US20100331075A1

US20100331075A1 - Using game elements to motivate learning

Info

Publication number: US20100331075A1
Application number: US12/492,588
Authority: US
Inventors: Jennifer P. Michelstein; Marina Dukhon; Jonas Helin; Mark R. Alexieff; Nina F. Shih
Original assignee: Microsoft Corp
Current assignee: Microsoft Technology Licensing LLC
Priority date: 2009-06-26
Filing date: 2009-06-26
Publication date: 2010-12-30

Abstract

Elements of game play are incorporated into a productivity application to assist in motivating users to learn features of the productivity application. For example, the elements of game play that are incorporated into learning features of the productivity application may include items such as usage statistics, scores, levels, challenges, achievements, competition, and the like. A recommendation system assists users in determining what features to learn next. For instance, the recommendations may be based on what features the user has already learned and/or based on what features the user's peers are using. Help content that is associated with the productivity application is also tied to the features that are currently being used by the user.

Description

BACKGROUND

Many individuals spend a lot of time trying to become proficient at software games. For example, in order to maximize their points and complete all of the objectives of a game, users attempt to learn in detail how each level and other aspects of the game works. Some individuals even play the game to the point where the game stops being fun and starts being a chore in order to become proficient.

SUMMARY

This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
Elements of game play are incorporated into a productivity application to assist in motivating users to learn features of the productivity application. For example, the elements of game play that are incorporated into learning features of the productivity application may include items such as usage statistics, scores, levels, challenges, achievements, competition, and the like. A recommendation system is utilized to assist users in determining what features of the application to learn next. For instance, the recommendations may be based on what features the user has already learned and/or based on what features the user's peers or some other group of users are using. Help content that is associated with the productivity application can also be tied to the features that are currently being learned and used by the user such that the linked help content is readily available.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a computer architecture for a computer;

FIG. 2 shows an example learning system using game elements for motivating learning within a productivity application;

FIGS. 3 and 4 show exemplary user interfaces for viewing performance information and presenting challenges;

FIG. 5 illustrates an exemplary training challenge that is utilized in learning a feature;

FIG. 6 illustrates a process for employing gaming elements within a application to motivate learning; and

FIG. 7 shows a process for learning a new feature using game play elements.

