Free Model of Sentence Classifier for Automatic Extraction of Topic Sentences

This  research  employs  free  model  that  uses  only  sentential  features without paragraph context  to extract topic sentences of a paragraph. For finding optimal  combination  of  features,  corpus-based  classification  is  used  for constructing a sentence classifier  as the model.  The sentence classifier is trained by  using Support Vector Machine  (SVM).  The experiment shows that position and meta-discourse features are more important  than syntactic features  to extract topic  sentence,  and  the  best  performer  (80.68%)  is  SVM  classifier  with  all features.

A web-based model to enhance competency in the interconnection of multiple levels of representation for pre-service teachers

This study aimed to design a web-based learning model to enhance pre-service teachers’ competencies in the Interconnection of Multiple Levels of Representation (IMLR). The model contains multimodal representations with assignments and probing questions; it creates social engagement through online discussion forums and online assessment as feedback on learning performance. The validity of the model was evaluated by expert judgment, while the feasibilty of the model was explored through a limited test with students using the quasi-experimental method. The results showed that the implementation of a web-based model increased the pre-service teachers’ abilities in IMLR on each subtopic of chemical equilibrium in aqueous solution. The pre-service students also showed good abilities to resolve problems with interconnection patterns that progressed from macroscopic to submicroscopic and symbolic, rather than starting from submicroscopic and moving to symbolic and macroscopic. It can be concluded that the web-based learning model enhanced the pre-service teachers’ understanding of the submicroscopic level, changing existing problem-solving ability patterns from macroscopic–symbolic into six interconnection patterns, and improving student learning patterns

The Importance of Development of Representational Competence in Chemical Problem Solving Using Interactive Multimedia

This paper examined various literature to describe the importance of the development of representational competence within the context of chemical problem-solving. Problem-solving ability is one of high order thinking skills using representational competence. Representational competence is the ability to connect each level of multiple representations in chemistry. Students can use chemical multiple representations to solve problems if they are able to formulate a mental image of objects or processes at the submicroscopic level that cannot be physically observed, relate them to macroscopic phenomena and express them in symbolic representation, or vice versa. Submicroscopic representation is a key factor in chemical multiple representations. The inability to represent aspects submicroscopic can hinder the ability to solve problems related to the phenomenon of macroscopic and symbolic representations. Students generally have difficulty with chemistry due to the inability to represent and give explanations about the structure and processes at the level of submicroscopic. Optimal effort to develop this ability can be done using multimedia that integrates the three levels of chemical representations