INTERACTIVE MOBILE LEARNING (IML) PLATFORM

Abstract

A computer implemented learning and assessment apparatus includes a database having at least one set of educational game parameters, a processor operable to receive input signals from a user of the apparatus, and a human readable display. Game logic of the apparatus is operable to generate a game-based learning experience by presenting to the user a virtual game on the display in accordance with a selected first one of the set of educational game parameters. Measurement logic generates measurement data representative of actions of the user during interaction by the user with the virtual game. Assessment logic is operable to generate assessment data representative of gameplay results wherein a failure of the user to produce predetermined expected learning results is weighted in accordance with predetermined game-based learning parameters relative to experiential exercise of the virtual game by the user. A result signal is selectively rendered on the human readable display.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. provisional application Ser. No. 61/558,818, filed on Nov. 11, 2011, incorporated in its entirety herein.

BACKGROUND

1. Field

Embodiments herein relate to electronic books incorporating multimedia and, more particularly, to computer-assisted learning methods and systems using gaming techniques for comprehension assessment.

2. Description of Related Art

Modern students are less inclined toward the linear, textual learning mode of traditional printed and electronic textbooks. High school students are now accustomed to a new, non-linear style of discovering information made possible by the Internet. Current electronic textbook trends, however, follow the traditional linear modus operandi with added media components. This does not address the method of learning and engaging with content that the Internet has made popular.

Total breaks from traditional linear modes of learning are often unsuccessful, however. Some students do still learn best by reading traditional textbooks and, although videos and animations can be more engaging than text, they are often better used as introductions to material than as references proper.

Educational games have been used as a method of learning but have a history of failure. Most such systems lack the comprehensive content of traditional textbooks. Also, some educational games used as a method of learning are too complicated, not complex enough, or generally not fun for the end user students. Further, cultural attitudes toward games as lacking “seriousness” are an obstacle for adoption within the educational community. Overall therefore, implementation or use of educational learning games has remained substantially underfunded in most school systems and elsewhere and the return on investment for the school systems too low or uncertain for any substantial adoption thereof.

However, there remains no simple, standard way of assessing learning comprehension. Textbooks have a mostly standardized and accepted way to assess comprehension through end-of-chapter reviews and instructor materials. Different learning methods often teach distinct aspects of a subject and thus require separate assessment methods. It is difficult therefore to determine what learning/assessment method is objectively better for providing fair and accurate assessment results.

SUMMARY

The following presents a simplified summary of the example embodiments in order to provide a basic understanding of some aspects of the example embodiments. This summary is not an extensive overview of the example embodiments. It is intended to neither identify key or critical elements of the invention nor delineate the scope of the invention. Its sole purpose is to present some concepts of the example embodiments in a simplified form as a prelude to the more detailed description that is presented later.

In an example embodiment, there is disclosed herein a computer implemented learning and assessment apparatus, comprising a non-transient memory, a database stored in the memory, the database having at least one set of educational game parameters, a processor operable to receive input signals from a user of the apparatus, and a human readable display. Game logic of the apparatus is operable to generate a game-based learning experience by presenting to the user a virtual game on the display in accordance with a selected first one of the set of educational game parameters. Measurement logic of the apparatus is operable to generate measurement data based on the input signals received from a user by the processor, the measurement data being representative of actions of the user during interaction by the user with the virtual game. Assessment logic of the apparatus is operable to generate assessment data based on the measurement data, the assessment data being representative of gameplay results wherein a failure of the user to produce predetermined expected learning results based on the selected first one of the set of educational game parameters is weighted in accordance with predetermined game-based learning parameters relative to experiential exercise of the virtual game by the user. Result logic of the apparatus is operable to generate in accordance with the assessment data, a result signal for selective rendering on the human readable display.

In a further example embodiment, there is disclosed herein a learning and assessment method in an apparatus comprising a non-transient memory, a database stored in the memory, the database having at least one set of educational game parameters, a processor operable to receive input signals from a user of the apparatus, and a human readable display. The method comprises generating, by game logic of the apparatus, a game-based learning experience by presenting to the user a virtual game on the display in accordance with a selected first one of the set of educational game parameters. The method further comprises generating, by measurement logic of the apparatus, measurement data based on the input signals received from a user by the processor, the measurement data being representative of actions of the user during interaction by the user with the virtual game. The method further comprises generating, by assessment logic of the apparatus, assessment data based on the measurement data, the assessment data being representative of gameplay results wherein a failure of the user to produce predetermined expected learning results based on the selected first one of the set of educational game parameters is weighted in accordance with predetermined game-based learning parameters relative to experiential exercise of the virtual game by the user. The method further comprises generating, by result logic of the apparatus in accordance with the assessment data, a result signal for selective rendering on the human readable display.

An Interactive Mobile Learning Platform (IMLP) system in accordance with an example embodiment comprises methods and apparatus providing an integrated source for textual, graphical, auditory, and interactive information. It provides a familiar interface for modern learning techniques based on the production qualities and traditions of print media. In general, the IMLP system consists of electronic pages of multimedia content that present information in a structured, linear manner, but that also provide access to a 3D scriptable rendering engine, which enables greater levels of experiential modes of learning.

The learning system of the example embodiment provides a computer interface with similarities to printed books. This allows for intuitive use for most users during this period of transition between printed and electronic books. Most education currently in practice utilizes textbooks, though practical methods of digitizing them have been available since the 1990s. Most of these methods have been met with critiques concerning formatting, comfort, and quality. High school students today are accustomed to discovering and participating in content via the Internet, and with mobile computing devices. IMLP system in accordance with the example embodiments described herein assists in bridging the gap between current educational practices and modern information sharing and discovery.

An interface of the learning system of the example embodiment consists of a view of one of numerous pages formatted in the likeness of a printed publication. The page has no set limits in terms of width and height, but the preferred implementation fixes the width to the horizontal size of the viewing device screen, while the length varies depending on the amount of content (but generally does not exceed the length of twice the vertical size of the device screen, held in portrait orientation). The preferred implementation of the page employs an HTML5 rendering engine, allowing the embedding and streaming of any web-enabled content. HTML pages can be dynamically generated or provided by a local or remote database; the current invention utilizes pages created by hand to more fully emulate the process and quality of print production.

In the learning system of the example embodiment, contextual multimedia objects supplement user comprehension of the textual and graphical information. Video, audio, text (such as RSS feeds), interactive objects, and other media are presented in proximity to textual information that is difficult to grasp through reading alone. Video, audio, and text objects are selectively stored locally on the device or streamed from the Internet, and are displayed via HTML5 in the current invention. Interactive objects are selectively presented in one of at least two modes including for example as in-line objects and in separate full-screen views. In-line objects are presented and interacted with directly on the page next to other textual and graphical information. The preferred implementation employs HTML5 and JavaScript to facilitate custom in-line interactive objects. These objects can include, but are not limited to, pan/zoom-able images confined behind a fixed frame on the page, which can also be viewed full-screen; image slideshows confined behind a fixed frame on the page, with an indication of the number of images and the current image being viewed, which can also be viewed full-screen; images with overlaid button elements that provide access to called out text or images; one or more boxes with multiple tabs that hide and show text or multimedia, providing a variety of contexts for a single concept; and charts, graphs, and tables with variable ways of displaying data.

