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Judy Brophy

Stephen Downes: The Role of the Educator - 0 views

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    it is misleading to suggests that all, or even most, aspects of providing an education should, or could, be placed into the hands of [teachers] Historically, it has been impractical to break up the roles of the teacher. You need a certain scale even to have a separate person assigned as a librarian or an audio-visual coordinator. You need a much greater scale, not to mention much better coordination, to have separate people assigned as lecturers, coaches, theorizers and evaluators. Yet relatively few of these roles need to be performed in person, and most of them scale pretty well. This means that with improved information and communications technologies we can begin to rethink how we've organized labor in education. This is in fact what is happening online, at least, outside the circles of formal education
Jenny Darrow

Cell Size and Scale - 0 views

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    Learn.Genetics: cell size and scale
Judy Brophy

Solving College With Big Data - 1 views

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    Coursera is a platform for instruction, discussion and grading at Internet scale. It extends the influence of universities around the world, and it provides them data-driven insights into how to adapt higher education to the global promise of the Internet.
Jenny Darrow

MOOC completion rates - 0 views

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    Massive Open Online Courses (MOOCs) have the potential to enable free university-level education on an enormous scale. A concern often raised about MOOCs is that although thousands enrol for courses, a very small proportion actually complete the course. The release of information about enrollment and completion rates from MOOCs appears to be ad hoc at the moment - that is, official statistics are not published for every course. This data visualisation draws together information about enrollment numbers and completion rates from across online news stories and blogs.
Jenny Darrow

Campus Focus - 0 views

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    From an LMS provider's standpoint, the more open and flexible the LMS, the more it can be integrated with other programs for robust analysis of student activity and interaction.  According to Lou Pugliese, president of online learning solutions provider  Moodlerooms, that kind of integration is needed. Technologies exist to measure student data and interactions on a large scale, Pugliese says: The focus now is how to effectively collect data and conduct reporting on-demand within the LMS. "Over the past ten years, the LMS has managed to record the most basic of student interactions and activity, but we've barely scratched the surface in enabling universities to analyze data on an institutional level," says Pugliese. "However, new developments in analytical technologies will provide educators with the ability to measure interactions within the ever-popular collaborative tools present in today's LMS environments. Moving beyond simple traffic reporting to more comprehensive online behaviour analysis will be critical to make more effective intervention decisions."  
Matthew Ragan

COVERITLIVE.COM - Home - 1 views

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    Whether it's Live Blogging, hosting a weekly Question & Answer session or simply reporting on Breaking News, all readers agree: Live is Better. CoveritLive is already being used by thousands of bloggers and large media companies to engage millions of readers each month. Reviewers and some of the largest sites of the web agree no other software delivers ease of use, scale and reliability like CoveritLive.
Jenny Darrow

End of Semester Guide for Instructors - TOC: TCNJ Canvas Online Resources - 0 views

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    This is our first large scale production "end of semester" with Canvas. We tried to capture the most important points without overwhelming you with details. Additionally, as Instructure receives feedback, Canvas behavior may change from what is outlined here. We will do our best to keep you informed with the latest changes from Instructure. At this time, there is no set time limit for courses to exist in Canvas. The college is discussing an appropriate time frame. Until this time frame is decided, we will not be deleting courses. We still recommend taking steps to back up important items from your course, especially the grade book and course structure.
Jenny Darrow

https://www.insidehighered.com/sites/default/server_files/files/Teaching%20With%20Techn... - 0 views

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    The use of technology to deliver instruction is an idea whose time has come - though the extent of its use varies greatly. At some institutions, professors do little more than use learning management systems to record attendance and grades and to communicate with students. At the other end of the scale, millions of students study entirely online.
Jenny Darrow

Easy iPad Management for Education- At Scale! | Bright ideas - 0 views

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    "iPad deployment includes all of the steps between buying an iPad and beginning to use the device. For an individual's personal iPad, this is often a very fast and easy process lasting no longer than five minutes: You just open the box, turn it on, download the apps you want on your device, and go. But for a school, or district, with potentially hundreds of iPads-each one requiring a specialized list of apps that need to be purchased before protecting the devices with a case and sending them out to classrooms. As an administrator or teacher, there are a few things you will need to know before you deploy your iPads. Lucky for you, the eSpark Engineering team has created a five step road map to aid in this journey, highlighting the best practices and considerations relevant to deploying and supporting iPads in education environments."
Matthew Ragan

YouTube U. Beats YouSnooze Through - Online Learning - The Chronicle of Higher Education - 0 views

