Abstract:
This paper proposes a new method for the objective evaluation of student work through the identification of original content in writing assignments. Using WordNet as a lexical reference, this process allows instructors to track how key phrases are employed and evolve over the course of a student&#39;s writing, and to automatically visualize the point at which the student&#39;s language first demonstrates original thought, phrased in their own, original words. After sketching the method for isolating “points of originality,” the paper provides a method for visualizing the resulting information. By visualizing otherwise subjective information in a way that is objectively intelligible, the goal is to provide educators with the ability to monitor student investment in concepts from the course syllabus, and to extend or modify the boundaries of the syllabus in anticipation of pre-existing knowledge or trends in interest.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     Not Applicable. 
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH 
     Not Applicable. 
     FIELD OF THE INVENTION 
     The concepts described herein relate to a system and method for evaluating writing and more particularly to a system and method for the objective evaluation of a writing through the identification of original content in the writing. 
     BACKGROUND OF THE INVENTION 
     As is known in the art, for most if not all learning activities, a substantial amount of an instructor&#39;s time and effort is devoted to evaluating and monitoring the quality of students&#39; work, and thus, hopefully, the depth of their learning. The purpose of this monitoring, however, is not merely the determination of grades; part of the instructor&#39;s work is entirely self-reflective, enabling the instructor to concurrently, or ideally even preemptively, intervene to make adjustments to course pedagogy based on students&#39; engagement or understanding. While assigning grades might be facile, some difficulties complicate this second objective: how might an instructor intuit when, precisely, students have understood the material sufficiently? Making this determination manually would prove an intensely laborious and time consuming process, far more complicated than simple reading and re-reading of any single student&#39;s work. 
     When students engage in a writing activity, the final evaluation of their work cannot only assess whether the student has provided the most closely correct answer. Process is just as relevant to student writing as content. Student writing considered by an instructor to be exceptional is generally seen as that which demonstrates a mastery of the course material in new, profound or statistically unusual ways. The ideal is not only for students to confirm that they&#39;ve understood lectures, but to do so in ways that even the instructor might not have thought of. 
     This process of mastery need not take place all at once. As a student is continually exposed to the same material, or is given the independent opportunity to rethink, reframe, or revisit that material, their writing on the subject has the chance to evolve, from rote regurgitation to wholly original expression. At the level of language, this evolution is reflected through recasting. 
     Recasting is the learning process whereby a student refines his or her understanding of a concept found in course lectures or readings by putting that concept into his or her own words. In the acquisition of new languages especially, this process can be useful, because it allows students to acquire new vocabulary using the assortment of words already available to them. Even where the student&#39;s understanding of a language is not an explicit concern, recasting can mark a student&#39;s attempts to graduate to more sophisticated or professionalized terminology, or, inversely but to the same end, to place new concepts into terms that are nearer to what the student would naturally be more likely to say. This process of learning aligns with theories of schema formulation, the sense-making process known as “scaffolding”, as well as the express principles of educational constructivism. 
     For an instructor, the simple identification of recast terminology within a student&#39;s written work can provide an effective barometer for pedagogical self-reflection. If a subset of terms or concepts are deemed vital to the syllabus, repetitions and recast iterations of those same terms will at least suggest that those terms are being acknowledged and reflected upon. 
     SUMMARY OF THE INVENTION 
     In accordance with the concepts, techniques and systems described herein, it has been recognized that if the instructor hopes not only to identify instances where key concepts are deployed, but to determine how comprehensively the concepts are being internalized, it is first necessary to possess a method of scoring how original any given recast might be. In order to do this, we propose a metric for isolating a specific point of originality within student writing. 
     Also, it should be appreciated that, at higher levels of education, there is an increasing trend requiring students to submit materials in electronic form. 
     Through the application of lexical analysis to a writing (e.g. a student writing), and in accordance with the concepts, systems and techniques described herein, a method and system to track how a student&#39;s written language migrates from mere paraphrase to mastery are described. The method and system operate by isolating the moment when the student&#39;s understanding of core concepts best demonstrates the pedagogical principle of recasting. This moment is referred to herein as “point of originality.” 
     The system and process described herein provide a model having a correspondence to cognitive activity which may be similar to, and ideally the same as, that which instructors or others might ordinarily undergo, yet in an automatic manner that is far less labor intensive. In one embodiment, the resulting data is presented to an evaluator (e.g. an instructor) by way of custom visualizations, which allow the evaluator to engage in continuous self-monitoring with minimally expended effort. 
     In accordance with the concepts, systems and techniques described herein, a method for evaluating a writing through the identification of original content in the writing includes: (a) submitting the writing to a processing system; (b) inputting a query term; (c) constructing a lexical relationship matrix of relationships between lexical terms generated from the query term; (d) searching the writing for terms which appear in the lexical relationship matrix; and (e) computing an originality estimate value for each of the terms in the writing which match one or more terms in the lexical relationship matrix, wherein the originality estimate value is based upon the lexical relationships between the terms in the writing and the terms in the lexical relationship matrix. 