DETAILED DESCRIPTION

Referring now to the drawings, in which like numerals represent like elements, various embodiments will be described. In particular, FIG. 1 and the corresponding discussion are intended to provide a brief, general description of a suitable computing environment in which embodiments may be implemented.
Generally, program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types. Other computer system configurations may also be used, including multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. Distributed computing environments may also be used where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.
Referring now to FIG. 1, an illustrative computer architecture for a computer 100 utilized in the various embodiments will be described. The computer architecture shown in FIG. 1 may be configured as a desktop, a server, or mobile computer and includes a central processing unit 5 (“CPU”), a system memory 7, including a random access memory 9 (“RAM”) and a read-only memory (“ROM”) 10, and a system bus 12 that couples the memory to the CPU 5. A basic input/output system containing the basic routines that help to transfer information between elements within the computer, such as during startup, is stored in the ROM 10. The computer 100 further includes a mass storage device 14 for storing an operating system 16, application programs, and other program modules, which will be described in greater detail below.
The mass storage device 14 is connected to the CPU 5 through a mass storage controller (not shown) connected to the bus 12. The mass storage device 14 and its associated computer-readable media provide non-volatile storage for the computer 100. Although the description of computer-readable media contained herein refers to a mass storage device, such as a hard disk or CD-ROM drive, the computer-readable media can be any available media that can be accessed by the computer 100.
By way of example, and not limitation, computer-readable media may comprise computer storage mediums and communication media. Computer storage mediums includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, digital versatile disks (“DVD”), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer 100.
According to various embodiments, computer 100 operates in a networked environment using logical connections to remote computers through a network 18, such as the Internet. The computer 100 may connect to the network 18 through a network interface unit 20 connected to the bus 12. The network connection may be wireless and/or wired. The network interface unit 20 may also be utilized to connect to other types of networks and remote computer systems. The computer 100 may also include an input/output controller 22 for receiving and processing input from a number of other devices, including a keyboard, mouse, or electronic stylus (not shown in FIG. 1). Similarly, an input/output controller 22 may provide output to a display screen that includes a user interface 28, a printer, or other type of output device. User interface (UI) 28 is designed to provide a user with a visual way to interact with application 24 that incorporates game play elements for learning features of the application, as well as to interact with other functionality that is included on computing device 100.
As mentioned briefly above, a number of program modules and data files may be stored in the mass storage device 14 and RAM 9 of the computer 100, including an operating system 16 suitable for controlling the operation of a networked computer, such as the WINDOWS 7® operating system from MICROSOFT CORPORATION of Redmond, Wash. The mass storage device 14 and RAM 9 may also store one or more program modules. In particular, the mass storage device 14 and the RAM 9 may store one or more application programs. One of the application programs is a productivity application 24, such as one of the MICROSOFT OFFICE® programs.
Generally, productivity application 24 is an application that a user utilizes in order to complete a task, such as authoring a document in a word-processing program, programming a feature, authoring a spreadsheet, and the like. Productivity application 24 is an application such as a word-processing program, a presentation program, a spreadsheet program, a database program, a programming environment, and the like. Feature manager 26 is configured to incorporate elements of game play into productivity application 24 to assist in motivating users to learn how to use features of the application. For example, the elements of game play that may be incorporated into the application may include items such as usage statistics, scores, levels, challenges, achievements, competition, and the like. Feature manager 26 is configured to track the usage of the features within the application by a user and provide the user with feedback relating to the usage of the features as well as to provide recommendations on what features to learn next. The recommendations may be based on what features the user has already learned and/or based on what features the user's peers are using. Feature manager 26 is also configured to link help content that is associated with the productivity application with the features that are currently being used by the user such that the help content for the feature that is currently being used is available to the user with a single selection.
FIG. 2 shows an example learning system using game elements for motivating learning within a productivity application. As illustrated, system 200 includes display 28, input 205, and productivity application 24. Feature manager 26 may be implemented within presentation application 10 as shown in FIG. 2 or may be implemented externally from application 24 as shown in FIG. 1.
In order to facilitate communication with the feature manager 26, one or more callback routines, illustrated in FIG. 2 as callback code 210, may be implemented. Through the use of the callback code 210, the feature manager 26 may query for additional information used in incorporating the elements of game play within productivity application 24. For example, feature manager 26 may request to be informed when a user transitions to another feature within the application. Other information may also be provided that relate to the features of the application. As discussed above, feature manager 26 is configured to incorporate game play elements into productivity application 24.
Feature manager 26 utilizes a tracking system 225 that provides statistics and usage reporting based on what features the user is utilizing in the application. The features that are tracked may be any features included within the application. For example, the features may be a base set of features that broadly covers the functionality of the application or some other set of features within the application. Feature manager 26 may store this tracked information for the user as well as a group of other users. The features used by the user and tracked by tracking system 225 are also used to determine a score for the user as determined by score tracker 220 as well as provide recommendations to the user as to what features should be learned next.
