How any institution can get started on learning analyticsJeremy Anderson
Two case studies from Bay Path University in developing predictive retention analytics at the course level and across the four-year college experience. Walks through the CRISP-DM framework and how it guided each project. Also shares resources for carrying out similar projects in Excel. Presented at NERCOMP 2021
Creating Wraparound Supports for Students through Internal PartnershipsJeremy Anderson
Presentation delivered to the Quality Matters East Regional Conference in 2020. Covered is a basic framework for developing analytics projects by combining stakeholders, IR, and IT.
In this study, the effect of combining variables from the different data sources for student academic performance prediction was examined using three state-of-the–art classifiers: Decision Tree (DT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The study examined the use of heterogeneous multi-model ensemble techniques to predict student academic performance based on the combination of these classifiers and three different data sources. A quantitative approach was used to develop the various base classifier models while the ensemble models were developed using stacked generalisation ensemble method in order to overcome the individual weaknesses of the different models. Variables were extracted from the institution’s Student Record System and Learning Management System (Moodle) and from a structured student questionnaire. At present, negligible work has been done using this integrated approach and ensemble techniques especially with aggregated learner data in performance prediction in HE. The empirical results obtained show that the ensemble models.........................
How any institution can get started on learning analyticsJeremy Anderson
Two case studies from Bay Path University in developing predictive retention analytics at the course level and across the four-year college experience. Walks through the CRISP-DM framework and how it guided each project. Also shares resources for carrying out similar projects in Excel. Presented at NERCOMP 2021
Creating Wraparound Supports for Students through Internal PartnershipsJeremy Anderson
Presentation delivered to the Quality Matters East Regional Conference in 2020. Covered is a basic framework for developing analytics projects by combining stakeholders, IR, and IT.
In this study, the effect of combining variables from the different data sources for student academic performance prediction was examined using three state-of-the–art classifiers: Decision Tree (DT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The study examined the use of heterogeneous multi-model ensemble techniques to predict student academic performance based on the combination of these classifiers and three different data sources. A quantitative approach was used to develop the various base classifier models while the ensemble models were developed using stacked generalisation ensemble method in order to overcome the individual weaknesses of the different models. Variables were extracted from the institution’s Student Record System and Learning Management System (Moodle) and from a structured student questionnaire. At present, negligible work has been done using this integrated approach and ensemble techniques especially with aggregated learner data in performance prediction in HE. The empirical results obtained show that the ensemble models.........................
The Learning Ecosystem – A Content Agnostic Adaptive Learning and Analytics System
Presentation from 'InFocus: Learner analytics and big data', a CDE technology symposium held at Senate House on 10 December 2013. Conducted by George Mitchell (Chief Operations Officer, CCKF Ltd, Dublin).
Audio of the session and more details can be found at www.cde.london.ac.uk.
The process of business to business (B2B) sales forecasting is a complex decision-making process. There are many approaches to support this process, but mainly it is still based on the subjective judgement of a decision-maker. The problem of B2B sales forecasting can be modeled as a binary classification problem (the deal will be closed or not). However, top performing machine learning models (ML models) are black box and do not support transparent reasoning. The purpose of this research was to develop an organizational model, with ML model, coupled with general explanation methods in its core, and support the decision-maker in the process of B2B sales forecasting.
Participatory approach of action design research was used to promote acceptance of the model among users. ML model was built following CRISP-DM methodology and utilized in Orange and R softwares.
ML model was developed in several design cycles involving users. Special focus is put on the availability and quality of data, which affects the performance of models and explanations. During the project we had repeated the design cycle several times, especially in the phases of problem understanding, data understanding and preparing. The developed model was evaluated in the company for several months. Results show that based on the explanations of the ML model predictions users’ forecasts improved. Furthermore, when users embrace the proposed ML model and its explanations, they change their initial beliefs, make more accurate B2B sales predictions and detect other features of the process, not included in the ML model.
The proposed model promotes understanding, foster debate and testing existing hypothesis and thus contributes to organizational learning. Furthermore, active participation of users in the process of development, validation and implementation, has shown to be beneficial in creating trust and promoting acceptance in practice.