More complex interactive objects are selectively displayed as separate full-screen views activated via a button, a link, or an interactive page element. The current invention displays the full-screen view by means of a 3D scriptable rendering engine. Scripted 3D content may take the form of a simple method for viewing a 3D model or more complex instances of simulations and games.

Interactive objects also facilitate user comprehension assessment. In-line testing modules of the example embodiment selectively provide traditional multiple choice, matching, or other simple tests directly on the page. Additionally, assessments are selectively accomplished by means of the 3D scriptable rendering engine. Game scenarios custom-built for specific, experience-based learning objectives can be embedded as necessary. In the preferred example embodiment, the game mechanics are highly related to the learning objective so that experiential knowledge gained successfully transfers beyond the scope of the textbook and classroom. Assessment data is stored and displayed locally, and can also optionally be stored on a cloud-computing server or transferred to various learning management and content management systems.

Interactive objects are preferably displayed in the example embodiment in the context of a “Learn, Interact, Test” learning method. In the embodiment described, a page or other section of content is selectively delineated into three (3) segments related to a specific learning outcome including “Learn,” “Interact,” and “Test.” Learn comprises experiences in textual, graphical, auditory, or other non-interactive informational media covering the subject in detail. Interact comprises in-line or full-screen interactive object, game, or simulation based on criteria previously defined. Test comprises a link initiating a separate full-screen view of an assessment module, based on criteria previously defined.

With some subjects, it may be possible to chain several of these triads together to form a series of “levels” in a comprehensive curriculum.

The pages of the subject learning system of the example embodiment are navigated via a user interface consisting of three distinct parts, or “views,” within which different tiers of navigation are accessible comprising in the example embodiment a main content view, a local navigation view, and a global navigation view. The main content view displays the current page and provides access to adjacent pages via swiping gestures (from left to right or right to left) on a touch-sensitive surface or by use of graphic button elements. In the example embodiment described herein this view fills the bounds of the device screen except where overlapped by any “toolbars” or other graphical navigation elements.

The local navigation view provides access to a range of pages in the vicinity of the current page via thumbnails, whereby activating a thumbnail results in the specified page appearing in the main content view. The thumbnails are arranged horizontally in a sequential manner, and can be scrolled through via swiping gestures or graphic button elements. Other graphic interface elements may provide additional navigation via buttons, sliders, and/or touch-based input areas; the current invention provides buttons for accessing previous/next page in the user's history and a slider for quickly scrolling to specific pages of the book. The local navigation view slides up from the bottom of the screen in the current invention, ideally obscuring as little of the page as possible, and can be shown or hidden with swiping gestures.

The global navigation view provides access to a table of contents and other global application features such as bookmarks, notes, search, index, and options. Each global navigation view feature is accessed via labeled tabs that, when activated, display the selected feature in the view. The table of contents feature displays a list, textual or graphical, of chapters, sections, or other forms of content groupings. This list preferably provides an interactive hierarchy of the contents of the book, whereby top-level groupings (such as chapters) are shown, and the user may “drill down” to child nodes (such as chapter sections). When a child node is activated, the first page of the specified section is centered in the local navigation view and displayed in the main content view. Nodes may display more than just titles of sections; they may include links to multimedia objects and important content. The global navigation view slides in from the left in the current invention, and can be hidden or shown with swiping gestures or by activating one of the labeled tabs that stays visible along the left side of the screen when the view is hidden.

Along with methods for navigating and displaying content, the subject interactive mobile learning platform system of the example embodiment provides methods for marking and notating textual content. Selecting text by means of device-specific standards (such as touch-and-hold on iOS devices) prompts the user with a number of options, including “highlight” and “note.” In the preferred implementation, choosing the highlight option injects HTML tags around the selected text with JavaScript, which are styled to appear highlighted with CSS. The highlighted text, along with the page number, position, and other data, is stored in the user settings database. Choosing the note option opens the notepad view, which slides down from the top and contains an editable text body. This text is also stored and retrieved from the user settings database.

The subject interactive mobile learning platform system of the example embodiment is a customizable codebase that can be extended to fit the particular needs of a client. One such embodiment concerns collaborative learning. At any point in the current invention, social networking APIs can be utilized to enable social sharing of mediated content. APIs can also be integrated from content management or learning management systems to enable data collection and secure transfer of information, such as test scores and usage.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings incorporated herein and forming a part of the specification illustrate the example embodiments. In the drawings:

FIG. 1 is a block diagram illustrating an example of a device for implementing an example embodiment.

FIG. 2 is a block diagram illustrating a computer system upon which an example embodiment may be implemented.

FIG. 3 illustrates a schematic of an application framework and execution flow in accordance with an embodiment of the present invention.

FIG. 4 is a flowchart illustrating operations performed by the main loop controlling the user interface of FIG. 3.

FIGS. 5a-5c illustrate example user interface screens by which main views are displayed in accordance with the example embodiment.

FIG. 6 illustrates a user interface screen including a hierarchy in accordance with the example embodiment.

FIG. 7 illustrates a flowchart of a method of selecting and interacting with electronic book content of the subject learning and assessment system in accordance with the example embodiment.

FIG. 8 illustrates a flowchart of a method of interactive learning and assessment in accordance with the example embodiment.

FIG. 9 is a block diagram illustrating logic components of an interactive learning and assessment system in accordance with the example embodiment.

FIG. 10 is a functional block diagram illustrating measurement logic operable to provide learning and assessment measurements in accordance with the example embodiment.

FIG. 11 is a functional block diagram illustrating data derivation logic operable to provide derived learning and assessment measurements in accordance with the example embodiment.

FIGS. 12a-12f are block diagrams illustrating logic modules of the assessment logic of the example embodiment.

FIG. 13 is a block diagram illustrating the result logic of the example embodiment.

FIG. 14 is a block diagram illustrating the presentation logic of the example embodiment.

FIG. 15 is a radar chart displaying an assessment result of a first example user of the system of the example embodiment.

FIG. 16 is a radar chart displaying an assessment result of a second example user of the system of the example embodiment.

FIG. 17 is a radar chart displaying multiple assessment results of the first example user of the system of the example embodiment.

DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS

The following presents a simplified overview of the example embodiments in order to provide a basic understanding of some aspects of the example embodiments. This overview is not an extensive overview of the example embodiments. It is intended to neither identify key or critical elements of the example embodiments nor delineate the scope of the appended claims. Its sole purpose is to present some concepts of the example embodiments in a simplified form as a prelude to the more detailed description that is presented later.

This description provides examples not intended to limit the scope of the appended claims. The figures generally indicate the features of the examples, where it is understood and appreciated that like reference numerals are used to refer to like elements. Reference in the specification to “one embodiment” or “an embodiment” or “an example embodiment” means that a particular feature, structure, or characteristic described is included in at least one embodiment described herein and does not imply that the feature, structure, or characteristic is present in all embodiments described herein.