Score tracker 220 is configured to map the usage information for a feature(s) into a quantifiable value that may be converted into points, badges, levels, scores, and the like. Feature manager 26 is also configured to provide different challenges to a user that allows them to accumulate additional points, badges, and the like while learning a new feature of application 24.
According to an embodiment, a leaderboard is provided that allows the user to see how they are performing both individually as well as how they are performing relative to other users. For example, the group may be a work group, a set of designated friends, friends from one or more social networking sites, users who have a same work title, users in the same profession, and the like. In this way, a user may compare their scores and learning experience to other similarly situated users. Score tracker 220 is configured to determine when a user reaches a predetermined score such that the user is provided with a reward. For example, the reward may be unlocking videos, pictures, games, customization of the application, and the like. Score tracker 220 may also be configured to provide performance information relating to head-to-head competition between two or more users. For example a user could challenge one or more other users to play the same challenge. Feature manager 26 may also be configured to initiate head-to-head competitions. According to one embodiment, points are provided to the user in the head-to-head competition who completes the challenge fastest and/or most efficiently uses the features of the application to complete the challenge.
Recommended features 215 is configured to provide recommendations to the user for what to learn next, based on what the user has done or not done within the application, and based on the social element of what features within the productivity application other users are using and/or have already learned. The recommended features that are suggested may be based on features that enhance the features the user is currently using. For example, if the user uses three features out of a group of five related features, the other two features that are not used by the user may be suggested.
Feature manager 26 is coupled to help system 225 such that help content that is supplied by the productivity application is provided to the user based on what features/actions the user is or is not doing relating to the application. According to an embodiment, the help system 225 is the help content that is natively provided by productivity application 24. For example, when a user is learning feature one as determined by tracking system 225, the help content relating to feature one may be automatically linked to the feature such that the user may directly select help for the feature without having to search for the desired help content. Feature manager 26 tracks what the user is doing and then proactively surfaces the best help topics for that user.
Feature manager 26 is also configured to be expandable through one or more expansions 217. Expansions 217 may add functionality to feature manager 26 before or after the deployment of productivity application 24. For example, one or more “challenge” expansions that add new game/learning elements may be created by the developer of productivity application 24 and/or third party developers and then integrated and utilized by feature manager 26 to present the challenge. The expansions may be integrated with feature manager 26 using many different methods. For example, the expansion may be a patch to the productivity application, a plug-in, and the like.
Display 28 is configured to provide the user with a visual display of their score, as well as provide recommendations to the user and present the user with challenges (See FIGS. 3-5 for exemplary user interfaces).
Feature manager 26 is also coupled to other applications 230 such that information relating to the scores and recommendations may be provided to the other application as well as receiving information from the other applications. For example, feature manager 26 may be coupled to a social networking site such that when a user accesses the social networking site they are able to see how they are performing using application 24 as well as see how their linked associates are performing. Feature manager 26 may also post this performance and recommendation information to other locations, such as including the information within a news feed of a social networking site and/or some other location that is available by users. Another example application that may be linked is a messaging application such that the performance information/recommendations can be delivered to one or more users. Feature manager 26 may also be coupled to a backend data store 240 that is used to store the performance and recommendation information. This information may be used to compare users with each other.
FIG. 3 shows an exemplary user interface for viewing performance information and presenting challenges. As illustrated, user interface 300 includes user score 310, graph area 320, recommendations 325-327, display 330, a before preview picture 335, an after preview picture 340, and challenge, video, and help buttons.
As illustrated, user score 310 shows a score of 1350 that is based on the features that a user has utilized within the application. In order to increase the score, a user can accumulate points by using more features or groups of features that are associated with the productivity application. In the present example, the productivity application is a word-processing application. Other applications may also be utilized. For example, the performance information may relate to another application within a suite of programs and/or the entire suite of applications. According to one embodiment, the more difficult the feature or set of features utilized, the higher the value that is associated with the score.
Graph area 320 displays how the points making up user score 310 are distributed. In this way, a user can see what parts of the application they are using and how proficient they are at using the features. Graph area 320 also provides score comparisons based on other groups of users. As discussed above, these other user groups may be determined from the user's profession, people who work for the same company, all users, people at the user's similar level in the productivity application, people who utilize the application similarly to the user, people in the user's zip code, age group, gender, social networking groups, and the like. A group from which to compare may be selected by utilizing button 321.
