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AutoScholar Advisor System
Version 1.0.0
BUSINESS CASE &
COLLABORATION
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Contents
1. Introduction ... 3
2. Improvement science... 4
3. Case studies at Georgia State University ... 5
4. Financial model ... 7
5. Collaboration models ... 8
5.1. Hub institutions ... 9
5.2. Component development ... 10
5.3. Training programme ... 12
5.4. System development ... 13
6. Summary ... 14
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1. Introduction
The AutoScholar Advisor system auto-generates advice to students, lecturers, programme and faculty coordinators, and executive management. The system uses Artificial Intelligence for prediction, Analytics and Data Science to measure institutional performance and Gamification to influence positive behaviour. The AutoScholar securely connects to an institution’s database through an Application Programming Interface (API) and serves data to a client device where the bulk of the processing and advising is carried out.
This document summarises the benefits to be achieved from implementation and adoption of the AutoScholar Advisor system. The outcomes are benchmarked in terms of a financial model and the costs of a full-scale implementation are outlined together. The potential for monetisation and realising third stream income to the institution are also described.
The potential benefits that can be realised as a result of wide-scale use of the AutoScholar is illustrated. The costs are made explicit together with the potential for monetisation and achievement of third-stream income.
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2. Improvement science
The field of Institutional Research focuses on the development of analytics which reveal the progress by an institution toward its strategic goals. For example, an institution may set the goal of increasing its graduation rate of quintile 1 and 2 learners by 20% within 2 years.
This goal may be stated from a high level and even monitored, but until practical interventions are implemented at all levels to support this goal, the chance of success remains limited. Such a goal must therefore be devolved from the executive to the faculties and hence to the academic programmes.
Interventions required may include recruitment of learners of this category and additional teaching supports and resources made available. The capacity for the AutoScholar to advise students is one such support; another may be the implementation of a Supplemental Instruction programme.
When the outcomes of an intervention are measured in the context of an over-arching goal, it becomes possible to focus resources toward those supports which show the strongest
improvements. Incremental improvements accumulate, until the original challenges are met. This is summarised in the figure below, which outlines the principles of improvement science.
The AutoScholar Advisor supports this cycle in the following ways:
a. Metrics for the over-arching goal are defined and estimated b. Data for the operation of the interventions are gathered c. Intervention operation is eased by AutoScholar gamification d. Actions and their results are recorded
e. Outcomes and advice are auto-generated.
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3. Case studies at Georgia State University
Application of an improvement science model have resulted in significant improvements at Georgia State University (GSU). The results below show significant reductions in the time to graduate, the rate of retention to later years of study and the degrees conferred upon an increasingly diverse student body.
The parallels between UKZN and GSU are clear when considering the change in diversity over a relatively short period of time. The approach by GSU has been to increase the number of advising sessions to students; the improvements observed are a results of some 500,000 conversations between staff and students.
The funding requirement to implement such a programme is significant, and likely beyond the resources of South African universities. However, the observation that students are sensitive to the advice generated by the system can be leveraged in this context. Through the creation of advising scripts, auto-generated advice can be dispensed to students through the media of webapps, emails and chatbots. The AutoScholar can support such media.
Business Case Document 6 | P a g e In the context of achieving significant improvements through interventions in the framework of improvement science, the AutoScholar Advisor system is the mechanism which can manage the complex, data intensive process and integrate the efforts of the many participants required.
It should be noted that one of the new components of the AutoScholar is explicitly developed for the purpose of implementing an Improvement Science approach.
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4. Financial model
A simple way to estimate the value of the AutoScholar is through an estimate of the subsidy lost through students exiting the system without graduation.
It has been estimated that only 37% of all entering students eventually graduate. By taking a mean entering rate of 12,000 students per annum, this represents a cohort loss of 7,560 students.
Given that the subsidy income to the institution is R2.4b, this represents a loss of R504m per cohort.
If application of the AutoScholar reduces the loss rate by 5%; from 63% to 58%; this represents a subsidy loss reduction of almost R40m. Assuming an annual AutoScholar license cost of R1.45m, the system will have repaid its cost by more than 27 times.
We note that these values are based on very conservative estimates. Previous studies on the AutoScholar inception have shown improvements on the order of 15-23%.
