97
Author(s):  
Katerina Mangaroska ◽  
Kshitij Sharma ◽  
Dragan Gašević ◽  
Michalis Giannakos
Keyword(s):  
Learning Analytics ◽  
Lessons Learned ◽  
Learning Activity ◽  
Physiological Data ◽  
Learning Design ◽  
Multimodal Learning ◽  
Process Data ◽  
Computing Education ◽  
Log Data ◽  
Multimodal Learning Analytics

Programming is a complex learning activity that involves coordination of cognitive processes and affective states. These aspects are often considered individually in computing education research, demonstrating limited understanding of how and when students learn best. This issue confines researchers to contextualize evidence-driven outcomes when learning behaviour deviates from pedagogical intentions. Multimodal learning analytics (MMLA) captures data essential for measuring constructs (e.g., cognitive load, confusion) that are posited in the learning sciences as important for learning, and cannot effectively be measured solely with the use of programming process data (IDE-log data). Thus, we augmented IDE-log data with physiological data (e.g., gaze data) and participants’ facial expressions, collected during a debugging learning activity. The findings emphasize the need for learning analytics that are consequential for learning, rather than easy and convenient to collect. In that regard, our paper aims to provoke productive reflections and conversations about the potential of MMLA to expand and advance the synergy of learning analytics and learning design among the community of educators from a post-evaluation design-aware process to a permanent monitoring process of adaptation.

scholarly journals Advanced Sensors Technology in Education

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4155
Author(s):  
González Crespo ◽  
Burgos
Keyword(s):  
Learning Environments ◽  
Secondary Students ◽  
Low Cost ◽  
Multimodal Learning ◽  
Depth Cameras ◽  
Martial Art ◽  
Multimodal Learning Analytics ◽  
Soccer Kick ◽  
The Mind ◽  
Oral Presentations

The topic presented will show how different kinds of sensors can help to improve our skills in learning environments. When we open the mind and let it take the control to be creative, we can think how a martial art would be improved with registered sensors, or how a person may dance with machines to improve their technique, or how you may improve your soccer kick for a penalties round. The use of sensors seems easy to imagine in these examples, but their use is not limited to these types of learning environments. Using depth cameras to detect patterns in oral presentations, or improving the assessment of agility through low cost-sensors with multimodal learning analytics, or using computing devices as sensors to measure their impact on primary and secondary students’ performances are the focus of this study as well. We hope readers will find original ideas that allow them to improve and advance in their own researches.

scholarly journals Light Cross-Linkable Marine Collagen for Coaxial Printing of a 3D Model of Neuromuscular Junction Formation

Biomedicines ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 16
Author(s):  
Borja Sanz ◽  
Ane Albillos Sanchez ◽  
Bonnie Tangey ◽  
Kerry Gilmore ◽  
Zhilian Yue ◽  
...  
Keyword(s):  
Tissue Engineering ◽  
Neuromuscular Junction ◽  
Disease Transmission ◽  
Low Cost ◽  
Attractive Alternative ◽  
Waste Products ◽  
Environmentally Sustainable ◽  
Zoonotic Disease Transmission ◽  
And Migration

Collagen is a major component of the extracellular matrix (ECM) that modulates cell adhesion, growth, and migration, and has been utilised in tissue engineering applications. However, the common terrestrial sources of collagen carry the risk of zoonotic disease transmission and there are religious barriers to the use of bovine and porcine products in many cultures. Marine based collagens offer an attractive alternative and have so far been under-utilized for use as biomaterials for tissue engineering. Marine collagen can be extracted from fish waste products, therefore industry by-products offer an economical and environmentally sustainable source of collagen. In a handful of studies, marine collagen has successfully been methacrylated to form collagen methacrylate (ColMA). Our work included the extraction, characterization and methacrylation of Red Snapper collagen, optimisation of conditions for neural cell seeding and encapsulation using the unmodified collagen, thermally cross-linked, and the methacrylated collagen with UV-induced cross-linking. Finally, the 3D co-axial printing of neural and skeletal muscle cell cultures as a model for neuromuscular junction (NMJ) formation was investigated. Overall, the results of this study show great potential for a novel NMJ in vitro 3D bioprinted model that, with further development, could provide a low-cost, customizable, scalable and quick-to-print platform for drug screening and to study neuromuscular junction physiology and pathogenesis.

scholarly journals Detecting Drowsy Learners at the Wheel of e-Learning Platforms with Multimodal Learning Analytics

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ryosuke Kawamura ◽  
Shizuka Shirai ◽  
Noriko Takemura ◽  
Mehrasa Alizadeh ◽  
Mutlu Cukurova ◽  
...  
Keyword(s):  
Learning Analytics ◽  
Multimodal Learning ◽  
E Learning ◽  
Learning Platforms ◽  
Multimodal Learning Analytics

NUMERICAL ANALYSIS OF FLOW BEHAVIOR IN GAS-LIQUID CYLINDRICAL CYCLONE (GLCC©*) SEPARATORS WITH INLET DESIGN MODIFICATIONS

2021 ◽  
pp. 1-27
Author(s):  
Srinivas Swaroop Kolla ◽  
Ram S. Mohan ◽  
Ovadia Shoham
Keyword(s):  
Numerical Study ◽  
Flow Behavior ◽  
Low Cost ◽  
Field Application ◽  
Attractive Alternative ◽  
Inlet Section ◽  
Wide Range ◽  
Cylindrical Cyclone ◽  
Light Oil ◽  
Section Design

Abstract The Gas-Liquid Cylindrical Cyclone (GLCC©*) is a simple, compact and low-cost separator, which provides an economically attractive alternative to conventional gravity-based separators over a wide range of applications. More than 6,500 GLCC©'s have been installed in the field to date around the world over the past 2 decades. The GLCC© inlet section design is a key parameter, which is crucial for its performance and proper operation. The flow behavior in the GLCC© body is highly dependent on the fluid velocities generated at the reduced area nozzle inlet. An earlier study (Kolla et al. [1]) recommended design modifications to the inlet section, based on safety and structural robustness. It is important to ensure that these proposed configuration modifications do not adversely affect the flow behavior at the inlet and the overall performance of the GLCC©. This paper presents a numerical study utilizing specific GLCC© field application working under 3 different case studies representing the flow entering the GLCC, separating light oil, steam flooded wells in Minas, Indonesia. Commercially available Computational Fluid Dynamics (CFD) software is utilized to analyze the hydrodynamics of flow with the proposed modifications of the inlet section for GLCC© field applications.

Heavy Fluoride Glasses as an Alternative to Crystals in High Energy Physics Calorimetry

1994 ◽  
Vol 348 ◽  
Author(s):  
I. Dafinei ◽  
E. Auffray ◽  
P. Lecoq ◽  
M. Schneegans
Keyword(s):  
High Energy Physics ◽  
Low Cost ◽  
High Energy ◽  
Attractive Alternative ◽  
Close Collaboration ◽  
Fluoride Glasses ◽  
Different Types ◽  
Electromagnetic Calorimeters ◽  
Physics Experiments ◽  
Energy Physics

ABSTRACTIn the quest for low cost scintillators to equip the very large electromagnetic calorimeters for future High Energy Physics experiments, scintillating glasses can offer an attractive alternative to crystals. The expected production price is indeed supposed to be reduced as compared to crystals, especially for very large volumes. An intense R&D effort has been made by the Crystal Clear collaboration to develop heavy scintillating fluoride glasses in close collaboration with the industry. Results will be shown on the fluorescence and scintillation properties as well as on the radiation resistance of different types of fluoride glasses. Ideas about possible improvement of present performances will also be given.

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