     With this particular arrangement, a method for evaluating a point of originality in a writing is provided. Typically, a plurality of query terms are provided and terms and relationships generated from the query terms are stored in one or more lexical relationship matrices. In one embodiment, a single lexical relationship matrix holds terms and relationships generated via a lexical database from a plurality of different query terms. 
     In one embodiment, computing the originality estimate value is accomplished by computing an originality estimate value a according to the equation: α=δ×0.7×t in which δ corresponds to a value representing a distance (i.e. a metric) between the word in the writing found in the lexical relationship matrix and the query term; and t corresponds to a value representing a word type—i.e. a relationship (e.g. synonym, antonym, hypernym, hyponym, holonym, meronym) between the query terms and terms found in the lexical database. 
     In one embodiment, the method further includes repeating (b)-(e) for a plurality of different query terms to provide a corresponding plurality of originality estimate values and summing the plurality of originality estimate values to produce a point of originality score for the writing. 
     In one embodiment, submitting a writing to a processing system includes checking a format of an electronic document containing the writing to determine whether the electronic document is in a format accepted by the processing system. I n response to the electronic document not being in a format accepted by the processing system, then either notifying a user of a formal incompatibility or placing the document in a format accepted by the processing system. 
     In one embodiment after computing a point of originality score the process and system can present the point of originality score on a display in the form of a timeline graph. 
     In one embodiment the query term is a first one of a plurality of query terms and the matrix of relationships is a first one of a plurality of matrices of relationships. The process further includes: computing an originality estimate value α for each word in the student writing found in one or more of the plurality of matrices and computing a point of originality score for the writing using each of the computed originality estimate values. 
     In one embodiment, the writing is a student writing and the process is repeated for each of a plurality of writings by the same student. 
     In accordance with a further aspect of the concepts, systems and techniques described herein, a method for the objective evaluation of a student writing, includes identifying original content in the student writing and computing an originality score based upon one or more words in the student writing and lexical relationships between each of the one or more words in the student writing and one or more selected query terms. 
     In one embodiment, identifying original content in the student writing comprises isolating one or more points of originality in the student writing. In one embodiment, isolating one or more points of originality in the student writing is accomplished by receiving a first query term generating a first matrix of relationships between lexical terms based upon the first query term, searching the student writing for words which appear in the matrix and computing a first originality score based upon the lexical relationships between the words in the student writing which also appear in the first matrix. 
     In one embodiment, computing an originality score is done by computing an originality estimate α according to the equation: α=δ×0.7×t and wherein δ corresponds to a value representing a distance between the word in the student writing found in the matrix and the query term; and t corresponds to a value representing a word type. 
     In one embodiment, the first query term is a first one of a plurality of query terms and the first matrix of relationships is a first one of a plurality of matrices of relationships and an originality estimate α is computed for each word in the student writing found in one or more of the plurality of matrices and a point of originality for the student writing is computed using each of the computed originality estimates. 
     In one embodiment, computing a point of originality for the student writing using each of the computed originality estimates is accomplished by summing each of the originality estimates and the point of originality for the student writing corresponds to the sum of the originality estimates. The point of originality can then be visualized. on a timeline graph. 
     In accordance with a still further aspect of the concepts, systems and techniques described herein, a system includes means for constructing a first matrix of relationships between lexical terms generated from a first query term and means for searching a writing for terms which appear in the first matrix and for computing an originality estimate value for each of the terms in the writing which match one or more terms in the first matrix. 
     With this particular arrangement, a system for evaluating a point of originality in a writing is provided. In one embodiment, the originality estimate value is based upon the lexical relationships between the terms in the writing and the terms in the first matrix. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The foregoing features of the concepts, systems, circuits and techniques described herein may be more fully understood from the following description of the drawings in which: 
         FIG. 1  is a block diagram of an exemplary system for detecting a point of originality in a writing; 
         FIG. 1A  is a graphical representation of a portion of information stored in a matrix of lexical terms; 
         FIG. 2  is a model synset tree (by hyponym relation); and 
         FIGS. 3 and 3A  are series of flow diagrams which illustrate an exemplary process for detecting a point of originality in a writing; 