Referring now to FIG. 1, there is illustrated an example of a device 100 for implementing an interactive mobile learning and assessment system 10 of an example embodiment. Device 100 comprises a transceiver 102 suitable for sending and/or receiving data on a link 104. Link 104 may be a wired or wireless link, and transceiver 102 may be a wired or wireless transceiver. Logic 106 is coupled to transceiver 102 and configured to send and/or receive data from link 104 via transceiver 102. “Logic”, as used herein, includes but is not limited to hardware, firmware, software and/or combinations of each to perform a function(s) or an action(s), and/or to cause a function or action from another component. For example, based on a desired application or need, logic may include a software controlled microprocessor, discrete logic such as an application specific integrated circuit (ASIC), a programmable/programmed logic device, memory device containing instructions, or the like, or combinational logic embodied in hardware. Logic may also be fully embodied as software.

In an example embodiment, logic 106 is configured to receive data corresponding to educational, entertainment, gaming or any other material derived or sourced from any associated external system in operative communication with a predefined group via link 104. The data may be received in-band (via transceiver 102) or out-of-band (for example manually entered data, data ‘burned in’ at the factory or received from some other means other than transceiver 102).

In an example embodiment, logic 106 is responsive to a signal received from a device (not shown) via the transceiver 102 for communicating the data relating to the educational, entertainment, gaming or any other material.

In the example embodiment, the logic 106 is operable to provide interactive learning and assessment to end users.

In a further example embodiment, the logic 106 is operable to engage users in interactive learning using game-based learning experiences and assessments.

FIG. 2 illustrates a computer system 200 upon which an example embodiment may be implemented. Computer system 200 includes a bus 202 or other communication mechanism for communicating information and a processor 204 coupled with bus 202 for processing information. Computer system 200 also includes a main memory 206, such as random access memory (RAM) or other dynamic storage device coupled to bus 202 for storing information and instructions to be executed by processor 204. Main memory 206 also may be used for storing a temporary variable or other intermediate information during execution of instructions to be executed by processor 204. Computer system 200 further includes a read only memory (ROM) 208 or other static storage device coupled to bus 202 for storing static information and instructions for processor 204. A storage device 210, such as a magnetic disk or optical disk, is provided and coupled to bus 202 for storing information and instructions.

Computer system 200 may be coupled via bus 202 to a display 212 such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information to a computer user. An input device 214, such as a keyboard including alphanumeric and other keys is coupled to bus 202 for communicating information and command selections to processor 204. Another type of user input device is cursor control 216, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 204 and for controlling cursor movement on display 212. This input device typically has two degrees of freedom in two axes, a first axis (e.g. x) and a second axis (e.g. y) that allows the device to specify positions in a plane. Input device 214 may be employed for manually entering keying data.

An aspect of the example embodiment is related to the use of computer system 200 for interactive learning and learning assessment. According to an example embodiment, data corresponding to learning and assessment is provided by computer system 200 in response to processor 204 executing one or more sequences of one or more instructions contained in main memory 206. Such instructions may be read into main memory 206 from another computer-readable medium, such as storage device 210. Execution of the sequence of instructions contained in main memory 206 causes processor 204 to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in main memory 206. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement an example embodiment. Thus, embodiments described herein are not limited to any specific combination of hardware circuitry and software.

The term “computer-readable medium” as used herein refers to any medium that participates in providing instructions to processor 204 for execution. Such a medium may take many forms, including but not limited to non-volatile media and volatile media. Non-volatile media include for example optical or magnetic disks, such as storage device 210. Volatile media include dynamic memory such as main memory 206. Common forms of computer-readable media include for example floppy disk, a flexible disk, hard disk, magnetic cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASHPROM, CD, DVD or any other memory chip or cartridge, or any other media from which a computer can read.

Computer system 200 also includes a communication interface 218 coupled to bus 202. Communication interface 218 provides a two-way data communication coupling computer system 200 to a communication link 220 that is employed for communicating with other devices belonging to a predefined group. Computer system 200 can send messages and receive data, including program codes, through a network via communication link 220, and communication interface 218.

FIG. 3 illustrates a schematic of the application framework and execution flow in accordance with an embodiment of the present invention. The native application container 301 contains a scriptable 3D game engine 302, a user interface 303, and a local storage database 304. In the present invention, the 3D game engine 302 handles initialization with a startup scene 306, which passes a request to the native binding helper class 305 to switch to the user interface 303 after loading. The 3D game engine 302 runs any 3D games and any other 3D models or scenes that may be required or used to supplement the learning components. One preferred form of a 3D game engine 302 suitable for use in the subject system is Unity™ available from Unity Technologies (http://www.unity3d.com). In addition, the preferred 3D game engine 302 renders 3D content and is also a scriptable 3D game engine wherein customized models in accordance with the example embodiment may be selectively embedded and exercised by the 3D game engine to provide the desired teaching presentations and learning assessments in accordance with the embodiment.

The global navigation controller 307 loads options 308, bookmarks 309, notes 310, and other possible data from user settings 311, and also loads a content list 312 from the page database 313, which includes page entries 314 with data such as page number 315, page chapter 316, and page section 317. The content list 312 includes an outline with links to sections that, when activated, sends a message to the local navigation controller's 318 section navigation 319 to fetch and display the appropriate page thumbnails from the page database 313. The section navigation 315 in turn messages the main content controller's 320 page view 321 to display the appropriate page 322. The page 322 pulls in page data 323, such as textual content in the form of HTML in the present invention, from the page database 313. The page view 321 renders the page 322 and pulls in any necessary media content 324 from the local storage database 304 and/or from the Internet 325 or other computer network. The page data 323 may also contain links specifically formatted so as to be interpreted by the page view 321 as a request to the native binding helper class 305 to switch to the 3D game engine 302 and display one of a plurality of 3D scripted scenes 326. The 3D scripted scene 326 must contain a link or automated request to the native binding helper class 305 to switch back to the user interface 303 displaying the last page 322 viewed. Arrows connecting the 3D scene module and the page module 322 illustrate that links in the page module 322 of the user interface module 303 may be activated to transition the application to the one or more 3D scenes for learning in accordance with the embodiment.

FIG. 4 is a flowchart illustrating a method 400 comprising operations performed by the main loop controlling the user interface 303 of FIG. 3 in accordance with an example embodiment. Following an initialization step 410, a check is performed at step 412 on the user settings 311 for the last page number 315 viewed. If no record exists, the first page is retrieved 414 and displayed as a page 521 in a page view 502 such as shown, for example in FIG. 5a. In addition to retrieving the first page at 414, adjacent pages are retrieved as well wherein the adjacent pages 542b, 542c for example are displayed adjacent to the first page 542a in the thumbnail pages view 542 as shown for example in FIG. 5b. If a record does exist, the indicated last page viewed is retrieved at 416 from the page database 313 for example, and displayed as a page 521 in a page view 502 such as shown, for example in FIG. 5a. In addition to retrieving at 414 the last page viewed, adjacent pages are retrieved as well wherein the adjacent pages 542b, 542c for example are displayed adjacent to the first page 542a in the thumbnail pages view 542 as shown for example in FIG. 5b. This action causes at step 420 a message to be sent to the local navigation controller 318 to display adjacent pages such as shown for example at 542 in FIG. 5b.

Main content logic 320 in the system 300 executed at 422 to provide a main content view 521 of the display page such as shown in FIG. 5a for example.

