The assessment of the quality of sugar using electronic tongue and machine learning algorithms

The correct classification of sugar according to its physico-chemical characteristics directly influences the value of the product and its acceptance by the market. This study shows that using an electronic tongue system along with established techniques of supervised learning leads to the correct classification of sugar samples according to their qualities. In this paper, we offer two new real, public and non-encoded sugar datasets whose attributes were automatically collected using an electronic tongue, with and without pH controlling. Moreover, we compare the performance achieved by several established machine learning methods. Our experiments were diligently designed to ensure statistically sound results and they indicate that k-nearest neighbors method outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison. © 2012 IEEE.

E-Learning, E-Teaching und E-Assessment in der Hochschullehre

Wie lässt sich die Qualität des Lernens, Lehrens und Prüfens durch den Einsatz neuer Medien steigern? Übertragen auf die Komponenten und Bausteine des E-Education-Prozesses heißt das: - Mit welchen digitalen Materialien und Komponenten ist eine effiziente computergestützte Inhaltserschließung möglich? - Mit welcher Organisationsform der Lehre kann ein maximaler Qualitätsgewinn für die traditionelle Präsenzlehre erzielt werden? - Wie lassen sich traditionelle Prüfungsformen durch digitale Medien bereichern und mit technischer Hilfe auswerten? - Wie müssen digitale Inhalte beschaffen sein, um einen Mehrwert für den Lehr- und Lernprozess, möglicherweise in Selbstlernszenarien, zu erzielen? - Wie muss eine Lernplattformaufgebaut sein, um E-Education in ihrer gesamten Breite zu unterstützen und eine hohe Akzeptanz zu erreichen? Die Autoren sind Hauptakteure des Marburger „Linguistik Engineering Teams“, das in sich das gesamte Know-How für die Entwicklung und Nutzung verschiedener Lehr- und Lernszenarien vereinigt: von der Konzeption über die Programmierung bis hin zur Nutzung in allen denkbaren Varianten. Ihr Buch ist ein Leitfaden, der aufzeigt, wie mit einem komplexen E-Education-System nicht nur Qualitäts-, sondern auch Kapazitätsgewinne sowie erhebliche Aufwandsreduktionen erreicht werden können.

The Comprehensive Assessment of Leadership for Learning: A Next Generation Formative Evaluation and Feedback System

Recent developments in federal policy have prompted the creation of state evaluation frameworks for principals and teachers that hold educators accountable for effective practices and student outcomes. These changes have created a demand for formative evaluation instruments that reflect current accountability pressures and can be used by schools to focus school improvement and leadership development efforts. The Comprehensive Assessment of Leadership for Learning (CALL) is a next generation, 360-degree on-line assessment and feedback system that reflect best practices in feedback design. Some unique characteristics of CALL include a focus on: leadership distributed throughout the school rather than as carried out by an individual leader; assessment of leadership tasks rather than perceptions of leadership practice; a focus on larger complex systems of middle and high school; and transparency of assessment design. This paper describes research contributing to the design and validation of the CALL survey instrument.

A Step in the Right Direction: Development of the Computerized Assessment for Preschool Social Emotional Learning (CAPSEL)

Invited commentary on "Computerizing Social-Emotional Assessment for School Readiness".

GLMP for automatic assessment of DFS algorithm learning

We describe how to use a Granular Linguistic Model of a Phenomenon (GLMP) to assess e-learning processes. We apply this technique to evaluate algorithm learning using the GRAPHs learning environment.

Statistical Machine Learning for Automatic Assessment of Physical Activity Intensity Using Multi-axial Accelerometry and Heart Rate

This work explores the automatic recognition of physical activity intensity patterns from multi-axial accelerometry and heart rate signals. Data collection was carried out in free-living conditions and in three controlled gymnasium circuits, for a total amount of 179.80 h of data divided into: sedentary situations (65.5%), light-to-moderate activity (17.6%) and vigorous exercise (16.9%). The proposed machine learning algorithms comprise the following steps: time-domain feature definition, standardization and PCA projection, unsupervised clustering (by k-means and GMM) and a HMM to account for long-term temporal trends. Performance was evaluated by 30 runs of a 10-fold cross-validation. Both k-means and GMM-based approaches yielded high overall accuracy (86.97% and 85.03%, respectively) and, given the imbalance of the dataset, meritorious F-measures (up to 77.88%) for non-sedentary cases. Classification errors tended to be concentrated around transients, what constrains their practical impact. Hence, we consider our proposal to be suitable for 24 h-based monitoring of physical activity in ambulatory scenarios and a first step towards intensity-specific energy expenditure estimators

An approach to automatic learning assessment based on the computational theory of perceptions

E-learning systems output a huge quantity of data on a learning process. However, it takes a lot of specialist human resources to manually process these data and generate an assessment report. Additionally, for formative assessment, the report should state the attainment level of the learning goals defined by the instructor. This paper describes the use of the granular linguistic model of a phenomenon (GLMP) to model the assessment of the learning process and implement the automated generation of an assessment report. GLMP is based on fuzzy logic and the computational theory of perceptions. This technique is useful for implementing complex assessment criteria using inference systems based on linguistic rules. Apart from the grade, the model also generates a detailed natural language progress report on the achieved proficiency level, based exclusively on the objective data gathered from correct and incorrect responses. This is illustrated by applying the model to the assessment of Dijkstra’s algorithm learning using a visual simulation-based graph algorithm learning environment, called GRAPHs

Using multimedia and peer assessment to promote collaborative e-learning

Collaborative e-learning is increasingly appealing as a pedagogical approach that can positively affect student learning. We propose a didactical model that integrates multimedia with collaborative tools and peer assessment to foster collaborative e-learning. In this paper, we explain it and present the results of its application to the “International Seminars on Materials Science” online course. The proposed didactical model consists of five educational activities. In the first three, students review the multimedia resources proposed by the teacher in collaboration with their classmates. Then, in the last two activities, they create their own multimedia resources and assess those created by their classmates. These activities foster communication and collaboration among students and their ability to use and create multimedia resources. Our purpose is to encourage the creativity, motivation, and dynamism of the learning process for both teachers and students.

Mentors’ implication in clinical learning and assessment of nursing students

Nursing Education in Europe is regulated by law from 2005. Clinical learning comprises at least 50% of the total degree program in nursing. It is necessary rely on professionals nurses involved in the learning process and skills development assessment. The level of implication in learning processes of these professional nurses is very important to ensure good results.

Realizing the potential : improving postsecondary teaching, learning and assessment /

"Grant R117G10037"--T.p. verso.

Field assessment of social learning approach to teaching adolescents about alcohol and driving. Final report.

National Highway Traffic Safety Administration, Washington, D.C.