         FIG. 4  is an example of a visualization of the results of detecting a point of originality in a writing. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Referring now to  FIG. 1 , a system  10  for objectively determining a point of originality in a writing includes an input system  12  through which a user (e.g. an instructor, an educator, a faculty member, researcher or a student) submits a writing to a point of originality processor  14  and more particularly to a writing sample interface  16 . In one embodiment, system  10  processes writings submitted in a particular format. In such embodiments, when a writing is submitted in a format not compatible with the desired system format, a message (e.g. an error message) is provided to the user. Such a message may be provided by input system  12  or interface  16  or by some other portion of processor  14 . In other embodiments, however, when a writing is submitted in a format not compatible with the desired system format, system  10  converts or otherwise places the document in a format accepted by the processing system. For example, in response to an electronic document submitted through input system  12  not being in a format accepted by processor  14 , processor  14  (e.g. via writing sample interface  16 ) places the document into a format accepted by processor  14 . 
     Input system  12  also allows a user to submit one or more query terms to point of originality processor  14  and more particularly to a query interface  18 . I n some embodiments, a user manually selects the query terms while in other embodiments, selection of the query terms is automated. Query interface  18  receives each query term provided thereto and provides each of the query terms to a lexical database  20 . Lexical database  20  provides lexical terms back to point or originality processor  14 . As shown in the exemplary embodiment of  FIG. 1 , the terms as well as the term&#39;s relationship to the query term is stored in a matrix of lexical terms  22 . Although a single matrix is shown in the exemplary embodiment of  FIG. 1 , multiple matrices (e.g. one matrix for each query term) may also be used. Other forms of storing the terms as well as the term&#39;s relationship to the query term are also possible for a functional embodiment and will be apparent to one of ordinary skill in the art after reading the description provided herein. 
     Lexical database  20  arranges nouns, verbs, adjectives, and adverbs by their conceptual-semantic and lexical relationships. Whereas a simple thesaurus would be able to identify any two words as synonyms or antonyms of one another, lexical database  20  is able to note the similarity between two words that don&#39;t have literally identical meanings. These relationships are ideally meant to mirror the same lexical associations made by human cognition. In one exemplary embodiment, lexical database  20  may be provided as a lexical database of English referred to as WordNet made available through Princeton University at http://wordnet.princeton.edu/wordnet/license/. 
     WordNet&#39;s arrangement is hierarchical, which is to say that certain terms are more closely related than others. Within WordNet, these relationships are displayed as so-called “synsets,” clusters of terms that branch, like neurons or tree branches, from more specific to more and more diffuse associations (e.g. as illustrated in  FIGS. 1A and 2 ). If two words are found within one another&#39;s synset tree, it stands to reason that these terms are, in some way, related, be it closely or distantly. 
     As will be described in detail further below, point of originality processor  14  computes distances between two terms, and assigns a value commensurate with their degree of semantic relatedness. 
     Thus, in response to receiving a query term from query interface  18 , lexical database  20  provides one or more related words (be they closely or distantly related to the query term), to processor  14  which stores the terms (i.e. the set of synset matches found in any given writing) and their relationship to the query term in the matrix of lexical terms  22 . 
     In one embodiment, and referring briefly to  FIG. 1A , the matrix of lexical terms  22  may be graphically represented as one or more tree structures with each tree having the query term as a root node. That is, each query term input to the system (e.g. via input system  12  and/or otherwise provided to query interface  18 ) serves as a root node for a tree based upon that query term. In one exemplary embodiment, to generate a single “tree,” a query term such as query term  36  is provided to lexical database  20  which may return one or more additional terms (e.g. terms  38   a ,  38   b ) in response to query term  36 . Terms  38   a ,  38   b  are then, in turn, provided to lexical database  20  which again may provide one or more additional terms (e.g. terms  40   a - 40 N). 
     It should be noted that in the example of  FIG. 1A , while lexical database  20  returned terms  40   a - 40 N in response to receiving term  38   a , lexical database  20  did not return any additional terms in response to receiving term  38   b . Thus, the “tree” structure need not be symmetric. Also, it should be appreciated that a user may decide how many terms to return to lexical database  20  before ending the process of generating new “branches” (i.e. terms) in the tree. In some embodiments, it may be desirable to use a fixed value (i.e. return to lexical database  20  a fixed number of times). In one particular embodiment, a fixed value of six (6) is used. As discussed above, regardless of the number of terms  42   a - 42 R generated by lexical database  20 , each of the terms as well as the terms&#39; relationship to the query term is stored in the matrix of lexical terms  22  ( FIG. 1 ). 
     Returning now to  FIG. 1 , a matching processor  24 , receives one or more lexical terms from matrix of lexical terms  22  and searches the writing provided to writing interface  16  for terms which match either the query term(s) or the lexical term(s) generated from the query term(s). The results of the search (i.e. the matched words) are provided to a distance computation processor  26 . 
     When matches are found, a distance computation processor  26  performs a distance calculation between the original query term and the match within the student&#39;s work. In one embodiment, this is performed as follows: 
     Let β be a supplied query term (e.g. supplied by a user or automatically supplied). Then, let Q be a set containing all synset word matches from the lexical database for β. 
     Let W be a set of all words from a given student assignment and let S be a set of stop words, a list of common words in English usage (like “the” or “and”) to be omitted to speed up processing time. Then, M, the set of synset terms found in any given writing sample can be defined as:
 