A global navigation action 432 by the global UI logic 307 of FIG. 3, a local navigation action 433 by the local UI 318 logic of FIG. 3, or main content navigation action 434 by the main content logic 320 of FIG. 3 sends one or more appropriate messages to retrieve the appropriate page data from the local storage 304. Selection of the global navigation view such as at 432 causes the global UI logic 307 to display a global view 546 as shown in the example at FIG. 5c. The content list 312 enables page navigation within the global view 546. Selection of the local navigation view such as at 433 causes the local UI logic 318 to display a set of local navigation views of adjacent pages 540-543 as shown in the example at FIG. 5b. Selection of the main content navigation view such as at 434 causes the main content logic 320 to display a set of local navigation views of adjacent pages 540-543 as shown in the example at FIG. 5b. By a user swiping to the left or right by hand or finger the page area 542 scrolls left or right accordingly to present page selection options for viewing to the user.

When specially formatted links 522 such as shown for example in FIG. 5a occur in a page 421, they may be selectively activated at 435 to display full-screen media 436. In the full-screen media view, the 3D scene logic 326 and the page view logic 321 are operable to present full screen images and the like to the user by drawing suitable selected data form the media storage 324. A link is provided enabling the user to selectively exit at 438 the full-screen view wherein the logic returns the user to the main content view 422.

FIGS. 5a-5c illustrate example user interface screens 502, 504, and 506 by which the three main views 538, 540, and 544 are displayed in accordance with the example embodiment. Overall, the three main views comprise a main content view 538, a local navigation view 540, and a global navigation view 544. Upon initialization of the application such as at step 427 shown in FIG. 4, the system is operable to present the user with the main content view 538 which, in the example embodiment, is comprised of the page view 521 and a set of virtual tabs 539 that, when initiated or activated by the user, are operable to open sections of the global navigation view 544 as shown for example in FIG. 5c. The local navigation view 540 slides up from the bottom of the screen 541 in an embodiment of the current invention and consists of a horizontal slider of page thumbnails 542 and a slider bar 543 that displays and navigates the user's absolute position in the book. The global navigation view 544 as shown in FIG. 5c slides in from the left of the screen 545 when a tab 539 is activated or dragged in an embodiment of the current invention. The default view of the global navigation view consists of an outline of the book content 546 in the form of chapter titles that, when activated, expand to show a list of subsections 547.

FIG. 6 illustrates a user interface screen 600 including a hierarchy in one embodiment of the subject learning and assessment system by which the electronic book is navigated. The global navigation view 644 (shown as 544 in FIG. 5c) affects the rendering of the local navigation view 640 (shown as 540 in FIG. 5b), which can cause a specific page 622 to be rendered in the main content view 638 (shown as 538 in FIG. 5b). Also illustrated is the notion that all navigation and content views can be displayed simultaneously. The subject system is operable to update each of the local navigation view 640 and the specific page 622 to be rendered in the main content view 638 based on a selection by the system user of one of the items presented in the global navigation view 644. The subject system is operable to update each of the global navigation view 644 and the main content view 638 based on a selection by the system user of one of the items presented in the local navigation view 640. Yet still further, subject system is operable to update each of the global navigation view 644 and the local navigation view 640 based on a selection by the system user of one of the items presented in the main content view 638.

FIG. 7 illustrates a flowchart of a method 700 whereby a user of one embodiment of the present invention selects and interacts with electronic book content of the learning and assessment system. Initially, a startup program is initiated 702 whereby the user is provided with a view at 704 of one or more pages in accordance with the method described above in connection with FIG. 4 and, further, the user is provided with one or more choices at 706 including, in the example embodiment enabling the user to either choose which content to display 712 through various navigation views or to begin directly reading and displaying content at 714. On any given page, the user may choose at 716 to interact with certain multimedia objects from the page database 313 and presented on the screen, including manipulating interactive in-line page objects 720 and activating links to full-screen previews of static media 722. The system further provides the user with the selectable option to view three dimensional data such as the media data 324 by activating at 724 the 3D game engine module 302, which may consist of a 3D object to manipulate 730. Selection by the user of playing a learning game activates the game module at 750 followed in the example embodiment by a learning assessment performed by the assessment module 756 and further followed by generation of assessment results by the assessment result module 760.

In addition, in accordance with the example embodiment, the user may make a selection at 770 to replay the learning game. In the flowchart illustrated, an election to replay the game returns control to the game module step 750.

After activating and completing a game assessment module, any score or other measurement of comprehension is stored and displayed as necessary or desired, and possibly submitted to a learning management system or other internet or network database.

FIG. 8 illustrates a flow diagram of an overall method 800 for interactive learning and assessment in accordance with the example an embodiment. Learning occurs in the subject system during the presentation by the system to the user of information which may be absorbed or otherwise understood by the user. The information may be textual, illustrative, video, audio or of any other form as necessary or desired. In any case, for purposes of explanation, in the example shown, the course material is divided into separate “units” wherein the user may learn and be assessed with respect to the learning within these separate learning units. In the example, the learning and assessment occurs in first 810, second 812 and nth 814 learning stages. The system of the example embodiment executes logic within each of the learning stages comprising a sequence of learn presentation 859, interact logic 860, test logic 861. The learning and assessment system is operable to enable the user to enter the sequence at the “Learn” logic module 859, which is comprised of non-interactive information, including textual, graphical, auditory, and video media. In accordance with the example embodiment, the learn logic 859 is operable while the user is actively viewing content such as, for example, in steps 702-714 of FIG. 7. Examples include reading the electronic book contents or viewing pictures of the electronic book, for example. It is to be appreciated that in accordance with the example embodiment, the measurement logic 932 may selectively log these learning activities for purposes of developing measurement data 1000 as will be discussed below in greater detail.

When ready, the system is operable to enable the user to move or otherwise enter into the “Interact” logic module 860 which, in the example embodiment, illuminates specific and focused elements of the learning material presented during execution of the “Learn” logic module 859 and including custom-designed interactive experiences, such as manipulate-able 2D or 3D objects, simulations, and games. The system of the example embodiment is operable to engage the user in the interact logic module and challenge the user to apply the knowledge absorbed in the learning phase such as by presenting layered information and by presentation of problem/solution scenarios, questions and answers, or the like. In accordance with the example embodiment, the interact logic 860 is operable while the user is actively interacting with the content such as, for example, while the user is manipulating interactive objects, previewing full screen media objects or items, and while the user is activating the 3D engine module such as for example in steps 720-724 of FIG. 7. It is to be appreciated that in accordance with the example embodiment, the measurement logic 932 selectively logs these interaction activities for purposes of developing measurement data 1000 as will be discussed below in greater detail.

After completing the learning and interactive modules 859, 860 or otherwise exiting, the system is operable to enable the user to move or otherwise enter the “Test” logic module 861. This module can consist of traditional assessment methods such as multiple-choice questions, or, preferably such as in the example embodiment, it consists of an interactive assessment based on the “Interact” logic module 860. The interactive assessment is custom-designed to focus on specific learning objectives derived from the “Learn” 859 and “Interact” 860 logic modules, and scores the user based on those learning objectives and not on the user's skill at manipulating the virtual space. The “Learn, Interact, Test” triads 858, 858′, 858″ are, in the example embodiment, an ongoing cycle, moving on to different learning objectives after each cycle is completed. This model can be extrapolated as comprising the “Learn” module of an overarching cumulative triad 862; after completing a series of cycles, a cumulative interactive module and cumulative assessment module can be selectively presented to the user.

