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Curricula Di Studi: Modelli E Verifica Di Competenze

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Curricula Di Studi: Modelli E Verifica Di Competenze UNIVERSITA` DEGLI STUDI DI TORINO FACOLTA` DI SCIENZE MATEMATICHE, FISICHE E NATURALI DIPARTIMENTO DI INFORMATICA TESI DI LAUREA Curricula di Studi: Modelli e Verifica di Competenze Relatore Controrelatore Prof. Matteo Baldoni Prof. Alberto Martelli Candidata Elisa Marengo A.A 2007-2008 Indice I Stato dell’arte 9 1 Il ruolo dei Learning Objects nel Semantic Web 10 1.1 Learning Object e l’importante tema del “riuso” . 11 1.2 “Personalizzazione” nel Semantic Web . 16 1.2.1 Adaptive Hypermedia Systems .............. 17 2 Course Sequencing per la composizione di corsi 21 2.1 DCG: Dynamic Course Generation System . 24 2.2 CoCoA: Concept-based Courseware Analysis . 25 2.3 DCG e CoCoA: osservazioni e conclusioni . 28 2.4 Dynamic Assembly per la generazione di corsi . 29 3 Il curriculum planning problem come problema CSP 32 3.1 MI-CSP: mixed-initiative constraint satisfaction problem . 33 3.2 Vincoli gestiti dal sistema . 34 3.3 Il sistema . 35 3.4 Formalizzazione del problema . 36 3.5 Metodi per la ricerca . 37 3.6 Il problema della simmetria . 38 3.7 Curricula accademici bilanciati . 39 3.7.1 Parametri da considerare . 40 3.7.2 Modello matematico del problema . 41 2 II DCML: Declarative Curricula Model Language 44 4 Il sistema Wlog per la generazione di curricula 45 4.1 Il paradigma ad azioni per la rappresentazione dei corsi . 47 4.2 Il linguaggio di programmazione Dynamic in LOGic . 49 4.3 Costruzione di curricula personalizzati mediante procedural planning . 50 4.4 Validazione di curricula per mezzo del temporal projection reasoning . 52 4.5 Il Procedural planning e i problemi legati alla sua natura prescrittiva . 52 5 Definizione del curricula model 58 5.1 La logica LTL in breve . 59 5.1.1 Sintassi della logica LTL . 59 5.1.2 Semantica della logica LTL . 60 5.2 Il linguaggio grafico DCML . 64 5.3 Rappresentare competenze e vincoli base in DCML . 66 5.3.1 Rappresentazione delle competenze . 66 5.3.2 Esprimere competenze iniziali . 68 5.3.3 Vincoli sul livello delle conoscenze . 69 5.3.4 Esprimere vincoli su pi`uconoscenze . 69 5.4 Vincoli temporali e relazioni tra le competenze . 73 5.4.1 Before . 74 5.4.2 Implication . 80 5.4.3 Succession . 86 5.5 Caso di studio: esempio di curricula model . 92 III Rappresentazione e Verifica dei curricula di studio 96 6 Rappresentazione di curricula di studio 97 6.1 Caratteristiche di una rappresentazione grafica adeguata . 99 6.2 Rappresentazione dei curricula di studi mediante activity di- agram: una prima proposta . 99 3 6.3 Una scelta adeguata per gli istanti di tempo . 101 6.3.1 Diagrammi di Gantt . 104 6.4 Rappresentazione dei piani di studio mediante activity dia- gram: utilizzo di swimlanes e milestones . 106 6.4.1 Caso di studio . 108 7 Verifica dei curricula di studio mediante il model checker SPIN 111 7.1 Model checking in breve . 113 7.1.1 Model checking di formule LTL . 116 7.2 Il model checker SPIN . 117 7.3 Rappresentazione delle competenze e dei corsi nel linguaggio PROMELA . 119 7.4 Traduzione delle milestones che compongono i curricula . 123 7.4.1 Controllo delle precondizioni . 123 7.4.2 Applicazione degli effetti . 131 7.5 Verifica dei piani di studio . 133 7.5.1 Verifica dell’assenza di competency gaps . 134 7.5.2 Raggiungimento del learning goal . 136 7.5.3 Conformit`adel piano rispetto al curricula model . 137 7.6 Caso di studio . 138 7.7 Importanza della traduzione automatica di curricula di studi e di diagrammi DCML . 144 8 Considerazioni finali 146 8.1 Il Personal Reader Framework . 147 8.2 Upper ontologies per la definizione di un “vocabolario” comune149 Bibliografia 151 A Articoli correlati 158 4 Introduzione Il World Wide Web ha indubbiamente cambiato il modo in cui le persone utilizzano il computer. Inizialmente il loro ruolo era quello di sostituire l’uo- mo in lunghe e noiose operazioni e possederne uno era privilegio di pochi. Oggi, quasi in ogni casa, se ne pu`otrovare uno che, nella maggior parte dei casi, viene usato per comunicare con altre persone, per fare acquisiti on-line o per la ricerca di informazioni sul Web. Quest’ultimo aspetto ha causato la rapida crescita del numero e del tipo di risorse fruibili, ma mentre il Web “di ieri” era un universo di informazione accessibile via rete fatto dalle persone per le persone, gi`ada alcuni anni ci si sta muovendo verso un nuovo Web in cui le macchine sono in grado di operare sempre di pi`uin modo automatico. A tal proposito, si sente spesso parlare di Semantic Web, il cui obiettivo `eautomatizzare in buona parte l’elaborazione delle informazioni. Non si tratta, quindi, di qualcosa di diverso, separato o aggiunto a posteriori al Web “di ieri”. Gli esperti preferiscono definirlo come l’efficace completamento e la naturale conseguenza [23]. Pi`uconcretamente il Semantic Web si propone di