 M =( Q−S )∩ W  
 
     As noted above, terms and relationships from lexical database  20  are stored in the matrix of lexical terms  22  and the terms (sometimes referred to as synset matches) may be graphically represented as a tree structure having the root node defined as β. Then, δ, the distance (depth) for any given synset (γ) in M from the root node ( query term) β may be defined as:
 
δ=0 if γ=β
 
δ=1 if γis first child of β
 
δ=2 if γ is second child of β
 
δ=N if γ is Nth child of β
 
     Lexical database  20  also supplies the type of each synset term. Thus, t, the “word type” of any given synset term in M, may be defined as:
 
T=1.00 if γ=β
 
T=0.90 if γ=synonym/antonym
 
T=0.85 if γ=hypernym/holonym
 
T=0.85 if γ=holonym/meronym
 
     Then, α, the weight for any given synset term in M in the general “point of originality” estimate is calculated as follows:
 
α=(δ×0.7)× t  
 
     In one embodiment, the depth for any given synset term, defined by δ is multiplied by a constant value of 0.7, which reflects the diminished associations between terms the farther separated two terms are along the synset tree. This value is selected because it corresponds with the calculation of distance between terms that yields the nearest match with human intuition. It should, of course, be appreciated that in some instances a value other than 0.7 may be used as the constant value. Similarly, it should be appreciated that the values of distances δ may be set as any zero or non-zero value and likewise the values for each “word type,” t, of any given synset term may be assigned any zero or non-zero value. 
     Then, P, the point of originality in a given student&#39;s writing for the query term β, can be defined as: 
     
       
         
           
             
               P 
               ⁡ 
               
                 ( 
                 β 
                 ) 
               
             
             = 
             
               
                 ∑ 
                 
                   n 
                   = 
                   0 
                 
                 
                    
                   M 
                    
                 
               
               ⁢ 
               
                   
               