In accordance with the example embodiment, the test logic 861 is operable while the user is actively interacting with game module 750 such as, for example, while the user is playing a learning game within the game module in step 750 and further, while the user operates the assessment logic and assessment result logic of steps 756 and 760 of FIG. 7. Essentially, in accordance with the example embodiment, the knowledge gained in the learn and interact phases presented to the user by the system is applied by the system against the user in the game module 750. It is to be appreciated that in accordance with the example embodiment, the measurement logic 932 selectively logs these learning game activities occurring during execution of the game module 750 such as, for example, user's scores relative to one or more sets of learning objectives, for purposes of developing measurement data 1000 as will be discussed below in greater detail.

As noted above, the subject interactive learning and assessment system advantageously adopts a gaming assessment philosophy for reasons including because game-based learning experiences within the system of the example embodiment provide users with an engaging way of learning information while providing educators with an accurate assessment model for confirming the learning experience. Games appeal to users of all ages and genders, and games motivate users to do better, to navigate through levels towards a targeted goal, and to achieve successes through experiential learning. The system of the example embodiment enables users to experiment through gameplay in a safe environment where failure to produce an expected result does not negatively impact the final “grade” or “score” within set parameters. This experimentation inherent to game-based learning helps users develop critical thinking skills, heightens engagement in learning experiences, and ultimately increases comprehension of a subject matter. Accordingly, in the example embodiment, the assessment module 756 (FIG. 7) comprises logic modules operable to provide assessment measures in selected learning areas including for example knowledge retention, engagement, perseverance, comprehension, skill, and interest. The game module pits the users against a set of learning objectives and the assessment module tests the user's learning. In accordance with the embodiment, the game module is engaging, visually interesting and intellectually compelling so that the user feels comfortable for enhanced learning. The learning objectives are based on the particular subject matter of the course work and are selectively drawn from the local storage 304 as needed and in accordance with a selected curriculum. In an embodiment, the learning objectives based on selected subject matter of the course work may be selectively drawn from external sources such as the Internet 325 alone or in combination with the local storage 304 as needed and in accordance with a selected curriculum.

Accordingly and with reference now to FIG. 9, the assessment module 756 of the example embodiment includes assessment logic 910 operable to generate learning assessment metrics including metrics for the set of learning areas identified above. To this end, the assessment logic 910 includes knowledge retention logic 912 operable to generate a knowledge retention learning metric, engagement logic 914 operable to generate an engagement learning metric, perseverance logic 916 operable to generate a perseverance learning metric, comprehension logic 918 operable to generate a comprehension learning metric, skill logic 920 operable to generate a skill learning metric, and interest logic 922 operable to generate an interest learning metric. Although the example embodiment provides learning assessment in learning areas of knowledge retention, engagement, perseverance, comprehension, skill, and interest, the embodiment is not so limited and the system may be extended to other areas of learning assessment as desired.

In addition to the above and with continued reference to FIG. 9, the assessment module 756 of the example embodiment further includes improvement logic 930 operable to generate an improvement metric based on selected one or more of the learning assessment areas listed above, measurement logic 932 operable to generate selected learning parameter measurement data, and data derivation logic 934 operable to derive selected composite data from the selected learning parameter measurement data obtained from the measurement logic 932.

As noted above, the knowledge retention logic 912 is operable to generate a knowledge retention learning metric. In accordance with the example embodiment, “Retention” is measured by averaging score accuracy and factoring in any change in score over multiple attempts. Retention generally reflects the average score, impacted positively or negatively depending on an increase or decrease in scores.

In accordance with the example embodiment, the engagement logic 914 is operable to generate an engagement learning metric wherein “Engagement” is measured by averaging Retention, the rate of interaction, and exploration. Retention is used in calculating Engagement because it provides a value of improvement in score over all attempts, and if there is little improvement it is unlikely that the learner is engaged. “Interactions” are defined in accordance with the example embodiment as any input events received by the computer from the player, and each game has a unique, ideal interaction rate based on user testing. Exploration is a percentage of the number of choices made vs. an ideal number of choices based on data from user testing; high exploration indicates a broader range of learning opportunities aside from the singular game objective.

Further in accordance with the example embodiment, the perseverance logic 916 operable to generate a perseverance learning metric, wherein “Perseverance” is measured by giving increased weight to multiple attempts, and factoring in the precision and improvement of scores. The number of attempts is significant in developing the perseverance learning metric wherein a high Perseverance measure may indicate that the learner is trying but not comprehending well, whereas a low Perseverance measure may indicate that they are just not trying.

The comprehension logic 918 in accordance with the example embodiment is operable to generate a comprehension learning metric. In the embodiment, “Comprehension” is measured by averaging Retention, improvement in time, and score accuracy over the number of attempts. Comprehension decreases with each successive attempt; otherwise, it will generally reflect Retention. If multiple attempts show improvement in time, in accordance with the example embodiment, Comprehension is maintained, but if there is a decline in time, Comprehension may drop drastically.

Lastly with regard to the assessment logic 910 of FIG. 9, skill logic 920 is operable to generate a skill learning metric, and interest logic 922 is operable to generate an interest learning metric. In the embodiment, “Skill” is measured by averaging score, time, and interaction rate. Skill measures the player's ability to manipulate the game environment and does not necessarily reflect how much he has learned about the content. Further in the embodiment, “Interest” is measured by averaging Retention, Comprehension, and Engagement. Interest is weighted in favor of Engagement, accounting for players who may be interested but are having difficulty retaining or comprehending.

The improvement logic 930 is operable to generate an improvement metric based on selected one or more of the learning assessment areas listed above. In this regard, in accordance with the example embodiment, the improvement logic 930 specifies a percentage of improvement in relation to the input values and the expected goal value over multiple attempts.

As noted above, in the example embodiment, the measurement logic 932 is operable to generate selected learning parameter measurement data. With reference now to FIG. 10, the measurement logic 932 is in operative communication with the 3D game engine 302 (FIG. 3), the user interface 303, and the local storage 304 to generate the learning parameter measurement data 1000 in accordance with the user's interaction with the interactive learning system of the example embodiment. Preferably, the learning parameter measurement data 1000 includes Score data 1002, Time data 1004, Attempts data 1006, Number of Interactions data 1008, and Options Chosen data 1010. The Score data 1002 is derived in the example embodiment as the user collects points or other units of score measurement from the game module 750 and possibly other areas of the 3D game engine 302 and user interface 303, which is then compared to a total score or goal score data derived from local storage 304. Similarly, the Time data 1004 is derived in the example embodiment as the user is timed from the initialization to the completion of the game module 750 and possibly other areas of the 3D game engine 302 and user interface 303, which is also compared to a goal time data derived from local storage 304. The Attempts data 1006 is derived in the example embodiment as the user replays the singular game module 750. The Number of Interactions data 1008 is derived in the example embodiment as the user clicks with a computer mouse, touches a touch-sensitive surface, enters input from a keyboard, or other methods of user interaction with the computer system 200 input device 214 or cursor control 216. The Options Chosen data 1010 is derived in the example embodiment as the user makes decisions based on choices presented in the game module 750, wherein the number of choices made is compared with a goal number of choices data derived from local storage 304.

