Technology Coordination: Data and Information Services
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Explaining Big Data : NPR

Explaining Big Data : NPR | Technology Coordination: Data and Information Services | Scoop.it
What "Big Data" really means in under 9 minutes. It's a video, so you can just sit back and watch as Christopher Barnatt digs into this topic.
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Marcia Pereira's curator insight, March 11, 2013 12:54 PM

Big Data explained clearly. What should our school districts do with Big Data?  How?  

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Number Crunchers & the Big Data Revolution | The Big Picture

Number Crunchers & the Big Data Revolution | The Big Picture | Technology Coordination: Data and Information Services | Scoop.it
The 'Big Data' Revolution: How Number Crunchers Can Predict Our Lives Reviews: Every decade, there are a handful of books that change the way you look.
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Gonna read this this week!

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Big Data in ArcGIS | Esri Video

Big Data in ArcGIS | Esri Video | Technology Coordination: Data and Information Services | Scoop.it
Mansour Raad demonstrates the ArcGIS tools for analyzing and querying big data and billions of records. (RT @Esri: Big Data in ArcGIS: @mraad demonstrates ArcGIS tools for analyzing big data and billions of records.
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Rescooped by Marcia Pereira from Students with dyslexia & ADHD in independent and public schools
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Mass School districts working hard to reach at-risk students. dropouts dip to 2-decade lows - Boston.com

Mass School districts working hard to reach at-risk students. dropouts dip to 2-decade lows - Boston.com | Technology Coordination: Data and Information Services | Scoop.it
Mass. high school graduation rate edges up, dropouts dip to two-decade lowsBoston.comThe state reported that all subgroups of students, except those with limited English proficiency, showed an improvement in graduation rates over the past year.

Via Lou Salza
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Rescooped by Marcia Pereira from Interactive Teaching and Learning
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Using analytics to identify students at risk

Using analytics to identify students at risk | Technology Coordination: Data and Information Services | Scoop.it

Fusch, D. and Jones, J. (2012) Vetting Early Alert Technologies, Academic Impressions, January 20

"Daniel Fusch of Academic Impressions interviewed Jennifer Jones of Minnesota State University about what to look for when deciding on software to identify students at risk.

This is an excellent article that identifies key questions to ask before investing in learning analytics software."


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Secondary At-Risk Students' Online Course Self-Confidence Levels: An Exploratory Study

Many high school seniors are either not graduating on time or not graduating at all. A computer-based course recovery program could help many struggling districts in this area of need. This study employed a non-experimental quantitative approach to address possible relationships between computer-based instruction and learning outcomes. The sample (N=59) included at-risk students (ages 15-18), randomly selected from the largest high school within the sponsoring district of Saginaw, Michigan. Participants were administered a pre-survey prior to course enrollment to measure self-efficacy/motivational levels with regard to academics in general, as well as perceived success they would have in the computer-based course. Following the completed computer-based course the same post-test survey tool was given to compare pre-survey results as well as an ancillary questionnaire. Quantitative data was analyzed through the administration of one-sample nonparametric tests and linear regression models (ANOVA). In general, through ANOVA analysis all 6 MSLQ dependent variables revealed no observed significance levels below the statistically significant level of .05. The act of taking an online course could not successfully predict an increase in at-risk student self-confidence for learning and grade or completion performance outcomes. Significant differences were shown to exist in respect to demographic group's part of this study. The ANOVA tests in regard to the construct of task value revealed that age groups differences revealed a p = .048, which did indicate statistical significance. Furthermore, testing in regard to intrinsic goal orientation and gender differences showed a p = .025, while additional analysis disclosed a significance level of p = .017 when comparing self-efficacy for learning and performance amongst the three grade levels studied.

 

Recommendations include that both educators and online course content providers make more efforts to have courseware presented in a higher interest manner to this age group, increase male intrinsic goal orientation, and contribute to increasing underclassmen's self-efficacy for learning and performance in relation to online education. Future research should consider incorporating a larger sample size, conducting the study in a cross district capacity at both suburban and urban districts, and the inclusion of qualitative questions and/or open response questions within the study apparatus.

 

Source: http://search.proquest.com/docview/1013830400/abstract?accountid=36155

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Common Core Standards Will Benefit At-Risk Students

Common Core Standards Will Benefit At-Risk Students | Technology Coordination: Data and Information Services | Scoop.it

Whole Child Education

 

Consistency in the knowledge and skills taught in schools across the country promises to reduce barriers to students and families. It will greatly benefit students who find themselves in different schools throughout their educational life.


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Mel Riddile's curator insight, January 31, 2013 3:24 PM

Key Point:


"The Common Core State Standards will create an educational environment in the United States that is more accessible for students and understandable for parents. Consistency as well as more opportunities and tools for greater collaboration among students, parents, and educators will enhance education for all students and remove barriers for parents and students."

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Number Crunchers & the Big Data Revolution | The Big Picture

Number Crunchers & the Big Data Revolution | The Big Picture | Technology Coordination: Data and Information Services | Scoop.it
The 'Big Data' Revolution: How Number Crunchers Can Predict Our Lives Reviews: Every decade, there are a handful of books that change the way you look.
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Tony Hirst joins the School of Data | Open Knowledge Foundation ...

Tony will be working part time for the School of Data in shaping the materials, masterminding the blog and running workshops. He'll also be out and about on site visits to learn more about how organisations use data “in the ...
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What's to be Done about Big Data? - Forbes

What's to be Done about Big Data? - Forbes | Technology Coordination: Data and Information Services | Scoop.it
Forbes
What's to be Done about Big Data?
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How do school districts fit in? 

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Study suggests keeping students in high school longer, better 'at-risk ...

Study suggests keeping students in high school longer, better 'at-risk ... | Technology Coordination: Data and Information Services | Scoop.it
Raising the age at which students can legally drop out of school could determine their future earning power and quality of life, according to a new paper by U of T Mississauga economics professor Philip Oreopoulos.
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Study suggests keeping students in high school longer, better 'at-risk ...

Study suggests keeping students in high school longer, better 'at-risk ... | Technology Coordination: Data and Information Services | Scoop.it
Raising the age at which students can legally drop out of school could determine their future earning power and quality of life, according to a new paper by U of T Mississauga economics professor Philip Oreopoulos.
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Smarter Education: Predictive analytics can help schools quickly identify at-risk students

Smarter Education: Predictive analytics can help schools quickly identify at-risk students | Technology Coordination: Data and Information Services | Scoop.it
For years, marketers have used sophisticated software to track consumers’ buying habits and web browsing activity, then crunch this information and make intelligent predictions to target their sales messages more effectively.

Via EDTECH@UTRGV
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Data Science: What's in a Name?

Data Science: What's in a Name? | Technology Coordination: Data and Information Services | Scoop.it
Explore. Hypothesize. Test. Repeat.That's what scientists do.
Marcia Pereira's insight:

Potential study area

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