scholarly journals Data Mining for Information Literacy

2012 ◽  
pp. 265-297 ◽  
Author(s):  
Bettina Berendt
Keyword(s):  
Data Mining ◽  
Information Literacy

Data Mining: Associating Information Literacy with Regulating Rules

2011 ◽  
Vol 204-210 ◽  
pp. 183-188
Author(s):  
Chia Chia Lin ◽  
Dong Her Shih
Keyword(s):  
Data Mining ◽  
Information Literacy ◽  
Association Rules ◽  
Main Tool ◽  
The Internet ◽  
Valuable Resource ◽  
Self Defense ◽  
Opportunities For Learning ◽  
Potential Problems ◽  
The Relationship

The Internet is filled with opportunities for learning, communicating, and sharing information. It is a valuable resource for children and adults alike. Like any large community, however, the online world presents some underlying risks, especially for children. Parents need to be aware of some of the potential problems their children could encounter, and try to take adequate measures to protect their children from injury. In this paper, research will try to explore the relationship between parent’s information literary, the confidence in child’s ability of self-defense on the internet, and adequate measures to promote child using the internet more effectively. Association rules, a kind of data mining strategies, will be the main tool to manipulate the dataset. Apriori, a classic algorithm for learning association rules, is designed to operate on databases containing transactions.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Diagnostik von ICT Literacy: Messen Multiple-Choice-Aufgaben und simulationsbasierte Aufgaben vergleichbare Konstrukte?

Diagnostica ◽  
2020 ◽  
Vol 66 (3) ◽  
pp. 147-157
Author(s):  
Martin Senkbeil ◽  
Jan Marten Ihme
Keyword(s):  
Information Literacy ◽  
Large Scale ◽  
Panel Study ◽  
Multiple Choice ◽  
Ict Literacy ◽  
Computer And Information Literacy ◽  
Literacy Test

Zusammenfassung. ICT Literacy legt eine performanzbasierte Erfassung mit simulierten und interaktiven Testaufgaben nahe. Der vorliegende Beitrag untersucht, ob mit Multiple-Choice (MC)-Aufgaben ein vergleichbares Konstrukt wie mit Simulationsaufgaben erfasst wird. Hierfür wurden die Testergebnisse zweier Instrumente aus aktuellen Large-Scale-Studien gegenübergestellt, die an N = 2 075 Jugendlichen erhoben wurden: der auf MC-Aufgaben basierende ICT Literacy-Test für Klasse 9 des Nationalen Bildungspanels (National Educational Panel Study, NEPS) und der simulationsbasierte Kompetenztest der internationalen Schulleistungsstudie ICILS 2013 (International Computer and Information Literacy Study). Die Analysen unterstützen die Gültigkeit der Konstruktinterpretation des MC-basierten Tests in NEPS. Im Sinne der konvergenten Evidenz korrelieren die MC-Aufgaben substanziell mit den computer- und simulationsbasierten Aufgaben in ICILS 2013 (.68 ≤  r ≤ .90). Weiterhin ergeben sich positive und für beide Tests vergleichbar hohe Korrelationen mit ICT-bezogenen Schülermerkmalen (z. B. Selbstwirksamkeit). Weiterführende Analysen zum Zusammenhang mit allgemeinen kognitiven Fähigkeiten zeigen zudem, dass ICT Literacy und kognitive Grundfähigkeiten distinkte Faktoren repräsentieren.

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

Post, Mine, and Be Disturbed: Social Media Data Mining

PsycCRITIQUES ◽  
2016 ◽  
Vol 61 (51) ◽  
Author(s):  
Daniel Keyes
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Media Data ◽  
Media Data
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