In accordance with the example embodiment, the data derivation logic 934 is operable to derive selected composite data from the selected learning parameter measurement data 1000 obtained from the measurement logic 932. With reference next to FIG. 11, the data derivation logic 934 is operable to receive selected data from among the learning parameter measurement data 1000 (FIG. 10) and generate Accuracy data 1102 based on the score data 1002 and a predefined goal score, Precision data 1104 based on closeness of scores data derived from attempt data 1006 and Exploration data 1108 based on the options chosen data 1010 and predefined goal options chosen data. More particularly, the Accuracy data 1102 is derived based on score data divided by goal score data. The Precision data 1104 is derived based on the standard deviation of the score data from the goal score data. The Exploration data 1108 is derived based on the average of options chosen data 110 divided by goal options chosen data.

With reference next to FIG. 10, the measurement logic 932 is operative to generate measurement data 1000. In the example embodiment, the measurement data comprises score data 1002, time data 1004, attempt data 1006, interactions data 1008, and options data 1010. More particularly, measurement logic 932 is operative to receive raw data from the 3D game engine 302, the user interface module 303, and the local storage 304 and to convert the raw data in to the measurement data 1000. In the example embodiment, the score data 1002 is representative of the score of a user as measured against a set of learning objectives, and the time data 1004 is a measure of the amount of time the user consumed during the learning 859 and interacting 860 using the subject system. The attempt data 1006 is representative of the number of re-tries 770 pursued by the user during the learning. The attempts are also represented by learn, interact and test loops within any of the units 858, 858′ as shown in FIG. 8. The interactions data 1008 is representative of a quantity of interactions by the user with the subject system such as, for example, key strokes, screen touches, pages viewed, or any other form of control by the user over the system during a course of learning using the system. The options data 1010 is representative of the amount of utilization by the user of the range of game options available to the user by the game module 750. Users who completely exercise the game options develop a high utilization score and users who have only simple interaction develop a low utilization score.

With reference next to FIG. 11, the data derivation logic 934 is operative to generate a set of derived data 1100 including, in the example embodiment, accuracy data 1102, precision data 1104, and exploration data 1108. As shown, the data derivation logic 934 is configured to receive selected items of the measurement data 1000 generated by the measurement logic 932 in a manner described above.

For ease of understanding, the functional operation of the data derivation logic 934 will be described below through use of example pseudocode as follows:

Set times to array of time measured per attempt
Set goal_time to time expected for success
Set scores to array of scores recorded per attempt
Set goal_score to total possible score OR expected score value
Set attempt to current attempt value (between 0 and attempts)
Set attempts to number of attempts/replays of the game (length of
scores)
Set interactions to array of interactions measured per attempt
Set goal_interactions to expected interactions value
Set rank to number based on score vs global or local score database
Set options to array of the number of options chosen or choices
made per attempt
Set goal_options to total possible options OR expected number of
options chosen

In the example embodiment, the data derivation logic 934 is operative to generate the accuracy data 1102 of the set of derived data 1100 as follows:

Function Accuracy(score, goal_score)
If score is greater than or equal to goal_score
Return 1
Else
Return score divided by goal_score
End Function
Function Average(array)
Return Sum_of_all(array) divided by length of array
End Function
Function Sum_of_all(array)
Set sum to 0
For each item in array
Add item to sum
End For
Return sum
End Function
Function WeightedAverage(array)
Set total to 0
Set weight to 1
For each item in array
Add product of item & weight to total
Increment weight by 1
End For
Set weight_total to weight / 2 * (weight + 1)
Return total divided by weight_total
End Function
Function AverageAccuracy(scores, goal_score)
Set accuracies to array of Accuracy(score, goal_score) for each
score in scores
Return Average(accuracies)
End Function
Function InteractionRatio(interactions, goal_interactions, times,
goal_time)
Set ratio to Sum_of_all(interactions) / Sum_of_all(times) *
goal_time / goal_interactions
Return Constrain(ratio)
End Function
Function ImprovementHigher(array)
Set total_improve to 0
If array length is greater than 1
For each item in array (except last item)
Set partial_imp to next item minus item, divided
by item
Add partial_imp to total_improve
End For
Return total_improve divided by (array length minus 1)
Else
Return 0
End If
End Function
Function ImprovementLower(array)
Set total_improve to 0
If length of array is greater than 1
For each item in array (except last item)
Set partial_imp to item minus next item, divided
by item
Add partial_imp to total_improve
End For
Return total_improve divided by (array length minus 1)
Else
Return 0
End If
End Function
Function Variance(scores)
Set total_var to 0
For each score in scores
Add score minus Average(scores), squared, to total_var
End For
Return total_var divided by length of scores
End Function

In the example embodiment, the data derivation logic 934 is operative to generate the precision data 1104 of the set of derived data 1100 as follows:

Function Precision(scores, goal_score)
Set standard_deviation to the square root of Variance(scores)
Return 1 minus (the reciprocal of goal_score) multiplied by
standard_deviation
End Function
Function Constrain(number)
If number is less than 0, set to 0
If number is greater than 1, set to 1
Return number
End Function

In the example embodiment, the data derivation logic 934 is operative to generate the exploration data 1108 of the set of derived data 1100 as follows:

Function Exploration(options, goal_options)
Return Average(options) divided by goal_options
End Function

With reference next to FIG. 12a, the retention logic 912 is configured to receive selected items of the measurement data 1000 generated by the measurement logic 932 in a manner described above and also to receive selected items of the derived data 1100 generated by the data derivation logic 934 in a manner described above. The received data includes score data 1002, accuracy data 1102, and attempts data 1106. The retention logic of the example embodiment is operative to generate retention data 1202

For ease of understanding, the functional operation of the retention logic 912 will be described below through use of example pseudocode as follows:

Function Retention(scores, goal_score)
Set avg_diff to 0
If length of scores is greater than 1
For each score in scores (except last score)
Add Accuracy(next score, goal_score) minus Accuracy(score,
goal_score) to avg_diff
End For
Divide avg_diff by length of scores − 1
End If
Set total_ret to sum of avg_diff & AverageAccuracy(scores)
Return Constrain(total_ret)
End Function

With reference next to FIG. 12b, the engagement logic 914 is configured to receive selected items of the measurement data 1000 generated by the measurement logic 932 in a manner described above and also to receive selected items of the derived data 1100 generated by the data derivation logic 934 in a manner described above. The received data includes time data 1004, exploration data 1106, attempts data 1106, and interactions data 1008. The engagement logic 914 of the example embodiment is operative to generate engagement data 1204.

For ease of understanding, the functional operation of the engagement logic 912 will be described below through use of example pseudocode as follows:

Function Engagement(interactions, goal_interactions, times,
goal_time, retention_value, exploration_value)
Set interaction_weight to InteractionRatio(interactions,
goal_interactions, times, goal_time)
Set total_eng to the sum of interaction_weight &
exploration_value & retention_value, divided by 3
Return Constrain(total_eng)
End Function

With reference next to FIG. 12c, the perseverance logic 916 is configured to receive selected items of the measurement data 1000 generated by the measurement logic 932 in a manner described above and also to receive selected items of the derived data 1100 generated by the data derivation logic 934 in a manner described above. The received data includes time data 1004, attempts data 1106, and score data 1002. The perseverance logic 916 of the example embodiment is operative to generate perseverance data 1206.

