Posts tagged Grade (education)

You are the only lecturer that helps keep us honest, as far as I can see.

A final year student enrolled in Peter Mellalieu’s freshman course, innovation and entrepreneurship, BSNS 5391

For instance, my assessment policy includes:

  • No grades are allocated to an assignment in a student’s gradebook until they have submitted their assignment to http://turnitin.com
  • No grades are allocated to an assignment if the writing quality falls below a threshold of 24/30 marks on the writing quality assessment rubric. Students are required to rewrite to gain the ORIGINAL mark I first noted in my ‘offline’ gradebook. (See Haswell, 1983; Maryvale, n.d.)

Haswell, R. H. (1983). Minimal marking. College English, 45(6), 600-604.

Maryvale Elementary. (n.d.). Student Friendly Writing Rubric (From a School Using the 6 Traits of Writing).

Mellalieu, P. J. (2011, July 18). Creating the future through “Innovation and Entrepreneurship” – BSNS 5391 commencing August 2011. Department of Management and Marketing. Retrieved July 18, 2011, from http://thedomm.com/2011/07/18/creating-the-future-through-innovation-and-entrepreneurship-bsns-5391-commencing-august-2011/

Mellalieu, P. J. (2010a, June 25). My teaching philosophy (1997). Innovation & chaos … in search of optimality. Retrieved March 5, 2011, from http://pogus.tumblr.com/post/731293852/teaching-philosophy

Mellalieu, P. J. (2010b, September 14). My teaching strategy for first-year courses. Innovation & chaos … in search of optimality. Retrieved March 5, 2011, from http://pogus.tumblr.com/post/1117803030/my-teaching-strategy-for-first-year-courses

Mellalieu, P. J. (2008). Writing to learn argument and persuasion: A “Trojan Horse” for promoting the adoption of “Writing Across the Curriculum” (WAC) principles (Working paper). Auckland, NZ: Unitec New Zealand Centre for Innovation & Entrepreneurship. Retrieved from http://web.me.com/petermellalieu/Teacher/Blog/Entries/2009/9/30_Slide_show__Writing_to_learn_argument_and_persuasion.html

Related articles

Enhanced by Zemanta
A Decision Support System for predicting Retention, eXcellence, & Success: The Dashboard for ReXS: Late November, I began a journey exploring how I could apply data mining/machine learning to help predict students’ performance throughout their semester of studies. The intention is to gain information within the first few weeks of the semester and develop prognoses for their future performance. Over the last few weeks I have been testing, calibrating and experimenting with a spreadsheet-based Decision Support System assembled on the relationships I identified from my analyses of last semester’s student performance data. The image shows the dashboard that a student uses to ‘drive’ the DSS. The cells in green show where the user enters data, including: The grade they achieved for a specific assignment The actual class mean and standard deviation for a specific assignment. If the data is not available, the statistics from a prediction dataset are used. The DSS displays several outputs for the user: Under MY GRADES (in orange): the per cent mark, letter grade, and weighted contribution to the final course grade - nothing special there! Under CLASS PERFORMANCE (in blue): an estimate of the student’s relative placing in the class, measured by the per cent of students below them in the class. This is derived simply from their mark and the class statistics (assuming a normal distribution of grades) Under PREDICTED GRADES (Yellow): an estimate for the student’s grades for all outstanding assignments in the course. This also includes a prediction of the student’s overall relative placing in the class. This is the ‘magic’ part of the DSS. In addition, the student can enter their actual grades and conduct ‘what if’ analyses to explore what grade for assignment components they need to achieve to gain their target grade for the course as a whole. Tabs at the bottom of the DSS dashboard permit the user to explore the inner workings of the DSS. These inner workings include the prediction equations resulting from the machine learning data analysis: Predictors, Class stats, etc. The Babbage tab is a giant spreadsheet that shows all the calculations in full. Project summary Mellalieu, P. J. (2010, December 6). A Decision Support System for predicting success, excellence, and retention from students’ early course performance: a machine learning approach in a tertiary education programme in innovation and entrepreneurship: Part 1: Project summary. Innovation & chaos … in search of optimality. Retrieved January 10, 2011, from http://pogus.tumblr.com/post/2110849868/a-decision-support-system-for-predicting-success Download the Rexs .xls spreadsheet here Mellalieu, P. J. (2011, April 26). ReXS: Decision Support System for Retention, eXcellence, and Success (.xls spreadsheet). Peter Mellalieu - Teacher. Retrieved April 26, 2011, from http://preview.tinyurl.com/rexsdss1-2 Related information Culver, T. (2010, January 21). Setting student retention goals and developing student retention strategy. Noel-Levitz. Retrieved January 23, 2011, from http://blog.noellevitz.com/2011/01/21/6-keys-setting-realistic-student-retention-goals-developing-successful-student-retention-strategy/ Culver, T. (2011). Mid-Year Retention Indicators Report for Two-Year and Four-Year, Public and Private Institutions: Benchmark Research Study Conducted Fall 2010. Higher Ed Benchmarks. Noel-Levitz. Retrieved from https://www.noellevitz.com/NR/rdonlyres/C72E0608-2024-4175-9A94-9669C394253E/0/2011MIDYEARINDICATORSREPORT.pdf Related articles Paper for PedR meeting: The effect of time-on-task on student grades and grade expectations (drbadgr.wordpress.com) Tackling the Retention Challenge: Defining and Delivering a Unique Student Experience (hollymccracken.wordpress.com) Retention Revisited: Can Staying Back Move Students Forward? (huffingtonpost.com) What Do Selective Colleges Look for in an Applicant? The Academic Record (education.com) Living the College Life: How Much Should I Work at a Part-Time Job? (education.com) Subject Time Machine: Reading (vcmsvoice.wordpress.com) Why College is Getting Easier (redstateprogressive.com) Professor Discovers 2010 Grading Errors; Class Ranks Get Reworked in April 2011 (abovethelaw.com) Roadmap to College: What Happens If I Get Waitlisted? (education.com) A Failed Extra Credit Offer (mathequality.wordpress.com) To go, or not to go. This is the question. (ubiquitousdark7.wordpress.com) Questionable Attitudes Toward Education (kristenargentina.wordpress.com) Grades are just numbers. (cameraforthoughts.wordpress.com)