  • There are some college experiences that don't fit this mold. Many seminars and advanced courses are based on hands-on projects and small-scale discussions with professors. Those are undoubtedly valuable. But core classes tend not to be taught that way. The very classes that should establish a student's base understanding of a subject are taught like assembly lines—lecture, problem set, exam—with no quality control. Sure, the product's quality is graded, but nothing is done about defective understanding as the student is pushed down the line.
  • Students don't retain anything because they didn't intuitively understand it to begin with.
  • Why aren't we using the 300-person gathering at 10 a.m. every Tuesday and Thursday as an opportunity for active peer-to-peer instruction rather than a passive, one-size-fits-all lecture?
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  • Then the professor is freed to be an active participant in an interactive, peer-to-peer problem-solving powwow in the classroom.
  • Ten years from today, students will be learning at their own pace, with all relevant data being collected on how to optimize their learning and the content itself. Grades and transcripts will be replaced with real-time reports and analytics on what a student actually knows and doesn't know.
Matthew Ragan

Function list : Functions - Google Docs Help - 1 views

  • Frequency distribution
  • FREQUENCY(data, classes)
  • FILTER(sourceArray, arrayCondition_1, arrayCondition_2, ..., arrayCondition_30)
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  • SORT(data, keyColumn_1, ascOrDesc_1, keyColumn_2, ascOrDesc_2, ..., keyColumn_30, ascOrDesc_30)
  • Cross-workbook referenceImportRange(spreadsheet_key, [sheet!]range)
  • Elements based on criteriaCOUNTIF(range, criteria)
  • RANDBETWEEN (bottom, top)Returns an integer random number between bottom and top (inclusive).
  • ROUND(number, count)Rounds the given number to a certain number of decimal places according to valid mathematical criteria. Count (optional) is the number of the places to which the value is to be rounded. If the count parameter is negative, only the whole number portion is rounded. It is rounded to the place indicated by the count.
  • RAND()Returns a random number between 0 and 1.
  • AVERAGE(number_1, number_2, ... number_30)Returns the average of the arguments. Number_1, number_2, ... number_30 are numerical values or ranges. Text is ignored.
  • CONFIDENCE(alpha, STDEV, size)Returns the (1-alpha) confidence interval for a normal distribution. Alpha is the level of the confidence interval. STDEV is the standard deviation for the total population. Size is the size of the total population.
  • CORREL(data_1, data_2)Returns the correlation coefficient between two data sets. Data_1 is the first data set. Data_2 is the second data set.
  • COUNT(value_1, value_2, ... value_30)Counts how many numbers are in the list of arguments. Text entries are ignored. Value_1, value_2, ... value_30 are values or ranges which are to be counted.
  • COUNTA(value_1, value_2, ... value_30)Counts how many values are in the list of arguments. Text entries are also counted, even when they contain an empty string of length 0. If an argument is an array or reference, empty cells within the array or reference are ignored. value_1, value_2, ... value_30 are up to 30 arguments representing the values to be counted.
  • MAX(number_1, number_2, ... number_30)Returns the maximum value in a list of arguments. Number_1, number_2, ... number_30 are numerical values or ranges.
  • MEDIAN(number_1, number_2, ... number_30)Returns the median of a set of numbers. Number_1, number_2, ... number_30 are values or ranges, which represent a sample. Each number can also be replaced by a reference.
  • MIN(number_1, number_2, ... number_30)Returns the minimum value in a list of arguments. Number_1, number_2, ... number_30 are numerical values or ranges.
  • MODE(number_1, number_2, ... number_30)Returns the most common value in a data set. Number_1, number_2, ... number_30 are numerical values or ranges. If several values have the same frequency, it returns the smallest value. An error occurs when a value does not appear twice.
  • PERCENTILE(data, alpha)Returns the alpha-percentile of data values in an array. Data is the array of data. Alpha is the percentage of the scale between 0 and 1.
  • QUARTILE(data, type)Returns the quartile of a data set. Data is the array of data in the sample. Type is the type of quartile. (0 = Min, 1 = 25%, 2 = 50% (Median), 3 = 75% and 4 = Max.)
  • RANK(value, data, type)Returns the rank of the given Value in a sample. Data is the array or range of data in the sample. Type (optional) is the sequence order, either ascending (0) or descending (1).
  • STDEV(number_1, number_2, ... number_30)Estimates the standard deviation based on a sample. Number_1, number_2, ... number_30 are numerical values or ranges representing a sample based on an entire population.
  • STDEVP(number_1, number_2, ... number_30) Calculates the standard deviation based on the entire population. Number_1, number_2, ... number_30 are numerical values or ranges representing a sample based on an entire population.
  • Combines text stringsCONCATENATE(text_1, text_2, ..., text_30)Combines several text strings into one string. Text_1, text_2, ... text_30 are text passages that are to be combined into one string.
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    Google Spreadsheets Formula Help
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