IMPROVING THE STUDENTS’ WRITING COMPETENCE THROUGH PROCESS ORIENTED APPROACH (A ClassroomAction Research of the Grade 9.2 Students of SMP Negeri 1 Probolinggo in Academic Year of 2009/2010)

MASJHARI, S890208123, 2010. Improving the Students’ Writing Competence through Process Oriented Approach. A ClassroomAction Research in the Grade 9.2 Students of SMP Negeri 1 Probolinggo in Academic Year of 2009/2010. A Thesis. Graduate Program. English Education Department of. Sebelas Maret University of Surakarta.. This research is aimed at improving the students’ writing competence at SMP Negeri 1 Probolinggo in Academic Year of 2009/2010. It is assumed that the product will be good if the process is also good. Inthis research, there are two statements ofproblems: (1) Can process oriented approach improve the students’ writing competence of the grade 9.2 of the students of SMP Negeri 1 Probolinggo in Academic Year of 2009/2010? and (2) How can process oriented approach energize the students in writing processin the classroom of the grade 9.2 of the students of SMP Negeri 1 Probolinggo in Academic Year of 2009/2010? Related to the statement of problems, the researcher believes that the Process Oriented Approach (POA) is able tosolve the problems of students writingcompetence. The research was conducted through Classroom Action Research (CAR). The research was conducted in three cycles. Every cycle consisted of: planning, acting, observing, and reflecting. Each meeting consisted of three steps: 1) prewriting,2) writing, and rewriting that were broken down into the activity of building knowledge of field, modeling of text, outlining, drafting, and editingas the media to achieve the objectivesof learning writing. The researcher used two techniques in collecting the data – test and nontest. The researcher used subjective test to measure the students writing competence with the rubrics – content, organization, vocabulary, language and mechanicscreated by Tribble (1996: 130 –131). The result of subjective test was used to know the students’ success in writing process quantitatively while nontest used by the researcher was to get the qualitative data were taken through observation, interview, and questionnaires. The qualitative data was taken from three sources namely students, collaborator, and researcher himself to make the valid data. After the data had collected, the researcher and the collaborator analyzed and classified based on the sections. Then, they concluded the data collected as the research report. Quantitatively, the results of the implementation indicatedthat POA could improveand enhance the students’ writing competence. It could be seen that the grade 9.2 students of SMP Negeri 1 Probolinggo were able to improve their scoressignificantly. Here are the improvement between the results of cycle 1 to Cycle 3. The meanscorein termsof content had improved from 14.57 to 16.48, the organizationhadimproved from 13.91 to 16.22, the vocabularyhadimproved from 13.87 to 16.00, the language had improved from 17.87 to 22.30, and the mechanics had improved from 6.48 to 7.83. The mean scores of the students’ writing competence had improvedsignificantlyfrom 64.04to 75.91. Qualitatively, in joining the learning process, the students could show the motivation and self-esteem in joining the learning process of writing.It could be seen that the students’ involvement and motivation in learning process increased significantly. The percentage of teacher’s guide to the students in doing the task iii decreased significantly. It means that the students can be more independent in doing the tasks in the learning process of writing. Classically, all the students had been successful in gaining the score of writing competence. The average score was 75.91. It was the factthat POA could really give the positive influence to the students’ improvement in English writing. Individually, however, there were 21studentswho had gained the scores of 70 – 94 and there were 2 students who had gained the scores of 65 – 67. It couldbe concludedthat there were about 91.30% of students who had fulfilled the passing grade of 70 but there were 8.70% of students had not. In conclusion, there were twentystudents who had been successful in the writing competence while two students had not. Process oriented approach is one of approaches that is appropriate to improve the writing competence. So, it is necessary to develop and to applyitin the learning process for writing. This approach is flexible to apply in writing skills because everything needs the process before creating the last products. Frankly speaking, if the process can be done effectively and efficiently, the product must indicate the positive influence. If the process cannot be done well, it will be vise versa. In addition, based on the observation that had been done by the researcher, POA was able to improve the students’ motivation and self-esteem. So, it is really able to energize the students in term of learning process of writing. Finally, the students are successful in achieving the passing grade of 70 and even the last scores of students writing competences are 75.91 –pass over the students ‘KKM’ of 70. In implementing POA for the teacher should have a lot of competence, especially in preparing the learning materials, in handling the learning process, in mastering the knowledge of writing, and in creating innovative techniques and strategies of learning, and in managing the time. If the teacher is lacks of the teaching and learning competencies, POA cannot run effectively. As the result, it cannot give the positive influences to the developing the students’ writing competence

