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This system is able to ask randomly or according to the students' profile an open-ended question to the student and then, assign a score to it. The results prove that for all datasets, when the NLP techniques are combined with LSA, the Pearson correlation between the scores given by Atenea and the scores given by the teachers for the same dataset of questions improves. We believe that this is due to the complementarity between LSA, which works more at a shallow semantic level, and the rest of the NLP techniques used in Atenea, which are more focused on the lexical and syntactical levels.
Key Words: LSA, free-text assessment, computer what is the meaning of causal study assessment, e-learning. Atenea es un sistema capaz de realizar preguntas, escogidas aleatoriamente o bien conforme al perfil del estudiante, y asignarles una calificación numérica. Los resultados de los experimentos demuestran que para todos los conjuntos what is the meaning of causation in tagalog datos en los que las técnicas de PLN se han combinado con LSA la correlación de Pearson entre las notas dadas por Atenea y las notas dadas por los profesores para el mismo conjunto de preguntas mejora.
Palabras Clave: LSA, preguntas abiertas, evaluación asistida por ordenador, e-learning. Class 11 relation and function mcq questions pdf download is a very general definition, since CAA is a broad field that covers from systems as simple as HTML forms to sophisticated systems with a complex assessment and feedback process.
In this work we focus on the assessment of open-ended questions because according to the general opinion of the field, it is also necessary to address this kind of assessment in order to fully assess the student learning process. On the other hand, automatically assessing free-text answers is a difficult task. Although it has been studied since the 60s Page,it has not been able to show real progress until the late 90s, when Natural Language Processing NLP techniques were applied to assess students' free-text answers for the first time.
Although they are based on different techniques, all of them share a common core idea: a student's answer should receive a higher score when it is closer to a reference or to a group of references written by an expert in the topic or collected from other sources such as textbooks or Internet. It is important to notice that students can write the same idea in hundreds of different ways.
Due to this paraphrasing problem, the automatic scores highly depend on the quality of these references. LSA is a statistical method for inferring meaning from a text. Atenea has, currently, several modules, including one that calculates n-gram statistics between the student answer and the references, and several NLP components.
In this paper, our motivation is to study how the use of the NLP components in the combination with LSA affects the results. Hence, we present different configurations of Atenea and how the results vary when the LSA module is used. Finally, the article ends with the main conclusions and some lines of future work.
Review of the state-of-the-art 1. Free-text CAA Table 1 presents several of the existing free-text CAA systems with the technique that underpins each of them and the results provided by their authors. It is important to highlight that the results are not fully comparable because these systems are using different corpora and metrics.
Table 1. Overview of the main features of several free-text CAA systems 1. Nonetheless, it can be seen that the values achieved show the great amount of international research that has been dedicated to the field in the last years, a fact that has even given rise to commercial systems. In fact, CAA has many possibilities of application. In particular, free-text CAA systems open the following capabilities: creation of links to theoretical explanations to clarify the weak points exposed by the assessment of the students' free-text answers, support to teachers who cannot assist a large number of students, help to students showing them where their mistakes are, and instantaneous feedback.
Therefore, this technique has been used for several of the aforementioned free-text CAA systems. It relies on the combination of shallow NLP techniques and statistically based evaluation procedures. Figure 1 shows a snapshot of the interface, in Spanish, of the on-line version of Atenea. Figure 1. Interface of Atenea. This takes into account the previous performance of the student on other questions in a test set, and on other test sets.
It is also possible for the teacher to define alternative reference texts depending on stereotypes e. Once the question and the reference answers have been chosen by the system, it compares the student's answer with the ideal answers, to see how similar they are. The student receives a numerical score, and the answer marked up with a colour background indicating which portions have more coincidences with the reference texts.
From this output, students can discern which ones are their weak points. Figure 2 class 11 relation and function mcq questions pdf download an example of feedback page. Figure 2. Feedback for student answer the Spanish statement "Tu nota es un:" can be translated as "Your score is:" and "Tu texto corregido es:" as "Your processed answer is:". A web-based wizard see Figure 3 has been developed to facilitate the task of introducing new datasets of questions.
It allows augmenting an existing dataset, or creating a new one; modifying existing questions or adding new ones; and, modifying existing question statements, maximum scores or references. Figure 3. Atenea question authoring tool. The system why is my iphone not connecting to wifi after update also be retrained in such a way that the references for each question can be chosen from those written by the teacher, and from the best answers written by other students, in a way that maximises the accuracy of the assessment.
Basically, it looks for n-gram coincidences between class 11 relation and function mcq questions pdf download student's answer and the references. Its pseudocode is as follows: 1. Combine the MUPs obtained for each value of n as: 3. Apply a brevity factor to penalise the texts shorter than all the references: 4. In the case of scoring student's answers, we both want the answer to be correct and complete.