CURRENT 5% IMPROVEMENT
Graduates in Minimum Time 19%
Minimum Time+1 year 11%
Minimum Time+2 years 5%
Minimum Time+x years 2%
Total graduating 37% 42%
Total exited without graduating 63% 58%
No. of students entered 12000 12000
No. of students exiting without graduating 7560 6960
Total subsidy R 2 443 000 000.00
Student FTEs 36660
Subsidy per FTE student R 66 639.39
Subsidy loss R 503 793 780.69 R 463 810 147.30
Loss Reduction R 39 983 633.39
AutoScholar licence R 1 450 000.00
Justification factor 27.57
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5. Collaboration models
There are significant opportunities for realising 3rd stream income through collaboration with the AutoScholar author, viz the Nanostream Blü company (NBc).
The AutoScholar system has been created with the view that competent technical staff at Higher Ed institutions will be able to create an implementation through the use of an Application Programming Interface (API) spec document. Due to the emphasis on client-side computing, the need for tech support is also minimal. The NBc does not plan to change its approach.
It is however acknowledged that institutions expect “installation” and tech-support carried out by the company as part of the offering. To meet this need, the NBc has defined the concept of Hub Institutions, which achieve income through a reseller model. The institution may therefore, through the creation of a spin-off company, offer the service of API creation, providing tech-support and training.
It should be noted that the NBc already provides documentation (including sample code) which makes the carrying out of these activities relatively simple. Additionally, when negotiating executive- to-executive between institutions, it is possible to achieve a high mark-up rate (more than 100%).
The potential for third stream income development is therefore significant. The usual operations of a traditional tech company are therefore undertaken through the involvement of third party
companies which then claim a share of the income based on their level of activity. This section outlines four models for the operation of the AutoScholar Advisor system.
These collaboration models include:
a. Hub institution reselling
b. New component co-development c. Existing system componentisation
d. Training and implementation of training programmes e. Co-development of new systems
The real value of the AutoScholar Advisor system lies in the education experience by both staff and students. The business value of the AutoScholar Advisor system to an institution can, at least in the first instance, be estimated in terms of the subsidy increase associated with the increased
graduation rate. However, by adoption of the Collaboration models, it is possible for an institution to attract significant third stream income. The present document provides an introduction to these models.
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5.1. Hub institutions
A significant part of the implementation at institutions involves the initial discussion and inception, the development of the API, the management of the relationship toward an annual license
commitment, the training and the subsequent query handling. It has been estimated the value of these activities is as much as 50% of the AutoScholar license fee.
Due to the strategic decision by the NBc to remain as a small company with a light footprint, these activities are best undertaken by academic institutions with larger staffing complements. Academic institutions can therefore integrate these activities into the job descriptions of existing institution staff and thereby derive significant third stream income with relatively little change to resource commitment.
The following table summarises a typical example of a hub institution servicing a license with a base price of R1.45m.
Hub institutions are a practical means of scaling the user base of the AutoScholar Advisor system and of supporting the financial health of institutions.
0. Annual license fee for access to AutoScholar R 1 450 000.00
1. Introduction, inception and signup 7% R 101 500.00
2. Client institution API development & maintenance 10% R 145 000.00 3. Maintain client relationship, annual license 10% R 145 000.00 4. Implement Modern Scholarship training 15% R 217 500.00 5. Answer queries & "Tech Support" 8% R 116 000.00
Total cost to client institution R 2 175 000.00
Total annual income to hub institution per client institution 50% R 725 000.00
Hub Institution Reseller Model
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5.2. Component development
The AutoScholar Advisor has grown over the previous 15 years to accumulate a large functionality base. An emerging challenge was the steep learning curve required to use all functionality
effectively. It was therefore decided to devolve the AutoScholar to the 9 core components listed here.
The nine core components are mentioned and briefly described below:
a. ClassView Connect – For lecturers to manage course performance, benchmark against peer courses, ensure assessment results release on time, and to record student attendance.
b. Student Central – Advising to students in progress according to assessments in the current semester; reviewing of progress in the previous semester; and benchmarking against peers.
c. Curriculum Analyst – Progression mapping to identify choke points; gateway and killer courses. Cohort analysis; Retention analysis; Biographical analysis.
d. Accreditation AutoMate – Auto-generation of accreditation reporting and student accreditation panel access.
e. Coursework Curator – Curriculum Design, conceptual scaffolding creation, and analysis based on graph theory. Learning management system and course resources host.
f. Research Portal – Data analysis and model building through AutoScholar engine; component creator.
g. CaseWork Counsellor – Recording of student cases in terms of events and advising;
engagement with support structures and to estimate the effectiveness of interventions.
h. Admission and Registration Advisor – Auto-generation of advice for registration based on pre- and co-requisites logic; Admissions criteria; Machine Learning to analyse the impact of changes in admissions on graduation.
i. Career Mapper – Students can maintain a CV and apply for posts.