For ease of understanding, the functional operation of the perseverance logic 916 will be described below through use of example pseudocode as follows:

Function Perseverance(scores, goal_score times)
Set attempt_factor to 1 minus the reciprocal of the length of
scores
Set precision_factor to Precision(scores, goal_score)
Set improvement_factor to ImprovementHigher(scores)
Set total_pers to attemt_factor + (1 − attempt_factor) *
improvement_factor * precision_factor
Return Constrain(total_pers)
End Function

With reference next to FIG. 12d, the comprehension logic 918 is configured to receive selected items of the measurement data 1000 generated by the measurement logic 932 in a manner described above and also to receive selected items of the derived data 1100 generated by the data derivation logic 934 in a manner described above. The received data includes time data 1004, attempts data 1106, score data 1002, and accuracy data 1102. The comprehension logic 918 of the example embodiment is operative to generate comprehension data 1208.

For ease of understanding, the functional operation of the comprehension logic 918 will be described below through use of example pseudocode as follows:

Function Comprehension(scores, goal_score, times, goal_time,
retention_value)
Set reduction_rate to 0.98
Set improvement_factor to 0.02 *
Constrain(ImprovementLower(times))
Set total_comp to retention_value * ((reduction_rate +
improvement_factor) to the power of the length of scores − 1)
Return total_comp
End Function

With reference next to FIG. 12e, the skill logic 920 is configured to receive selected items of the measurement data 1000 generated by the measurement logic 932 in a manner described above and also to receive selected items of the derived data 1100 generated by the data derivation logic 934 in a manner described above. The received data includes time data 1004, attempts data 1106, score data 1002, accuracy data 1102, and interactions data 1008. The skill logic 920 of the example embodiment is operative to generate skill data 1210.

For ease of understanding, the functional operation of the skill logic 920 will be described below through use of example pseudocode as follows:

Function Skill(scores, goal_score, times, goal_time interactions,
goal_interactions, [rank])
Set score_factor to WeightedAverage(scores) divided by
goal_score
Set time_factor to Constrain(goal_time divided by
WeightedAverage(times))
Set interaction_factor to Constrain(1 −
(WeightedAverage(interactions) − goal_interactions) /
goal_interactions)
Return product of score_factor, time_factor, &
interaction_factor
End Function

With reference next to FIG. 12f, the interest logic 922 is configured to receive selected retention data 1202 from the retention logic 912, engagement data 1204 from the engagement logic 914, and comprehension data 1208 from the comprehension logic 918. The interest logic 922 of the example embodiment is operative to generate interest data 1212.

For ease of understanding, the functional operation of the interest logic 922 will be described below through use of example pseudocode as follows:

Function Interest(retention_value, comprehension_value,
engagement_value)
Return (retention_value + engagement_value * 2 +
comprehension_value) / 4
End Function

With reference next to FIG. 13, the result logic 936 of the assessment module 756 (FIGS. 7, 9) is operative to receive the outputs of the logic modules 912-922, perform one or more operations on the data 1202-1212, and generate result data 1302 suitable for use by users such as educators or the like in making learning assessments in accordance with the example embodiment.

FIG. 14 illustrates a block diagram of the presentation logic 938 of the assessment module 756 (FIGS. 7, 9), wherein the presentation logic 938 is operative to receive the one or more outputs of the result logic 936, perform one or more operations on the data, and generate presentation data 1402 suitable for use in the example embodiment for displaying on a display screen 212 (FIG. 2) or the like the learning assessment results in a simple and easily comprehensible way. In the example embodiment, the presentation data 1402 is configured to be used in generating a radar chart such as shown, for example, in FIGS. 15-16 to be described below.

With reference now to those drawings, FIG. 15 shows a radar chart 1500 of a “User A” in an example to be described below and FIG. 16 shows a radar chart 1600 of a “User D” in the example. In the example embodiment, the presentation logic 938 is operative to generate the presentation data 1402 in a manner to be suitable for use by the system of the example embodiment for presentation on the display 212 (FIG. 2).

In the example, ideal scores and user behavior is as follows:

Ideal

Attempt #
1
Scores       100
Times        100
Interactions 100
Options      100
Retention: 100%
Engagement: 100%
Perseverance: 0%
Comprehension: 100%
Skill: 100%
Interest: 100%

User A

	Attempt #
	1	2
	Scores	85	90
	Times	120	110
	Interactions	110	105
	Options	80	90
	Retention: 92.5%
	Engagement: 90.33%
	Perseverance: 52.87%
	Comprehension: 90.8%
	Skill: 72.75%
	Interest: 90.99%

The results of the learning assessment of User A in the above example is represented in the radar line 1502 presented in the radar chart 1500 of FIG. 15.

User B

	Attempt #
	1	2	3	4
	Scores	25	50	60	90
	Times	200	160	170	110
	Interactions	170	120	110	120
	Options	80	70	85	90
	Retention: 77.92%
	Engagement: 80.14%
	Perseverance: 85.87%
	Comprehension: 74.07%
	Skill: 35.29%
	Interest: 78.07%

User C

Attempt #	1	2	3	4	5	6
Scores	80	85	92	96	97	99
Times	130	132	120	110	104	102
Interactions	120	114	110	105	98	99
Options	92	95	97	97	99	100
Retention: 95.3%
Engagement: 94.84%
Perseverance: 84.01%
Comprehension: 86.55%
Skill: 82.17%
Interest: 92.88%

User D

	Attempt #
	1	2	3
	Scores	50	60	20
	Times	140	120	80
	Interactions	70	80	40
	Options	60	50	30
	Retention: 28.33%
	Engagement: 43.63%
	Perseverance: 60.21%
	Comprehension: 27.48%
	Skill: 37.1%
	Interest: 35.77%

The results of the learning assessment of User D in the above example is represented in the radar line 1602 presented in the radar chart 1600 of FIG. 16.

FIG. 17 is a radar chart 1700 of an example user using the system of the example embodiment to execute multiple learning and assessment cycles 858, 858′, 862 such as shown diagrammatically in FIG. 8 wherein for a first unit of study or learning (unit 1) the system generates a first radar line 1702, for a second unit of study or learning (unit 2) the system generates a second radar line 1704, for a third unit of study or learning the system generates a third radar line 1706, for a fourth unit of study or learning the system generates a fourth radar line 1708, and for the average learning and assessment the system generates an average radar line 1710.

Described above are example embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies, but one of ordinary skill in the art will recognize that many further combinations and permutations of the example embodiments are possible. Accordingly, this application is intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims interpreted in accordance with the breadth to which they are fairly, legally and equitably entitled.

