Business intelligence data repository and data management system and method

• US 7,512,627 B2
• Filed: 12/30/2005
• Issued: 03/31/2009
• Est. Priority Date: 12/30/2005
• Status: Expired due to Fees

• Current Assignee: eCollege.com, Inc (Pearson plc)
• Original Assignee: eCollege.com, Inc (Pearson plc)
• Inventors: Schnittman, Matthew, Rodgers, Allen, Hossfeld, Cassandra, Holliday, Marc
• Primary Examiner(s): Nguyen; Cam-Linh
• Application Number:
  US11/323,435
• Publication Number:
  US 20070156718A1
• Time in Patent Office: 1,187 Days
• Field of Search: 707/1, 707/6, 707/7, 707/10, 707100-102, 434/350, 434/365
• US Class Current: 1/1
• CPC Class Codes: G06Q 50/20 Education
• CPC Class Codes: G06Q 90/00 Systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not involving significant data processing
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

A business intelligence and data management system is disclosed comprising a database for storing multi-dimensional business data from multiple online educational institutions; a usage tracking engine for recording within a user profile the time and duration of access to disparate system features. A reporting engine provides periodic and custom reports and a benchmarking engine facilitates comparison of internal institution data with aggregate data from multiple institutions, to compare student retention, course completion, student satisfaction, and student performance. The reporting engine provides reports on course retention rates, course evaluations, faculty evaluations, enrollment, student performance, and course run rates. The usage tracking engine, benchmarking engine, and reporting engine facilitate determination of best practices to improve student enrollment, student retention, course completion, student performance, and student satisfaction. A custom query engine facilitates freeform searches of business data and a data mining engine provides access to detailed data supporting the periodic reports.

Claims

1. A data management system comprising:

a host server including a processor for processing digital data, a memory coupled to said processor for storing digital data, an input digitizer coupled to the processor for inputting digital data, an application program stored in said memory and accessible by said processor for directing processing of digital data by said processor, a display coupled to the processor and memory for displaying information derived from digital data processed by said processor;

a database for storage of multi-dimensional data originating from multiple educational institutions;

a usage tracking engine configured to generate multi-dimensional tracking data including at least two of identification of a feature accessed by a user, identification of content accessed by a user, an identification of a user accessing said feature, a time of access to said feature, a duration of access to said feature, and user activity relative to said feature, wherein said user is a student and an instructor;

a reporting engine configured to provide periodic reports based on said multi-dimensional data stored in said database;

a benchmarking engine configured to aggregate said multi-dimensional data from said multiple educational institutions to facilitate comparison of internal data associated with a first of said multiple educational institutions with aggregate data from a subset of said multiple educational institutions;

a predictive model configured to understand program performance, student retention, and learning outcomes; and

,

a multi-dimensional analysis engine configured to understand program performance, student retention, learning outcomes.

2. The system of claim 1, wherein said usage tracking engine is configured to facilitate comparison of at least one of student usage profiles, instructor usage profiles, faculty usage profiles, administration usage profiles, and course tool usage profiles.

3. The system of claim 2, wherein said usage tracking engine is configured to facilitate comparison of usage profiles grouped according to at least one of user role type, feature type, term, course, and hierarchal node.

4. The system of claim 3, further comprising a custom query engine configured to facilitate freeform searches of said multi-dimensional data in said database.

5. The system of claim 4, wherein said reporting engine is configured to facilitate reporting of at least one of course retention rates, course evaluations, faculty evaluations, enrollment, student performance, faculty response times, help desk response times, and course run rates.

6. The system of claim 5, wherein said benchmarking engine is configured to facilitate comparison of said internal data with said aggregate data related to at least one of student retention, student enrollment, course completion, student satisfaction, student to faculty ratios, learning outcomes, and student performance.

7. The system of claim 6, wherein said aggregate data is grouped according to at least one of the size of said multiple educational institutions and whether said plurality of said multiple educational institutions are at least one of a for-profit, non-profit and private institutions.

8. The system of claim 7, wherein at least one of said usage tracking engine, said benchmarking engine, and said reporting engine is configured to facilitate determination of best practices relating to at least one of student enrollment, student retention, recourse completion, student performance, learning outcomes, and student satisfaction.

9. The system of claim 8, wherein said reporting engine is configured to provide notification of potential user attrition based upon a comparison of a, user profile in said database with historic user profile data.

10. The system of claim 9, further comprising a data mining engine configured to provide access to detailed data supporting said periodic reports.

11. The system of claim 10, further comprising an interface view listing a reported metric value and a corresponding target metric value and at least one of a status indicator and a trend indicator dependent on said reported metric value and said target metric value.

12. The system of claim 11, wherein said usage tracking engine is configured to record data related to a user'"'"'s access to a help desk feature, including the nature of a query submitted to said help desk feature.