Business Case Document 11 | P a g e An implementation of the AutoScholar Advisor license includes these 9 components as a minimum entry point. Additionally, an institution may also choose to purchase licenses to other components hosted in the AutoScholar third party extension store.
An institution may choose to develop a new component for internal use and for commercialisation through the component store. The pricing is set by the authoring institution, with a typical
component’s annual license priced at R200k.
The IP sharing between the NanoStream Blü company and the institution is dependent on the level of concept and software development contributed by each party. IP sharing is typically determined through negotiation and discussion; the following suggested allocations are a useful starting point for the discussion.
Institutions have typically developed multiple components for internal use without
commercialisation. The AutoScholar framework is an effective means of developing high quality professional online services with exposure to a growing network of institutions towards a significant source of third stream income to an authoring institution.
Activity IP share
Concept development 50%
Interface implementation 10%
Component API to AutoScholar API mapping 15%
Analysis formulation and analysis software 15%
Data hosting and server infrastructure 10%
Total 100%
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5.3. Training programme
Two options are available for training in the use of the system; a shorter route intended only for learning how to use the component (3 hrs per component) and a longer route, involving
understanding the principles of the subject and concept behind the component.
Modern Scholarship involves the training of teachers in the use of modern teaching methods (of which technology is just one part) and supporting the efforts of these teachers by making education resources accessible to themselves and their students.
Modern teaching methods include the concepts of active learning, the flipped classroom, animated simulations, AI-assisted student progression analysis, and automated advising. Overall, the Modern Scholarship practitioner will be able to deliver extremely high quality lectures and classes, develop high quality teaching materials through a network of teaching collaborators, rapidly analyse the progression of even large classrooms, and rapidly provide useful feedback to students about potential gaps in understanding and the management towards progression of their education.
The Modern Scholarship offerings include the courses shown in the table below. Each course shows the AutoScholar component associated with a particular course and a particular audience member.
It must be noted that in these courses, the AutoScholar component is only presented as a method for achieving the outcomes of the courses and for undertaking the assessments (assignments and tests).
These courses are presently under development; once complete, they will be submitted for accreditation as credit-bearing postgrad offerings. The course materials will be made available as online video and textbook packages. Delivery only requires oversight by the institution.
The host institution may charge student fees in line with the fees it currently charges for course offerings of the same number of credits of a postgrad teaching diploma or degree.
Modern Scholarship Course Offerings
Course title Component Audience Instr hrs Credits
Applied analytics in class management CourseView Connect Lecturers 12 4
Automated content delivery Student Central Lecturers 12 4
Supporting your students Support Centre ADOs, student advisors 12 4
Analysis and design of curricula Curriculum Analyst Faculty management 12 4 Quality assurance and professional accreditationAccreditation AutoMate QA, Faculty management 12 4 Admitting and progressing students Admissions, CourseViewQA, Admissions, Lecturers 12 4
Interfacing with Data R&D Portal Academics, developers 12 4
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5.4. System development
The NanoStream Blü company has developed an extensive set of libraries for interfacing with data, analysing data, interfacing with users and for hosting and managing servers. The analysis libraries include Data Analytics/Statistics, Machine Learning. Integration with web services from sophisticated Global Information Systems to simple API databases is available. Other Nanostream Blü services include the creation of third party stores as well as video and text content creation for training.
It is therefore possible to translate research concepts into professional, automated online services which system clients access through a paid license.
Undertaking such development is negotiated between the NBc and the author / authoring institution. A useful starting point for the discussion of IP / revenue sharing is the table shown on page 3 reproduced here for convenience.
The NBc may also assist with creating applications for funding the development to the appropriate funding bodies.
Activity IP share
Concept development 50%
Interface implementation 10%
Component API to AutoScholar API mapping 15%
Analysis formulation and analysis software 15%
Data hosting and server infrastructure 10%
Total 100%
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6. Summary
This document summarises the benefits to be achieved from implementation and adoption of the AutoScholar Advisor system. The outcomes are benchmarked in terms of a financial model and the costs of a full-scale implementation are outlined together. The potential for monetisation and realising third stream income to the institution are also described.
The real value of the AutoScholar Advisor system lies in the education experience by both staff and students. The business value of the AutoScholar Advisor system to an institution can be estimated in terms of the subsidy increase associated with the increased graduation rate. However, by adoption of the Collaboration models, it is possible for an institution to attract significant third stream income by way of the collaboration models outlined in this document.
Hub institution reselling
New component co-development
Training and implementation of training programmes
Co-development of new systems
It is hoped and expected that an institution will adopt the progressive ideals implicit here and thereby derive the education and financial benefits that are possible.