               ⁢ 
               
                 α 
                 n 
               
             
           
         
       
     
     This same calculation may then performed for all of the writing samples by a given student or other user of the system. 
     Distance computation processor  26  provides the results to an output interface  28  which displays or otherwise makes the results available to a user. In one embodiment, output interface includes a visualization processor  30 . In one embodiment, once a point of originality is calculated, visualization processor  30  generate s a plot of all instances of originality. An exemplary plot corresponding to a horizontal timeline is shown and described below in conjunction with  FIG. 4 . 
     Referring now to  FIG. 2 , a hierarchical arrangement inherent to a lexical database  20  ( FIG. 1 ) such as WordNet is shown providing one method of determining the relationship between two terms. If the synset tree of one term encompasses another term, it is simple enough to note how many synset jumps it takes to move from one to another, As shown in the exemplary embodiment of  FIG. 2 , a “Dalmatian” is a type of “dog,” which itself belongs to the subcategory of “domestic animals;” thus, there are two tiers of associations between the concepts of “Dalmatian” and “domestic animals.” Unfortunately, however, just how closely any two terms might be related is not a purely linear relationship. Lexical database  20  ( FIG. 1 ) organizes related terms by their precise lexical entailment, such that nouns might be categorized as synonyms, hypernyms, hyponyms, holonyms and merony, as seen in Table 1. 
     
       
         
               
             
               
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 Possible Lexical Entailments in Lexical Database 
               
             
          
           
               
                   
                 TYPE 
                 MEANING 
                 EXAMPLE 
               
               
                   
                   
               
               
                   
                 Synonym 
                 X is a synonym of Y if X 
                 {smile, grin} 
               
               
                   
                   
                 means Y 
               
               
                   
                 Hypernym 
                 X is a hypernym of Y if 
                 {dog, mammal} 
               
               
                   
                   
                 every X is a kind of Y 
               
               
                   
                 Hyponym 
                 X is a hyponym of Y if 
                 {mammal, dog} 
               
               
                   
                   
                 every Y is a kind of X 
               
               
                   
                 Holonym 
                 X is a holonym of Y if Y is 
                 {hand, finger} 
               
               
                   
                   
                 part of X 
               
               
                   
                 Meronym 
                 X is a meronym of Y if X is 
                 {finger, hand} 
               
               
                   
                   
                 part of Y 
               
               
                   
                   
               
             
          
         
       