strutturare il contenuto delle pagine Web, creando le condizioni per cui agenti e programmi software possano utilizzarle e portare a termine task per conto degli utenti. Tim Berners-Lee propone la seguente definizione: The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in coop- eration. In quest’ottica le applicazioni che vengono sviluppate si propongono di utilizzare meccanismi automatici di ragionamento orientati all’utente, che permettano, cio`e,di specificare quello che si sta cercando in modo sufficien- 5 temente preciso da consentire il risparmio di tempo con la certezza di trovare tutte le possibili e migliori alternative. Tutti questi aspetti si adattano benissimo al particolare dominio dell’e- learning, che rappresenta l’ambito di riferimento di questa tesi. Esso si occu- pa dell’“apprendimento centrato sull’utente, che coinvolge elaboratori e reti di comunicazione”. Un notevole impulso in questa direzione si ha intorno al 1999 quando l’apprendimento a distanza via Web viene adottato dalle aziende, consentendo loro di abbattere i costi per la formazione del perso- nale e facilitando le attivit`adi aggiornamento dei contenuti che, prima di allora, venivano distribuiti in modo cartaceo o su CD-ROM. Lo scopo di questa tesi `equello di fornire uno strumento per la persona- lizzazione e la verifica di curricula di studi. Pi`uprecisamente si discuter`aun approccio per la definizione e per il controllo dei vincoli che un certo piano di studi deve rispettare. Questi verranno espressi in termini di conoscenze acquisite. In particolare, si tratta di controllare che un piano non presenti competency gaps (conoscenze richieste in ingresso ad un corso che lo stu- dente non possiede), che permetta all’utente di raggiungere il suo learning goal e che le relazioni e i vincoli temporali imposti sull’acquisizione delle competenze, definiti nel curricula model, siano rispettati. A tal proposito, verranno descritti un linguaggio grafico basato sulla lo- gica LTL (Linear Temporal Logic), per la definizione di constraints tra le competenze ed una rappresentazione dei curricula per mezzo di activity dia- gram UML. La tecnica adottata per realizzare la verifica, prevede l’utilizzo del model cheker SPIN. Pertanto, il piano di studi verr`atradotto in un pro- gramma PROMELA e su di esso verranno verificate le varie formule LTL, rappresentanti le propriet`ad’interesse. Il presente lavoro si articola in otto capitoli organizzati in tre parti: Parte I: descrive una panoramica sullo stato dell’arte. Nel primo capitolo si sottolinea l’importanza di riutilizzare risorse, learning objects nello specifico, in ambienti aperti e dinamici come il Semantic Web. Tuttavia, a questo aspetto deve essere affiancato quello della personalizzazione: l’utente deve essere messo in condizione di 6 esprimere quello che desidera ottenere e, nel caso sia possibile, deve essere accontentato. Il secondo capitolo tratta la composizione dei learning object per la definizione automatica di corsi. In queste proposte emergono alcuni vincoli che devono essere rispettati, quali il soddisfacimento dei prere- quisiti di un certo corso, o la valutazione dell’effettivo apprendimento dello studente. Nel terzo capitolo si presenta un possibile approccio per la verifica di vincoli di fruizione. Si tratta di condizioni legate alla struttura di un curricula di studi, pi`uche al contenuto. Essi si riferiscono, ad esempio, al numero di crediti acquisito, al numero di corsi, al tipo e al numero di corsi per ogni periodo, etc. Parte II: affronta il problema di come descrivere vincoli sulle competenze e di quali relazioni tra queste abbia senso definire. Nel quarto capitolo si presenta il paradigma ad azioni per la rappre- sentazione dei corsi. In quest’ottica, essi vengono visti come azioni descritte in termini di precondizioni ed effetti. Su questa idea si basa il sistema Wlog [9]. Esso si occupa della pianificazione di curricula di stu- di, utilizzando il procedural planning e presentata una soluzione per la verifica dei piani, mediante tecniche di temporal projection reasoning. La soluzione dei problemi riscontrati in tale approccio, rappresentano l’ambizioso obiettivo di questa tesi. Nel quinto capitolo si definisce DCML, un linguaggio grafico per la rappresentazione di vincoli e relazioni tra le competenze. L’insieme di tali condizioni definisce il curricula model, cio`el’insieme di propriet`a che un curricula deve rispettare per definirsi corretto. Ad ogni vincolo esprimibile graficamente `eassociata una traduzione in formule logiche LTL. Parte III: presenta una soluzione per la rappresentazione e la traduzione di curricula di studi. Il sesto capitolo si occupa di definire come un curricula di studi pos- 7 sa essere “disegnato”, in modo semplice e intuitivo, per mezzo degli activity diagrams UML. Il settimo capitolo affronta il problema della traduzione dei curricula, dalla rappresentazione grafica al linguaggio PROMELA.
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