Claims

1. A computer implemented learning and assessment apparatus comprising:

a non-transient memory;

a database stored in the memory, the database having at least one set of educational game parameters;

a processor operable to receive input signals from a user of the apparatus;

a human readable display;

game logic operable to generate a game-based learning experience by presenting to the user a virtual game on the display in accordance with a selected first one of the set of educational game parameters;

measurement logic operable to generate measurement data based on the input signals received from a user by the processor, the measurement data being representative of actions of the user during interaction by the user with the virtual game;

assessment logic operable to generate assessment data based on the measurement data, the assessment data being representative of gameplay results wherein a failure of the user to produce predetermined expected learning results based on the selected first one of the set of educational game parameters is weighted in accordance with predetermined game-based learning parameters relative to experiential exercise of the virtual game by the user; and,

result logic operable to generate in accordance with the assessment data, a result signal for selective rendering on the human readable display.

2. The apparatus according to claim 1 wherein:

the measurement logic is operable to generate, as the measurement data, score data representative of a score of the user during interaction by the user with the virtual game as measured against a set of learning objectives; time data representative of an amount of time consumed by the user consumed during interaction by the user with the virtual game; attempts data representative of an number of re-tries pursued by the user during interaction by the user with the virtual game; interactions data representative of a quantity of interactions by the user with the game logic, wherein the interactions comprise key strokes, screen touches of the display, pages viewed, and exercise of control by the user over the apparatus during interaction by the user with the virtual game; and, options data representative of an amount of utilization by the user of a range of game options available to the user by the game logic.

3. The apparatus according to claim 2 wherein:

the assessment logic is operable to generate, as the assessment data, retention data representative of average score accuracy during interaction by the user with the virtual game and comprising a factor for any changes in score over multiple attempts.

4. The apparatus according to claim 3 wherein:

the assessment logic is operable to generate, as the assessment data, engagement data representative of an average of the retention data, a rate of interaction by the user with the virtual game, and exploration by the user with the virtual game.

5. The apparatus according to claim 2 wherein:

the assessment logic is operable to generate, as the assessment data, perseverance data representative of an increased weighting to multiple attempts by the user interacting with the virtual game, and comprising a factor in accordance with precision by the user interacting with the virtual game and an improvement of scores by the user interacting with the virtual game.

6. The apparatus according to claim 3 wherein:

the assessment logic is operable to generate, as the assessment data, comprehension data representative of an average of the retention data, improvements in time by the user interacting with the virtual game, and score accuracy over multiple attempts by the user interacting with the virtual game.

7. The apparatus according to claim 2 wherein:

the assessment logic is operable to generate, as the assessment data, skill data representative of an average score, an average time, and an average interaction rate by the user interacting with the virtual game.

8. The apparatus according to claim 4 wherein:

the assessment logic is operable to generate, as the assessment data, interest data representative of an average of the retention data, the engagement data, and comprehension data representative of an average of the retention data, improvements in time by the user interacting with the virtual game, and score accuracy over multiple attempts by the user interacting with the virtual game.

9. The apparatus according to claim 2 wherein the assessment logic is operable to generate, as the assessment data:

retention data representative of average score accuracy during interaction by the user with the virtual game and comprising a factor for any changes in score over multiple attempts;

engagement data representative of an average of the retention data, a rate of interaction by the user with the virtual game, and exploration by the user with the virtual game;

perseverance data representative of an increased weighting to multiple attempts by the user interacting with the virtual game, and comprising a factor in accordance with precision by the user interacting with the virtual game and an improvement of scores by the user interacting with the virtual game;

comprehension data representative of an average of the retention data, improvements in time by the user interacting with the virtual game, and score accuracy over multiple attempts by the user interacting with the virtual game;

skill data representative of an average score, an average time, and an average interaction rate by the user interacting with the virtual game; and,

interest data representative of an average of the retention data, the engagement data, and the comprehension data.

10. The apparatus according to claim 9 further comprising:

result logic configured to receive the retention data, the engagement data, the perseverance data, the comprehension data, the skill data, and the interest data, and being operable to generate a result signal in accordance with the retention data, the engagement data, the perseverance data, the comprehension data, the skill data, and the interest data, wherein the result signal is representative of a learning assessment of the user interacting with the virtual game.

11. The apparatus according to claim 1 wherein:

the result logic operable to generate the result signal as a line on a radar chart for selective rendering on the human readable display.

12. A learning and assessment method in an apparatus comprising a non-transient memory, a database stored in the memory, the database having at least one set of educational game parameters, a processor operable to receive input signals from a user of the apparatus, and a human readable display, the method comprising:

generating, by game logic of the apparatus, a game-based learning experience by presenting to the user a virtual game on the display in accordance with a selected first one of the set of educational game parameters;

generating, by measurement logic of the apparatus, measurement data based on the input signals received from a user by the processor, the measurement data being representative of actions of the user during interaction by the user with the virtual game;

generating, by assessment logic of the apparatus, assessment data based on the measurement data, the assessment data being representative of gameplay results wherein a failure of the user to produce predetermined expected learning results based on the selected first one of the set of educational game parameters is weighted in accordance with predetermined game-based learning parameters relative to experiential exercise of the virtual game by the user; and,

generating, by result logic of the apparatus in accordance with the assessment data, a result signal for selective rendering on the human readable display.

13. The learning and assessment method according to claim 12, wherein the generating the measurement data by the measurement logic comprises:

generating score data representative of a score of the user during interaction by the user with the virtual game as measured against a set of learning objectives;

generating time data representative of an amount of time consumed by the user consumed during interaction by the user with the virtual game;

generating attempts data representative of an number of re-tries pursued by the user during interaction by the user with the virtual game;

generating interactions data representative of a quantity of interactions by the user with the game logic, wherein the interactions comprise key strokes, screen touches of the display, pages viewed, and exercise of control by the user over the apparatus during interaction by the user with the virtual game; and,

generating options data representative of an amount of utilization by the user of a range of game options available to the user by the game logic.

14. The learning and assessment method according to claim 13, wherein the generating the assessment data by the assessment logic comprises:

generating, by retention logic of the assessment logic, retention data representative of average score accuracy during interaction by the user with the virtual game and comprising a factor for any changes in score over multiple attempts;

generating, by engagement logic of the assessment logic, engagement data representative of an average of the retention data, a rate of interaction by the user with the virtual game, and exploration by the user with the virtual game;

generating, by perseverance logic of the assessment logic, perseverance data representative of an increased weighting to multiple attempts by the user interacting with the virtual game, and comprising a factor in accordance with precision by the user interacting with the virtual game and an improvement of scores by the user interacting with the virtual game;

generating, by comprehension logic of the assessment logic, comprehension data representative of an average of the retention data, improvements in time by the user interacting with the virtual game, and score accuracy over multiple attempts by the user interacting with the virtual game;

generating, by skill logic of the assessment logic, skill data representative of an average score, an average time, and an average interaction rate by the user interacting with the virtual game; and,

generating, by interest logic of the assessment logic, interest data representative of an average of the retention data, the engagement data, and the comprehension data.

15. The learning and assessment method according to claim 14, further comprising:

receiving, by the result logic, the retention data, the engagement data, the perseverance data, the comprehension data, the skill data, and the interest data, and generating by the result logic, a result signal in accordance with the retention data, the engagement data, the perseverance data, the comprehension data, the skill data, and the interest data, wherein the result signal is representative of a learning assessment of the user interacting with the virtual game.

16. The learning and assessment method according to claim 15, further comprising:

generating, by the result logic, the result signal as a line on a radar chart and selectively rendering the line on the radar chart on the human readable display of the apparatus.