The Case for Dynamic Models of Learners' Ontologies in Physics

In a series of well-known papers, Chi and Slotta (Chi, 1992; Chi & Slotta, 1993; Chi, Slotta & de Leeuw, 1994; Slotta, Chi & Joram, 1995; Chi, 2005; Slotta & Chi, 2006) have contended that a reason for students' difficulties in learning physics is that they think about concepts as things rather than as processes, and that there is a significant barrier between these two ontological categories. We contest this view, arguing that expert and novice reasoning often and productively traverses ontological categories. We cite examples from everyday, classroom, and professional contexts to illustrate this. We agree with Chi and Slotta that instruction should attend to learners' ontologies; but we find these ontologies are better understood as dynamic and context-dependent, rather than as static constraints. To promote one ontological description in physics instruction, as suggested by Slotta and Chi, could undermine novices' access to productive cognitive resources they bring to their studies and inhibit their transition to the dynamic ontological flexibility required of experts.Comment: The Journal of the Learning Sciences (In Press

A study of feature exraction techniques for classifying topics and sentiments from news posts

Recently, many news channels have their own Facebook pages in which news posts have been released in a daily basis. Consequently, these news posts contain temporal opinions about social events that may change over time due to external factors as well as may use as a monitor to the significant events happened around the world. As a result, many text mining researches have been conducted in the area of Temporal Sentiment Analysis, which one of its most challenging tasks is to detect and extract the key features from news posts that arrive continuously overtime. However, extracting these features is a challenging task due to post’s complex properties, also posts about a specific topic may grow or vanish overtime leading in producing imbalanced datasets. Thus, this study has developed a comparative analysis on feature extraction Techniques which has examined various feature extraction techniques (TF-IDF, TF, BTO, IG, Chi-square) with three different n-gram features (Unigram, Bigram, Trigram), and using SVM as a classifier. The aim of this study is to discover the optimal Feature Extraction Technique (FET) that could achieve optimum accuracy results for both topic and sentiment classification. Accordingly, this analysis is conducted on three news channels’ datasets. The experimental results for topic classification have shown that Chi-square with unigram have proven to be the best FET compared to other techniques. Furthermore, to overcome the problem of imbalanced data, this study has combined the best FET with OverSampling technology. The evaluation results have shown an improvement in classifier’s performance and has achieved a higher accuracy at 93.37%, 92.89%, and 91.92 for BBC, Al-Arabiya, and Al-Jazeera, respectively, compared to what have been obtained on original datasets. Similarly, same combination (Chi-square+Unigram) has been used for sentiment classification and obtained accuracies at rates of 81.87%, 70.01%, 77.36%. However, testing the recognized optimal FET on unseen randomly selected news posts has shown a relatively very low accuracies for both topic and sentiment classification due to the changes of topics and sentiments over time

A New Feature Selection Method based on Intuitionistic Fuzzy Entropy to Categorize Text Documents

Selection of highly discriminative feature in text document plays a major challenging role in categorization. Feature selection is an important task that involves dimensionality reduction of feature matrix, which in turn enhances the performance of categorization. This article presents a new feature selection method based on Intuitionistic Fuzzy Entropy (IFE) for Text Categorization. Firstly, Intuitionistic Fuzzy C-Means (IFCM) clustering method is employed to compute the intuitionistic membership values. The computed intuitionistic membership values are used to estimate intuitionistic fuzzy entropy via Match degree. Further, features with lower entropy values are selected to categorize the text documents. To find the efficacy of the proposed method, experiments are conducted on three standard benchmark datasets using three classifiers. F-measure is used to assess the performance of the classifiers. The proposed method shows impressive results as compared to other well known feature selection methods. Moreover, Intuitionistic Fuzzy Set (IFS) property addresses the uncertainty limitations of traditional fuzzy set

KERT: Automatic Extraction and Ranking of Topical Keyphrases from Content-Representative Document Titles

We introduce KERT (Keyphrase Extraction and Ranking by Topic), a framework for topical keyphrase generation and ranking. By shifting from the unigram-centric traditional methods of unsupervised keyphrase extraction to a phrase-centric approach, we are able to directly compare and rank phrases of different lengths. We construct a topical keyphrase ranking function which implements the four criteria that represent high quality topical keyphrases (coverage, purity, phraseness, and completeness). The effectiveness of our approach is demonstrated on two collections of content-representative titles in the domains of Computer Science and Physics.Comment: 9 page

How One Writes, Makes, Markets a Movie and How an Audience Reads the Movie: Two Biographical Films of Hitler as a Case Study

According to John Lukacs, German people\u27s views on Hitler and Nazism once got examined right after the fall the Third Reich in the 1950s but this subject has lost its appeal since then. How do Germans nowadays, specifically those young ones raised in the New Germany after the fall of the Berlin Wall, think of Hitler and their country\u27s Nazi legacy? This dissertation is to explore how six young Germans growing up in the new unified Germany interpret two films\u27 representations of Hitler and Nazism

An empirical study on CO2 emissions in ASEAN countries

This paper proposes a local feature selection (FS) measure namely, Categorical Descriptor Term (CTD) for text categorization. It is derived based on classic term weighting scheme, TFIDF. The method explicitly chooses feature set for each category by only selecting set of terms from relevant category. Although past literatures have suggested that the use of features from irrelevant categories can improve the measure of text categorization, we believe that by incorporating only relevant feature can be highly effective. The experimental comparison is carried out between CTD and five wellknown feature selection measures: Information Gain, Chi-Square, Correlation Coefficient, Odd Ratio and GSS Coefficient. The results also show that our proposed method can perform comparatively well with other FS measures, especially on collection with highly overlapped topics

An empirical study of feature selection for text categorization based on term weightage

This paper proposes a local feature selection (FS) measure namely, Categorical Descriptor Term (CTD) for text categorization. It is derived based on classic term weighting scheme, TFIDF. The method explicitly chooses feature set for each category by only selecting set of terms from relevant category. Although past literatures have suggested that the use of features from irrelevant categories can improve the measure of text categorization, we believe that by incorporating only relevant feature can be highly effective. The experimental comparison is carried out between CTD and five wellknown feature selection measures: Information Gain, Chi-Square, Correlation Coefficient, Odd Ratio and GSS Coefficient. The results also show that our proposed method can perform comparatively well with other FS measures, especially on collection with highly overlapped topics