The role of an omnipresent pocket device : smartphone attendance and the role of user habits

Smartphones are convergent, always-on pocket devices that have taken up an important role in the life of their users. This warrants a closer look into how this medium is used in every-day situations. Are goal-oriented incentives the main drive for smartphone usage, or do habits play a critical role? This study with 481 Belgian smartphone users attempts to describe the precedents of smartphone attendance by validating the model of media attendance (MMA), a social-cognitive theory of uses and gratifications (LaRose & Eastin, 2004). We surprisingly did not find evidence for a significant effect of habits on smartphone usage. We suggest two explanations. First, we suggest some uncertainties concerning the MMA methodology. Second, we suggest a more complex reality in which several habitual use patterns are shaped, dependent on user, context and device. This warrants a more in-depth study, using more advanced measures for smartphone usage and habit strength

Comparing Grounded Theory and Topic Modeling: Extreme Divergence or Unlikely Convergence?

Researchers in information science and related areas have developed various methods for analyzing textual data, such as survey responses. This article describes the application of analysis methods from two distinct fields, one method from interpretive social science and one method from statistical machine learning, to the same survey data. The results show that the two analyses produce some similar and some complementary insights about the phenomenon of interest, in this case, nonuse of social media. We compare both the processes of conducting these analyses and the results they produce to derive insights about each method\u27s unique advantages and drawbacks, as well as the broader roles that these methods play in the respective fields where they are often used. These insights allow us to make more informed decisions about the tradeoffs in choosing different methods for analyzing textual data. Furthermore, this comparison suggests ways that such methods might be combined in novel and compelling ways

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Accurator: Nichesourcing for Cultural Heritage

With more and more cultural heritage data being published online, their usefulness in this open context depends on the quality and diversity of descriptive metadata for collection objects. In many cases, existing metadata is not adequate for a variety of retrieval and research tasks and more specific annotations are necessary. However, eliciting such annotations is a challenge since it often requires domain-specific knowledge. Where crowdsourcing can be successfully used for eliciting simple annotations, identifying people with the required expertise might prove troublesome for tasks requiring more complex or domain-specific knowledge. Nichesourcing addresses this problem, by tapping into the expert knowledge available in niche communities. This paper presents Accurator, a methodology for conducting nichesourcing campaigns for cultural heritage institutions, by addressing communities, organizing events and tailoring a web-based annotation tool to a domain of choice. The contribution of this paper is threefold: 1) a nichesourcing methodology, 2) an annotation tool for experts and 3) validation of the methodology and tool in three case studies. The three domains of the case studies are birds on art, bible prints and fashion images. We compare the quality and quantity of obtained annotations in the three case studies, showing that the nichesourcing methodology in combination with the image annotation tool can be used to collect high quality annotations in a variety of domains and annotation tasks. A user evaluation indicates the tool is suited and usable for domain specific annotation tasks

Data Modeling for Ambient Home Care Systems

Ambient assisted living (AAL) services are usually designed to work on the assumption that real-time context information about the user and his environment is available. Systems handling acquisition and context inference need to use a versatile data model, expressive and scalable enough to handle complex context and heterogeneous data sources. In this paper, we describe an ontology to be used in a system providing AAL services. The ontology reuses previous ontologies and models the partners in the value chain and their service offering. With our proposal, we aim at having an effective AAL data model, easily adaptable to specific domain needs and services