What is geodemography and how can it be used in improving student recruitment? How can geodemography be used to improve predictive models? This session will introduce you to Descriptor PLUS, a service providing educationally-relevant geodemographic information
to admission and enrollment managers interested in knowing more about college selection, choice, and persistence. Descriptor PLUS was revised and expanded in 2011 and this session will review these important changes.
CDE InFocus Conference (London): Big data in education - theory and practiceMike Moore
Big Data in Education: Theory and Practice
Presented at the CDE InFocus Conference - London
December 10, 2013
Presented by Mike Moore, Sr. Advisory Consultant - Analytics
Desire2Learn, Inc.
Presentation by Russ Little. Provides an overview of Integrated Planning and Advising Systems (IPAS). Demonstrates how the Student Success Plan software and My Academic Plan (MAP) function, and evidence of their effectiveness.
The Learning Ecosystem – A Content Agnostic Adaptive Learning and Analytics System
Presentation from 'InFocus: Learner analytics and big data', a CDE technology symposium held at Senate House on 10 December 2013. Conducted by George Mitchell (Chief Operations Officer, CCKF Ltd, Dublin).
Audio of the session and more details can be found at www.cde.london.ac.uk.
The process of business to business (B2B) sales forecasting is a complex decision-making process. There are many approaches to support this process, but mainly it is still based on the subjective judgement of a decision-maker. The problem of B2B sales forecasting can be modeled as a binary classification problem (the deal will be closed or not). However, top performing machine learning models (ML models) are black box and do not support transparent reasoning. The purpose of this research was to develop an organizational model, with ML model, coupled with general explanation methods in its core, and support the decision-maker in the process of B2B sales forecasting.
Participatory approach of action design research was used to promote acceptance of the model among users. ML model was built following CRISP-DM methodology and utilized in Orange and R softwares.
ML model was developed in several design cycles involving users. Special focus is put on the availability and quality of data, which affects the performance of models and explanations. During the project we had repeated the design cycle several times, especially in the phases of problem understanding, data understanding and preparing. The developed model was evaluated in the company for several months. Results show that based on the explanations of the ML model predictions users’ forecasts improved. Furthermore, when users embrace the proposed ML model and its explanations, they change their initial beliefs, make more accurate B2B sales predictions and detect other features of the process, not included in the ML model.
The proposed model promotes understanding, foster debate and testing existing hypothesis and thus contributes to organizational learning. Furthermore, active participation of users in the process of development, validation and implementation, has shown to be beneficial in creating trust and promoting acceptance in practice.
What is geodemography and how can it be used in improving student recruitment? How can geodemography be used to improve predictive models? This session will introduce you to Descriptor PLUS, a service providing educationally-relevant geodemographic information
to admission and enrollment managers interested in knowing more about college selection, choice, and persistence. Descriptor PLUS was revised and expanded in 2011 and this session will review these important changes.
CDE InFocus Conference (London): Big data in education - theory and practiceMike Moore
Big Data in Education: Theory and Practice
Presented at the CDE InFocus Conference - London
December 10, 2013
Presented by Mike Moore, Sr. Advisory Consultant - Analytics
Desire2Learn, Inc.
Presentation by Russ Little. Provides an overview of Integrated Planning and Advising Systems (IPAS). Demonstrates how the Student Success Plan software and My Academic Plan (MAP) function, and evidence of their effectiveness.
[DSC Europe 22] Machine learning algorithms as tools for student success pred...DataScienceConferenc1
The goal of higher education institutions is to provide quality education to students. Predicting academic success and early intervention to help at-risk students is an important task for this purpose. This talk explores the possibilities of applying machine learning in developing predictive models of academic performance. What factors lead to success at university? Are there differences between students of different generations? Answers are given by applying machine learning algorithms to a data set of 400 students of three generations of IT studies. The results show differences between students with regard to student responsibility and regularity of class attendance and great potential of applying machine learning in developing predictive models.
Ellen Wagner, Executive Director, WCET.