A Decision Support System for predicting Retention, eXcellence, & Success: The Dashboard for ReXS:

Late November, I began a journey exploring how I could apply data mining/machine learning to help predict students’ performance throughout their semester of studies. The intention is to gain information within the first few weeks of the semester and develop prognoses for their future performance.

Over the last few weeks I have been testing, calibrating and experimenting with a spreadsheet-based Decision Support System assembled on the relationships I identified from my analyses of last semester’s student performance data.

The image shows the dashboard that a student uses to ‘drive’ the DSS. The cells in green show where the user enters data, including:

  • The grade they achieved for a specific assignment
  • The actual class mean and standard deviation for a specific assignment. If the data is not available, the statistics from a prediction dataset are used.

The DSS displays several outputs for the user:

  • Under MY GRADES (in orange): the per cent mark, letter grade, and weighted contribution to the final course grade - nothing special there!
  • Under CLASS PERFORMANCE (in blue): an estimate of the student’s relative placing in the class, measured by the per cent of students below them in the class. This is derived simply from their mark and the class statistics (assuming a normal distribution of grades)
  • Under PREDICTED GRADES (Yellow): an estimate for the student’s grades for all outstanding assignments in the course. This also includes a prediction of the student’s overall relative placing in the class. This is the ‘magic’ part of the DSS.

In addition, the student can enter their actual grades and conduct ‘what if’ analyses to explore what grade for assignment components they need to achieve to gain their target grade for the course as a whole.

Tabs at the bottom of the DSS dashboard permit the user to explore the inner workings of the DSS. These inner workings include the prediction equations resulting from the machine learning data analysis: Predictors, Class stats, etc. The Babbage tab is a giant spreadsheet that shows all the calculations in full.

Project summary

Mellalieu, P. J. (2010, December 6). A Decision Support System for predicting success, excellence, and retention from students’ early course performance: a machine learning approach in a tertiary education programme in innovation and entrepreneurship: Part 1: Project summary. Innovation & chaos … in search of optimality. Retrieved January 10, 2011, from http://pogus.tumblr.com/post/2110849868/a-decision-support-system-for-predicting-success

Download the Rexs .xls spreadsheet here

Mellalieu, P. J. (2011, April 26). ReXS: Decision Support System for Retention, eXcellence, and Success (.xls spreadsheet). Peter Mellalieu - Teacher. Retrieved April 26, 2011, from http://preview.tinyurl.com/rexsdss1-2

Related information

Culver, T. (2010, January 21). Setting student retention goals and developing student retention strategy. Noel-Levitz. Retrieved January 23, 2011, from http://blog.noellevitz.com/2011/01/21/6-keys-setting-realistic-student-retention-goals-developing-successful-student-retention-strategy/ Culver, T. (2011). Mid-Year Retention Indicators Report for Two-Year and Four-Year, Public and Private Institutions: Benchmark Research Study Conducted Fall 2010. Higher Ed Benchmarks. Noel-Levitz. Retrieved from https://www.noellevitz.com/NR/rdonlyres/C72E0608-2024-4175-9A94-9669C394253E/0/2011MIDYEARINDICATORSREPORT.pdf

Related articles

Enhanced by Zemanta