13. The system of claim 12, wherein said reporting engine is configured to report a method of student registration and demographic information for said student.

14. A method for managing business data from multiple educational institutions at a central repository comprising:

tracking student activity associated with a feature of an application accessed by said student, wherein said tracking is performed by a host server including a processor for processing digital data, a memory coupled to said processor for storing digital data, an input digitizer coupled to the processor for inputting digital data, an application program stored in said memory and accessible by said processor for directing processing of digital data by said processor, a display coupled to the processor and memory for displaying information derived from digital data processed by said processor and said central respository;

tracking, using said host server, student activity associated with content accessed by said student;

generating, using said host server, a profile within a central repository from said student tracking;

recording, using said host server, at least one of a time and a duration of said student activity within said student profile;

tracking, using said host server, instructor activity associated with a feature of an application accessed by said instructor;

tracking, using said host server, instructor activity associated with content accessed by said instructor;

generating, using said host server, an instructor profile within a central repository from said instructor tracking;

recording, using said host server, at least one of a time and a duration of said instructor activity within said instructor profile;

comparing, using said host server, internal data associated with a first of said multiple educational institutions to aggregate historic data from a subset of said multiple educational institutions;

comparing, using said host server, said internal data associated with a first program level to said internal data associated with a second program level;

correlating academic activities within said student profile and said instructor profile;

using predictive models to facilitate predictions related to academic program performance, student retention, and learning outcomes;

performing an analysis to understand academic program performance, faculty effectiveness, student retention, and learning outcomes using at least one of online analytical processing (OLAP), multi-dimensional online analytical processing (MOLAP), relational online analytical processing (ROLAP) and hybrid online analytical processing (HOLAP);

identifying key drivers, trends and problems related to student course completion and successful course learning outcomes by analyzing said student profile and said instructor profile;

determining strategies for academic program growth based upon said multi-dimensional analysis, said key drivers, said trends and said problems;

identifying trends related to attrition in an academic program;

identifying times of activities that precede said attrition in said academic program;

providing, using said host server, periodic reports based on said identifications, strategies and said business data stored in said database, wherein said business data includes at least one of student enrollment, registration, student retention, student-instructor interaction, student or instructor system feature usage, student performance, student satisfaction, course evaluations; and

communicating with said student at said time to minimize said attrition.

15. The method of claim 14, wherein said tracking of user activity is performed upon said user accessing at least one of a lecture, exam, document sharing feature, journal feature, student portfolio, chat dialogue, and threaded discussion feature.

16. A machine-readable medium having stored thereon a plurality of instructions, said plurality of instructions when executed by a processor, cause said processor to perform a method comprising the steps of:

tracking student activity associated with a feature of an application accessed by said student, wherein said tracking is performed by a host server including a processor for processing digital data, a memory coupled to said processor for storing digital data, an input digitizer coupled to the processor for inputting digital data, an application program stored in said memory and accessible by said processor for directing processing of digital data by said processor, a display coupled to the processor and memory for displaying information derived from digital data processed by said processor and said central respository;

tracking, using said host server, student activity associated with content accessed by said student;

generating, using said host server, a student profile within a central repository from said student tracking;

recording, using said host server, at least one of a time and a duration of said student activity within said student profile;

tracking, using said host server, instructor activity associated with a feature of an application accessed by said instructor;

tracking, using said host server, instructor activity associated with content accessed by said instructor;

generating, using said host server, an instructor profile within a central repository from said instructor tracking;

recording, using said host server, at least one of a time and a duration of said instructor activity within said instructor profile;

comparing, using said host server, internal data associated with a first of said multiple educational institutions to aggregate historic data from a subset of said multiple educational institutions;

comparing, using said host server, said internal data associated with a first program level to said internal data associated with a second program level;

correlating academic activities within said student profile and said instructor profile;

using predictive models to facilitate predictions related to academic program performance, student retention, and learning outcomes;

performing an analysis to understand academic program performance, faculty effectiveness, student retention, and learning outcomes using at least one of online analytical processing (OLAP), multi-dimensional online analytical processing (MOLAP), relational online analytical processing (ROLAP) and hybrid online analytical processing (HOLAP);

identifying key drivers, trends and problems related to student course completion and successful course learning outcomes by analyzing said student profile and said instructor profile;

determining strategies for academic program growth based upon said multi-dimensional analysis, said key drivers, said trends and said problems;

identifying trends related to attrition in an academic program;

identifying times of activities that precede said attrition in said academic program;

providing, using said host server, periodic reports based on said identifications, strategies and said business data stored in said database, wherein said business data includes at least one of student enrollment, registration, student retention, student-instructor interaction, student or instructor system feature usage, student performance, student satisfaction, course evaluations; and

,

communicating with said student at said time to minimize said attrition.