Putting Data to Work
This session explores changing data sensibilities at US post-secondary institutions with particular attention paid to how predictive analytics are changing expectations for institutional accountability and student success. Results from the Predictive Analytics Reporting Framework show that predictive modeling can identify students at risk and that linking behavioral predictions of risk with interventions to mitigate those risks at the point of need is a powerful strategy for increasing rates of student retention, academic progress and completion.
presentation at the 15th annual SLN SOLsummit February 27, 2014
http://slnsolsummit2014.edublogs.org/
The Role of Non-Cognitive Indicators in Predictive and Proactive Analytics: T...SmarterServices Owen
We have all heard of IQ—but what about the importance of SQ and EQ? Join SmarterServices and Nuro Retention to learn more about how your students’ social and emotional non-cognitive data directly impacts student success and educational outcomes. Nuro Retention will share how to make BIG data actionable by combining the power of SmarterMeasure Learning Readiness Indicator's non-cognitive data along with its retention software platform and predictive analytics models.
In addition, Dr. Mac Adkins, CEO and founder of SmarterServices, will share a case study on how Ashford University has been able to improve retention rates using the power of non-cognitive data. Nuro Chief Data Scientist Natalie Young will also share some key findings from a recent predictive analytics model that dramatically improved retention efforts for one of Nuro’s clients.
Don’t miss out on your chance to learn the latest strategies on the power of predictive, proactive, and prescriptive data!
Online Educa Berlin conference: Big Data in Education - theory and practiceMike Moore
Online Educa Berlin Conference Presentation
Big Data in Education - Theory and Practice
Presented December 6, 2013 by
Mike Moore, Sr. Advisory Consultant - Analytics
Desire2Learn, Inc.
Getting a Return on Investment from Campus Recruiting - Metrics that MatterUniversum Webinars
When it comes to investing in the future of your organization, establishing clear metrics is crucial. Yet far too many companies still don't have a strong understanding of ROI when it comes to campus recruiting.
Register now for the webinar, Getting a Return on Investment from Campus Recruiting - Metrics that Matter, to learn how you can increase recruiter effectiveness, quality of hire, and reduce costs before you even step on campus this Fall. Universum America's Vice President of Advisory Services, John Flato, will dive into:
- The fundamental pillars of a campus recruiting program
- Why you need to use metrics - and how to measure success
- What you need to understand your return on investment
Summer Shorts: Using Predictive Analytics For Data-Driven Decisionsibi
Predictive analytics has gained a lot of attention in recent years, enabling organizations to make better, faster, and more accurate business decisions. These decisions are applied across virtually all industries to generate revenue, reduce costs and risks, and improve processes.
See the pre-recorded webcast online at: http://www.informationbuilders.com/webevents/online/24374#sthash.FoJkEyuL.dpuf
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
6. MODELS ARE MATHEMATICAL EQUATIONS
Y= + 1X1 + 2X2 + 3X3 + ... nXn
Yis the variable to be predicted
X’s represent variables on your students or prospective students
’sare coefficients that are statistically estimated
7. How does a model actually work?
=SLOPE of the line
11. Some examples of models across industries
Direct Marketing:
What is each customer’s probability of purchasing?
How much will each prospect purchase?
Healthcare:
What is each patient’s probability of readmitting within 30 days of discharge?
What is each patient’s risk of Sepsis?
How long will a patient be in the hospital?
Financial:
Which customers will default on their loan?
Insurance:
What is each person’s risk of an accident?
What is each customer’s probability of churning?
13. Prospect/Inquiry Modeling
What is each prospect’s (or inquiry’s) probability of applying?
How can these models be used?
• Buying optimal search names
• Strategically coordinate recruiting efforts by estimating expected
yield
• Prioritize staff and resources
• Mail vs. call vs. email
14. Applicant Enrollment Modeling
• What is each applicant’s probability of enrolling?
How can these models be used?
• Prioritize resources based on each applicant’s enrollment probability
• Help shape your incoming class
• Forecast enrollment and financial outlay based on admit pool
15. Student Retention Modeling
• Which students are at risk of attrition?
How can these models be used?
• Put programs in place to focus resources on at-risk students
• Increase retention and likelihood of student success
16. Alumni Donor Modeling
• Who is going to donate to the annual fund?
How can these models be used?
• Prioritize Advancement efforts based on predicted giving scores.