Affect experience in natural language collected with smartphones

Recent technological advancements in computerized text and speech analysis as well as machine learning methods have sparked a growing body of research investigating the algorithmic recognition of affect from the ubiquitous digital traces of natural language data and corresponding affect-linked language variations. Also, commercial interest to leverage these new data using AI for affect inferences is on the rise. However, due to the challenges associated with collecting data on subjective affect experience and corresponding language samples, previous research studies and commercial products have mostly relied on data sets from labelled text or enacted speech and, thereby, are focused on affect expression. This work leverages new smartphone-based data collection methods to collect self-reports on in-situ subjective affect experience and corresponding language samples in the wild to investigate between-person differences and within-person fluctuations in affect experience. The present dissertation aims to achieve three goals: (1) to investigate if between-person differences and within-person fluctuations in subjective affect experience are associated with and predictable from cues in spoken and written natural language, (2) to identify specific language characteristics, such as the use of specific word categories or voice parameters, that are associated with and predictive of affect experience, and (3) to analyze the influence of the context of language production on the associations and predictions of affect experience from natural language. This work is comprised of two empirical studies that analyze self-reports on subjective affect experience and natural language data collected with smartphones. Study 1 investigates predictions of between-person differences and within-person fluctuations in subjective momentary affect experience in more than 23000 speech samples from over 1000 participants in two data sets from Germany and the United States. In contrast to voice acoustics, which contain limited predictive information for affective arousal, state-of-the-art word embeddings yield significant above-chance predictions for affective arousal and valence. Moreover, interpretable machine learning methods are used to identify those voice features (i.e., loudness and spectral features) that are most predictive of affect experience. Finally, the work suggests that affect predictions from voice cues from semi-structured free speech are superior to those from read-out predefined sentences and that the emotional sentiment of the spoken content has no effect on affect predictions from voice cues. Study 2 analyzes patterns in written language data logged through smartphones' keyboards to investigate how between-person differences and within-person fluctuations in affect experience manifest in and are predictable from logged text data across different time frames and communication contexts. From a data set of more than 10 million typed words, features regarding typing dynamics, word use based on word dictionaries, and emoji and emoticon use are computed. From the data, distinct affect-linked language variations across communication contexts (private messaging versus public posting) and time frames (trait, weekly, daily, momentary) are identified (e.g., the use 1st person singular). Predictions of affect experience from machine learning algorithms, however, are not significantly better than chance. Results of this study highlight the challenges of using occurrence-counts, such as word dictionaries, for the assessment of subjective affect experience. By leveraging novel smartphone-based experience sampling and on-device language data collection in everyday life, the present dissertation shows how characteristics of spoken and written language are associated with and predictive of subjective affect experience. Thereby, this work highlights the utility of smartphones for investigating subjective affect experience in natural language in the wild, overcoming the caveats of prior research methods. Prediction results, however, challenge the optimistic prediction performances reported in prior works on the recognition of affect expression experience. Using statistical methods from the areas of description, prediction, and explanation, the present dissertation also reveals specific affect-linked language characteristics. Finally, results underline the relevance of the context of language production on language characteristics and corresponding affect predictions. The promising applications and potential future directions of this technology come with multiple challenges with regard to the conceptualization of affect, interdisciplinarity, ethics, and data privacy and security. If these challenges can be overcome, natural language analysis based on data collected with smartphones represents a promising tool to monitor affective well-being and to advance the affective sciences

I hear you eat and speak: automatic recognition of eating condition and food type, use-cases, and impact on ASR performance

We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient

Parental Support, Dance/Movement Therapy, and Early Childhood Self-Regulation – A Literature Review

Self-regulation is one of the main factors to contribute to successful social-emotional development during early childhood. Healthy attachment patterns between the parent/caregiver and child relationship, as well as safe and supportive environments in school contribute to children’s abilities to develop and learn to use self-regulating skills. This literature review explores the relationship dance/movement therapy has with the enhancement of parent-child relationships during early childhood, which is potentially contributing to the development of early childhood self-regulation. Using a citation chasing method, and research in online databases, inclusion criteria for resources consisted of peer reviewed quantitative, qualitative, arts-based, and mixed methods research studies. Effects of self-regulation skills and the predictability for later life successes were examined to determine how these skills prepare children for current and future challenges. Differences in culture and environment were researched to compare how the development of self-regulation is understood across different lenses, as well as evaluating different dance/movement therapy techniques to analyze the benefits they pose on children’s ability to self-regulate. Findings suggested that dance/movement therapy and parent-child dance/movement therapy support the development of self-regulating techniques during early childhood. This literature review will contribute to the field of dance/movement therapy by allowing other professionals in the field to further explore how through dance/movement therapy, children and their caregivers can benefit by developing self-regulating skills that will allow the child to succeed in individual and group settings