19the Analysis of Students\u27 Team Achievement Divisions (Stad) Used in Learning Practice of Translating and Interpreting

Due to the Motto of STKIP Siliwangi Bandung “ The Leader of Learning Innovation”, this research deals with The Analysis of Student Teams Achievement Division (STAD) used in Learning Practice of Translating and Interpreting. This research explores the implementation of Students\u27 Team Achievement Divisions (STAD) and find out the advantages and disadvantages of Students\u27 Team Achievement Divisions (STAD) used in learning Practice of Translating and Interpreting. The objective of the research was to motivate students and encourage them to be active in learning, to accelerate student achievement, to improve behavior in learning, and to find out the students\u27 ability with Student Teams-Achievement Divisions (STAD) method. Data collection technique focused on participant observation, interviews, and documentation. Student Team-Achievement Division (STAD) is one type of cooperative learning model using small groups with a number of members of each group of 4-5 students in heterogenic way. It begins by delivering the objectives of learning, delivering of material, group activities, quizzes and group rewards. Students\u27 Team Achievement Divisions (STAD) method also is an effective method of cooperative learning. As with other learning methods, STAD method also has advantages and disadvantages. In the learning process there are good interaction among students, good attitude, increased interpersonal skills. It\u27s effective in increasing student participation and can train students to be more focus, more concentrate in answering questions from the teacher. It can make students eager to learn. But if the chief of the group can not resolve conflicts that arise constructively, it will be less effective in a group work. And if the number of groups is not considered, that is less than four, it would tend to withdraw and less active during the discussion. And if the number of groups of more than five, then chances for them to be passive in task completio

A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality

Microtask crowdsourcing is increasingly critical to the creation of extremely large datasets. As a result, crowd workers spend weeks or months repeating the exact same tasks, making it necessary to understand their behavior over these long periods of time. We utilize three large, longitudinal datasets of nine million annotations collected from Amazon Mechanical Turk to examine claims that workers fatigue or satisfice over these long periods, producing lower quality work. We find that, contrary to these claims, workers are extremely stable in their quality over the entire period. To understand whether workers set their quality based on the task's requirements for acceptance, we then perform an experiment where we vary the required quality for a large crowdsourcing task. Workers did not adjust their quality based on the acceptance threshold: workers who were above the threshold continued working at their usual quality level, and workers below the threshold self-selected themselves out of the task. Capitalizing on this consistency, we demonstrate that it is possible to predict workers' long-term quality using just a glimpse of their quality on the first five tasks.Comment: 10 pages, 11 figures, accepted CSCW 201

A systematic review of pedagogical approaches that can effectively include children with special educational needs in mainstream classrooms with a particular focus on peer group interactive approaches

The broad background to this review is a long history of concepts of special pupils and special education, and a faith in special pedagogical approaches. The rise of inclusive schools and some important critiques of special pedagogy (e.g. Hart, 1996; Norwich and Lewis, 2001; Thomas and Loxley, 2001) have raised the profile of teaching approaches that ordinary teachers can and do use to include children with special educational needs in mainstream classrooms. Inclusive education itself is increasingly conceived as being about the quality of learning and participation that goes on in inclusive schools rather than simplistic matters of where children are place

How to Ask for Technical Help? Evidence-based Guidelines for Writing Questions on Stack Overflow

Context: The success of Stack Overflow and other community-based question-and-answer (Q&A) sites depends mainly on the will of their members to answer others' questions. In fact, when formulating requests on Q&A sites, we are not simply seeking for information. Instead, we are also asking for other people's help and feedback. Understanding the dynamics of the participation in Q&A communities is essential to improve the value of crowdsourced knowledge. Objective: In this paper, we investigate how information seekers can increase the chance of eliciting a successful answer to their questions on Stack Overflow by focusing on the following actionable factors: affect, presentation quality, and time. Method: We develop a conceptual framework of factors potentially influencing the success of questions in Stack Overflow. We quantitatively analyze a set of over 87K questions from the official Stack Overflow dump to assess the impact of actionable factors on the success of technical requests. The information seeker reputation is included as a control factor. Furthermore, to understand the role played by affective states in the success of questions, we qualitatively analyze questions containing positive and negative emotions. Finally, a survey is conducted to understand how Stack Overflow users perceive the guideline suggestions for writing questions. Results: We found that regardless of user reputation, successful questions are short, contain code snippets, and do not abuse with uppercase characters. As regards affect, successful questions adopt a neutral emotional style. Conclusion: We provide evidence-based guidelines for writing effective questions on Stack Overflow that software engineers can follow to increase the chance of getting technical help. As for the role of affect, we empirically confirmed community guidelines that suggest avoiding rudeness in question writing.Comment: Preprint, to appear in Information and Software Technolog