     
     These possible entailments provide a rudimentary roadmap f or all the ways in which two words might be related. Since lexical database  20  attempts to map the cognitive associations automatically formed between words, a student&#39;s evocation of the holonym or hypernym of a given noun instead of the noun itself is more likely to form an associative recast of the original term. Yet while this simple index displays just how any two terms might be related, all the possible relationships noted are not necessarily equal. Some relationships, like that between synonyms smile and grin, are bound to be more strongly associated than that between mammal and dog. 
     Thus, as described above, it is possible to install a series of weights that can best calculate the semantic distance between any two terms. This method in particular is useful because of all possible methods, it bears the highest correspondence between its own distance calculations and the intuitions of actual human respondents. 
       FIGS. 3 and 3A  are a series of flow diagrams showing the processing performed by a processing apparatus which may, for example, be provided as part of a system for objectively determining a point of originality in a writing such as that shown and described in  FIG. 1 . The rectangular elements (e.g. block  46  in  FIG. 3 ) in the flow diagrams of  FIGS. 3 and 3A  are herein denoted “processing blocks” and represent steps or instructions or groups of instructions. Some of the processing blocks can represent an empirical procedure or a database while others can represent computer software instructions or groups of instructions. The diamond shaped elements in the flow diagrams (e.g. block  50  in  FIG. 3 ) are herein denoted “decision blocks” and represent steps or instructions or groups of instructions which affect the processing of the processing blocks. Thus, some of the steps described in the flow diagram may be implemented via computer software while others may be implemented in a different manner e.g. via an empirical procedure. 
     Alternatively, some of the processing blocks can represent steps performed by functionally equivalent circuits such as a digital signal processor circuit or an application specific integrated circuit (ASIC) or a neural network. The flow diagram does not depict the syntax of any particular programming language. Rather, the flow diagram illustrates the functional information one of ordinary skill in the art requires to perform the steps or to fabricate circuits or to generate computer software to perform the processing required of the particular apparatus. It should be noted that where computer software can be used, many routine program elements, such as initialization of loop s and variables and the use of temporary variables are not shown. It will be appreciated by those of ordinary skill in the art that unless otherwise indicated herein, the particular sequence of steps described is illustrative only and can be varied without departing from the spirit of the concepts, techniques and systems described herein. 
     Turning now to  FIGS. 3 and 3A , a process to evaluate a writing through the identification of original content in the writing begins in processing block  44  in which a writing is submitted to a processing system. In some embodiments, the writing may be student writing (e.g. term paper, blog or other writing) which an instructor (e.g. high school teacher, college professors or other educator or another type of instructor) will evaluate using, at least in part, the system and techniques described herein. In one embodiment, submitting a writing to a processing system includes checking a format of an electronic document containing the writing to determine whether the electronic document is in a format accepted by the processing system. In response to the electronic document not being in a format accepted by the processing system, the system either provides notification of the incompatibility (in which case processing ends) or places the document in a format accepted by the processing system. 
     Processing then proceeds to processing block  45  in which a list of one or more words or phrases, i.e. groups of words, (collectively, terms) in the writing are identified. As apparent from the description herein, the terms identified are used to perform an objective evaluation of the writing. 
     Processing then proceeds to processing block  46  in which one or more query terms are selected. In one embodiment, selection of one or more query terms is automated. In other embodiments, a user (e.g. an instructor or even a student) manually selects one or more query terms. In still other embodiments, a combination of automated and manual selection may be used. 
     Once the one or more query terms are selected, the process continues by constructing a first matrix of relationships between lexical terms generated for each of the one or more query terms as shown in processing block  47 . This may be accomplished, for example, with the use of a lexical database (e.g. by providing each query term to a lexical database such as WordNet and receiving back from the lexical database lexical terms which are related to the query term and forming the matrix of relationships based upon the lexical terms provided by the lexical database). The first matrix of relationships includes both the terms and the relationship of the terms (e.g. homonym, synonym, etc . . . ) to the query term. 
     Processing then proceeds to processing block  48  in which terms in the writing (i.e. the list of terms generated in processing block  45 ) are compared with terms in the lexical relationship matrix generated in processing block  47 . That is, a searching of the writing is performed to identify terms which appear in the lexical relationship matrix. 
     Processing then proceeds to decision block  50  in which it is determined whether a match is found between a term in the writing and a term in the lexical relationship matrix. If no match is found, then processing proceeds to decision block  56  and if no other query terms remain, then processing proceeds to processing blocks  58 ,  60  in which the point of originality values are summed (e.g. as described above in conjunction with  FIG. 1 ) and an output is provided. In one embodiment, as will be shown below in conjunction with  FIG. 4 , the output is provided by presenting the point of originality value on a timeline graph. 
     If in decision block  56  a decision is made that more query terms remain to be processed, then processing flows to processing block  61  where anther query term is selected and then processing loops back to processing block  47  and the processing describe in blocks  47 - 56  are repeated until all query terms are processed. 
     It should be noted that in processing block  58  the process computes an originality estimate value for each of the terms in the writing which match one or more terms in the matrix. Typically there will be a plurality of originality estimate values which are summed to produce a “point of originality” value for the writing. If in decision, block  50  it is determined that a match is found between a term in the writing and a term in the lexical relationship matrix, then processing proceeds to processing block  52  where a distance calculation between the writing term and the lexical relationship term found in the matrix is performed and then processing proceeds to processing block  54  in which a point of originality value is computed. Processing then proceeds to blocks  56 - 62  as described above. 
     As noted above in conjunction with  FIG. 1 , the originality estimate values are based upon the lexical relationships between the terms in the writing and the terms in the lexical relationship matrix. As, also discussed above in conjunction with  FIG. 1 , in one embodiment, computing the originality estimate value corresponds to computing an originality estimate value α according to the equation:
 