Exemplary user interface 300 provides recommendations (e.g. 325-327) based on the top features the selected group is using that the user is not currently using and/or based on the features used by the user. In the present example, three recommendations (325-327) have been provided to the user. In order to learn these unused features, a training challenge may be associated with each suggestion. In the present example, text wrapping 326 around a picture is recommended for a user. When the user selects one of the recommendations (i.e. clicking on text wrapping 326), the user is presented with a display 330 that shows where the feature exists in the productivity application, a before picture 335 and after picture 340 that illustrates the benefits of using the feature, and different types of training such as a short video demo, a challenge which is like a small game or puzzle where the user is challenged to use the feature inside the application, and a tie-in to existing help content.
FIG. 4 shows another exemplary user interface for viewing performance information and presenting challenges. As illustrated, user interface 400 is similar to user interface 300. Achievement breakdown area 410 provides a user with a view of feature areas included within an application and how they are performing within these features areas. As illustrated, most of the score bars are empty (The Basics, Great Looking Docs, Professional Docs) indicating that the user has just begun using game play elements and has many different features to learn.
Selection area 420 allows a user to select a group from which the recommendations provided to the user are based as well as the groups' average achievement score. For example, when one group is selected a first set of recommendations is provided whereas when another group is selected, a second set of recommendations is provided. In this example, the recommendations are based on how the selected group uses the application and the user is not using the application. For instance, when the user compares himself to Group A and Group A uses feature X (that the user does not), a recommendation provided to the user is to learn feature X. If the user then compares himself to Group B and Group B does not use feature X, but instead uses feature Y, feature Y is recommended to the user.
FIG. 5 illustrates an exemplary training challenge that is utilized in learning a feature. In response to a user selecting a challenge, a challenge is presented to a user that allows them to learn a feature. In the present example, the challenge is reformatting table 510 to make it appear as table 520. Many different challenges may be created for a feature. Area 530 provides the user with information on their progress within the challenge as well as allowing them to receive a hint using button 531 when they become stuck in the challenge. The user may practice in the challenge area until they are comfortable with the feature. Points are also be awarded for completing a challenge. The points awarded may be determined using many different criteria. For example, the difficulty of the challenge, the time it took to complete the challenge, the navigation of the application functionality, and the like.
Referring now to FIGS. 6-7, illustrative processes for employing game elements to motivate learning within a productivity application is described.
When reading the discussion of the routines presented herein, it should be appreciated that the logical operations of various embodiments are implemented (1) as a sequence of computer implemented acts or program modules running on a computing system and/or (2) as interconnected machine logic circuits or circuit modules within the computing system. The implementation is a matter of choice dependent on the performance requirements of the computing system implementing the invention. Accordingly, the logical operations illustrated and making up the embodiments described herein are referred to variously as operations, structural devices, acts or modules. These operations, structural devices, acts and modules may be implemented in software, in firmware, in special purpose digital logic, and any combination thereof.
FIG. 6 illustrates a process for employing gaming elements within a application to motivate learning.
After a start operation, the process flows to operation 610, where the features within an application that are utilized by a user are tracked. The features tracked relate to features within one or more areas of the application. For example, the features may be divided according to functions performed within the application (i.e. formatting text, pictures, and the like).
Moving to operation 620, a score is calculated for each feature that is utilized. When a user utilizes a new feature, a score is attached to that action. The score may be dependent upon many factors, such as feature utilized, time the feature is utilized, difficulty level, and the like.
Flowing to operation 630, recommendations are determined for a user. The recommendations on what feature to learn next may be based on different items, including what the user has done or not done within the application and what features other users are using.
Transitioning to operation 640, the recommendations are provided to the user. The recommendations may be provided many different ways. For example, the recommendations may be provided within a user interface, the recommendations may be provided within a social networking site, and/or an electronic message may be sent to the user that includes the recommendations.
Moving to operation 650, the score is displayed to the user. As previously discussed, the score displayed may include an individual score, as well as scoring information as it relates to one or more groups of users.
The process then flows to an end operation and returns to processing other actions.
FIG. 7 shows a process for learning a new feature using game play elements.
After a start operation, the process flows to operation 710, where a recommended feature is selected. According to one embodiment, the feature is selected from a group of recommended features that are selected for the user.
Moving to operation 720, the help content that relates to the selected feature within the application is linked to a training challenge for the feature. The linked help content is readily available for the user without the user having to search for a specific help topic.
Flowing to operation 730, a training challenge for the feature is provided to the user. The training challenge allows the user to use the application in order to practice the feature while at the same time presenting the challenge using game playing elements.
Moving to operation 740, a score is calculated based on the interaction with the features during the challenge.
Flowing to operation 750, the user score is updated and displayed to the user. The user may also be provided with a new recommendation on what feature to learn next.
The process then flows to an end operation and returns to processing other actions.
The above specification, examples and data provide a complete description of the manufacture and use of the composition of the invention. Since many embodiments of the invention can be made without departing from the spirit and scope of the invention, the invention resides in the claims hereinafter appended.