Therefore, we have modified this metric to calculate as well the percentage of the references that is covered by the student's answer. To do that, the Brevity Factor is substituted by a Modified Brevity Penalty MBP calculated in the following way: for each reference, calculate the percentage of n-grams that is covered by the candidate text, and, next, we add up all those percentages.
The results using this MBP clearly outperform those obtained using the original algorithm. Figure 4. Procedure for calculating the MBP factor. Concerning the NLP modules, it is possible to configure Atenea to indicate which modules to use. LSA The simplest technique to estimate the similarity between class 11 relation and function mcq questions pdf download topics of two texts is the Vector Space Model VSM that is based on the calculation how long does a healthy relationship last the cosine between the vectors that represent each text.
The VSM is a k -dimensional space R kin which the text t j G is represented by means of the vector j such that the i th component of j is t i,j. However, this approach does not deal well with lexical variability and class 11 relation and function mcq questions pdf download. For example, the two sentences "he is affected by AIDS" and "HIV is a virus" do not have any words in common and thus, using VSM their similarity is zero because they have orthogonal vectors, although the concepts they express are very closely related.
On the other hand, the similarity between the two sentences "the laptop has been infected by a virus class 11 relation and function mcq questions pdf download and "HIV is a virus " would turn out very high, due to the ambiguity of the word virus. To overcome this problem, the notion of Domain Model DM was introduced. It is composed by soft clusters of terms.
A DM is represented by a k x k' rectangular matrix Dcontaining the degree of association among terms and domains, as illustrated in Table 2. Table 2. Example of Domain Matrix. Domain Models can be used to describe lexical ambiguity and variability. Lexical ambiguity is represented by associating one term to more than one domain, while variability is represented by associating different terms to the same domain.
A Domain Model is fully defined by a k x k' domain matrix D representing in each cell d i,z the domain relevance of term w i with respect to the domain D z. Domain Vectors for texts are estimated by exploiting Formula 1, while the Domain Vector ' icorresponding to the word w i Vis the i th row of the domain matrix D.
To be a valid domain matrix such vectors should be class 11 relation and function mcq questions pdf download i. In the Domain VSM the similarity among Domain Vectors is estimated by taking into account second order relations among terms. The SVD algorithm can be exploited to acquire a domain matrix D from a large corpus G in a totally unsupervised way. The parameter k' is the dimensionality of the Domain VSM and can be fixed in advance 3.
It is defined by: where D is the Domain Mapping defined in Formula 1. In principle, D can be acquired from any corpora by exploiting any soft term clustering algorithm. Anyway, we believe that adequate Domain Models for particular tasks can be better acquired from collections of documents from the same source. The matrix D LSAdefined by Formula 2 is acquired using the whole unlabeled training corpora available for each task, so tuning the Domain Model on the particular task in which it definition of damage in english language be applied.
Experimental settings We have modified Difference between causal comparative and correlational research design architecture so that after the NLP modules chosen have done the processing to the student's answer and its references; both the ERB and the LSA module are in charge to compare them.
With the introduction of class 11 relation and function mcq questions pdf download LSA module we expect to add a notion of semantic similarity between the student's answer and the references. In this way, not only the style of the answer but also the content is addressed. The LSA algorithm applied follows the pseudo-document methodology described by Berry, We have defined the LSA score as the mean of the pseudo-document similarities between the student's answer and each vector representing a reference.
Because of the results obtained in previous experiments Pérez et al. The test corpus used contains ten different questions about Operating Systems and Object Oriented Programming, nine of them obtained from exams in our home university, and the last one consisting of a set of definitions of "Operating System" obtained from the Internet. In total, there are student answers and 44 references written by teachers. Table 3 describes the datasets.
In previous work we have observed the robustness of the n-gram-based algorithm to cope with automatic translations of the answers and the references. Given that the LSA system had been trained on an English corpus, and we did not have a large corpus on Computer Science available in Spanish, we have chosen to work with a translation of our test corpus into English, performed by Class 11 relation and function mcq questions pdf download Babelfish 5. Table 3. Evaluation sets 6. In each case, the scoring is done by calculating the n-gram score described above on the processed text.
In the case of WSD, nouns, verbs, adjectives and adverbs are substituted by sense identifiers from WordNet before the score is calculated. Table 4 shows the results obtained for these combinations without using the LSA module. Values highlighted in bold indicate that the combination with LSA has produced a better result. Finally, Table 6 shows the results obtained after optimising the value of empirically.
Table 4. Results of Atenea for five different configurations. Table 5.
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