α=δ×0.7 ×t  
 
in which
 
     δ corresponds to a value representing a distance between the word in the writing found in the matrix and the query term; and 
     t corresponds to a value representing a word type as defined above. 
     It should be appreciated that the process of  FIGS. 3 and 3A  can be repeated for each of a plurality of writings by the same student. Then, all originality evaluations of the student&#39;s writings may be displayed on a timeline graph. 
     It is recognized that it is important to ensure the accuracy of results. Although possible query terms for analysis are literally endless, single-word queries have a higher likelihood of accidentally triggering false positives that might not indicate originality, but simple misuse; if an instructor sought to determine student activity relating to feline vision, a writing sample with frequent reference to Dalmatians might be original or simply irrelevant. In order to mitigate the likelihood of erroneous returns, it is possible, and indeed advisable, to use compound phrases, or to otherwise combine query terms that still correspond with the key concepts of a given course. 
     For example, in the fifth week of a course on the Internet and Society, taught in the fall of 2008 in the Department of Computer Science at Brandeis University, students were given a specific essay prompt that asked them to address the concept of “innovation” as it related to constraints of information “layers,” “resources,” and “control.” While students had uniformly discussed the concept of “innovation” in their earlier work, it is possible to restrict the proportional weight of the P values across writing samples by searching for all of the relevant concepts simultaneously. Thus writings demonstrating high P values would not simply be those with frequent synset matches for “innovation,” but for the additional concepts being tested. 
     In the earlier hypothetical then, rather than simply searching for “cat” or “feline,” the instructor might literally search for “feline vision.” Having done so, a post extraneously mentioning “Dalmations” would not be weighted as highly as one more appropriately discussing “color blindness in dogs.” 
     Referring now to  FIG. 4 , a technique for visualization of the point of originality (e.g. as may be shown on a display for example) includes a timeline visualization comprising a horizontal timeline that represents a time interval for a writing activity. For example, the writing activity of a student for the duration of a particular semester. Such a horizontal timeline can enhance, or in some cases even optimize, instructor comprehension. 
     Reference numeral  62  (also denoted with numeral  1 ) corresponds to a field in which a query term (β) is input(by an instructor, for example). 
     Reference numeral  64  (also denoted with numeral  2 ) corresponds to a drop-down menu which allows the instructor to select which student&#39;s writing samples are currently being displayed. 
     Reference numeral  66  (also denotes with numeral  3 ) corresponds to a timeline on which is associated date(s) and/or time(s) of each of a student&#39;s writing samples. By default, all markers share the same default color, a monochrome black. Writing samples are then color-coded, from colder to warmer colors along the ROYGBIV spectrum, the higher the value of the point of originality (P) score for any given writing sample, These color assignments present an intuitive way for the instructor to quickly recognize that the sample has been assigned a higher originality value. 
     Reference numeral  68  denotes a window which displays the student&#39;s writing samples in excerpted form, which according to one embodiment may be a list, with the matches between the query terms and the synset terms found within the writing sample (M) highlighted in the same color as that sample&#39;s marker color. The colored marker itself is displayed as the sample&#39;s bullet point. By default, this window is pre-sorted, from highest P value to lowest. 
     Reference numeral  70  denotes another window. If a writing sample marker is selected, either in the timeline window  66  or in the window which displays the student&#39;s writing samples in excerpted form  68 , the text of that writing sample is displayed here, again, with the matched synset terms (M) highlighted. This assortment of visualization options allows the point of originality calculation to be displayed in a number of intuitive ways: as a window which displays the student&#39;s writing samples in excerpted form  68 , which according to one embodiment may be a list, within a timeline  66 , and in context  70 . 
     Having described preferred embodiments of the concepts, systems, circuits and techniques described herein, it will now become apparent to those of ordinary skill in the art that other embodiments incorporating these concepts may be used. F or example, it should now be appreciated that one can apply the topologies described herein to rectifier systems (e.g. for grid-connected power supplies) as well and for bidirectional power flow converter systems. Accordingly, it is submitted that that the concepts, systems, circuits and techniques described herein, should not be limited to the described embodiments but rather should be limited only by the spirit and scope of the appended claims.