Claims

1. A method for utilizing game elements within a productivity application, the method executing on a processor of a computer, comprising:

tracking features being utilized by a user within the application; wherein the application is a productivity application;

calculating a score as the user utilizes the tracked features;

determining recommendations to provide to the user based on the tracked features utilized by the user; wherein the recommendations include new features of the application to learn;

providing the recommendations to the user;

displaying the score;

receiving a selection of one of the provided recommendations; and

providing a training challenge to allow a user to practice the recommended feature that is selected.

2. The method of claim 1, wherein displaying the score comprises displaying a component score for each of the components that determine the score.

3. The method of claim 2, further comprising comparing the score to a group of users using the application; wherein the group of users is selected by a user of the application.

4. The method of claim 2, further comprising in response to receiving a selection of one of the provided recommendations displaying a picture of where functionality relating to the feature is provided within a menu of the application.

5. The method of claim 2, wherein providing the recommendations to the user is based on features being used by a group of users using the application.

6. The method of claim 2, wherein providing the recommendations comprises incorporating the recommendations into a social networking site display that is associated with the user.

7. The method of claim 2, further comprising linking help content that is natively supplied by the application to the training challenge, such that the linked help content is viewable without searching for the linked help content.

8. The method of claim 2, wherein the group of users is determined based on other users having a similar usage pattern to the user.

9. A computer-readable storage medium having computer-executable instructions for utilizing game elements within a productivity application, the instructions executing on a processor of a computer, comprising:

calculating a score as the user utilizes the tracked features;

directly linking help content that is natively supplied by the application to each of the recommendations, such that the linked help content for one of the recommended features is provided to a user without performing a search for the linked help content;

providing the recommendations within a display;

displaying the score;

receiving a selection of one of the provided recommendations;

10. The computer-readable storage medium of claim 9, further comprising providing a training challenge to allow a user to practice the recommended feature that is selected; wherein the training challenge is provided within a third-party supplied expansion that is integrated into the application after deployment of the application.

11. The computer-readable storage medium of claim 10, wherein displaying the score comprises displaying a component score for each of the components that determine the score and displaying an indicator that shows how the score for each component compares to a group score for the component.

12. The computer-readable storage medium of claim 10, wherein the group of users for which the score is compared is selected by a user of the application.

13. The computer-readable storage medium of claim 10, further comprising in response to receiving a selection of one of the provided recommendations displaying a graphic of where functionality relating to the feature is provided within a menu of the application and a graphic of the feature before the feature is applied and a graphic of the feature after the feature is applied.

14. The computer-readable storage medium of claim 10, wherein providing the recommendations to the user is based on features being used by a group of users using the application that is selected by the user.

15. The computer-readable storage medium of claim 10, wherein providing the recommendations comprises sending the recommendations to the user through an electronic message.

16. The computer-readable storage medium of claim 10, wherein the group of users is determined based on other users having a usage pattern that is approximately equal to the user.

17. A system for utilizing game elements within a productivity application, comprising:

a processor and a computer-readable medium;

an operating environment stored on the computer-readable medium and executing on the processor;

a network connection;

a productivity application and a feature manager operating on the processor; and configured to perform tasks, comprising:

calculating a score as the user utilizes the tracked features;

providing the recommendations within a display;

displaying the score for the user along with a display showing a component score for each of the components that determine the score and displaying an indicator that shows how the score for each component compares to a group score for the component;

receiving a selection of one of the provided recommendations; and

18. The system of claim 17, wherein providing the training challenge is provided to at least one other user to create a head-to-head competition between at least two users.

19. The system of claim 17, wherein providing the recommendations to the user is based on features being used by a group of users using the application that is selected by the user.

20. The system of claim 17, wherein providing the recommendations comprises sending the recommendations to the user through an electronic message and to a social networking site.