THE ENGLISH TEACHERS’ PERCEPTION TOWARDS THE FORMULATION OF STUDENTS’ LEARNING OUTCOME OF LISTENING BASED ON COMPETENCE-BASED CURRICULUM IN SLTPNs IN MALANG

The main objective of this study is to investigate the English teachers’ perception towards the formulation students’ learning outcome of listening based on competence-based curriculum in SLTPNs in Malang. The research design used in this study was descriptive. It is conducted to investigate the relationship of phenomena that is the English teachers’ perception towards the formulation students’ learning outcome of listening based on competence-based curriculum in SLTPNs in Malang. The writer used English teachers in SLTPNs 4, 13, and 18 as the subjects. The population and sample were taken by used two steps. The first step is purposive random sampling to determine the school and the second step is simple random sampling to determine the number of English teachers. The instrument used in this study was likert scale. Furthermore, there were five steps of collecting and analyzing the data. First the writer developed a research instrument for likert scale. Second, evaluating content validity of the instrument. Third, administering the research activity. Fourth, analyzing the data. Fifth, the writer took conclusion from the data that were analyzed. The result of the study shows that the formulation of students’ learning outcome of listening based on CBC is categorized as important (6.3) according to the English teachers in SLTPN 4 in Malang. The formulation of students’ learning outcome of listening based on CBC is categorized as important (6.12) according to the English teachers in SLTPN 13 in Malang. The formulation of students’ learning outcome of listening based on CBC is categorized as important (6.48) according to the English teachers in SLTPN 18 in Malang. The result of this study indicates that the formulation of students’ learning outcome of listening based on CBC is categorized as important (6.3) according to the English teachers in SLTPNs in Malang

Learning Outcomes For Economists

Articulating thoughtful learning outcome statements for courses and majors improves teaching and learning and satisfies accreditation requirements. After reading this paper, economists will be able to construct learning outcome statements that guide and enhance teaching and learning in their courses and programs. We present a framework for developing learning outcomes based on a set of five fundamental competencies in economics. We then provide another public good, offering a complete set of learning outcomes for an introductory microeconomics course, which instructors can include in their syllabi. For additional guidance, we construct examples of lesson-specific learning outcomes as well

Explicit and Implicit Processes in Human Aversive Conditioning

The ability to adapt to a changing environment is central to an organism’s success. The process of associating two stimuli (as in associative conditioning) requires very little in the way of neural machinery. In fact, organisms with only a few hundred neurons show conditioning that is specific to an associated cue. This type of learning is commonly referred to as implicit learning. The learning can be performed in the absence of the subject’s ability to describe it. One example of learning that is thought to be implicit is delay conditioning. Delay conditioning consists of a single cue (a tone, for example) that starts before, and then overlaps with, an outcome (like a pain stimulus). In addition to associating sensory cues, humans routinely link abstract concepts with an outcome. This more complex learning is often described as explicit since subjects are able to describe the link between the stimulus and outcome. An example of conditioning that requires this type of knowledge is trace conditioning. Trace conditioning includes a separation of a few seconds between the cue and outcome. Explicit learning is often proposed to involve a separate system, but the degree of separation between implicit associations and explicit learning is still debated. We describe aversive conditioning experiments in human subjects used to study the degree of interaction that takes place between explicit and implicit systems. We do this in three ways. First, if a higher order task (in this case a working memory task) is performed during conditioning, it reduces not only explicit learning but also implicit learning. Second, we describe the area of the brain involved in explicit learning during conditioning and confirm that it is active during both trace and delay conditioning. Third, using functional magnetic resonance imaging (fMRI), we describe hemodynamic activity changes in perceptual areas of the brain that occur during delay conditioning and persist after the learned association has faded. From these studies, we conclude that there is a strong interaction between explicit and implicit learning systems, with one often directly changing the function of the other.</p

An evaluation of pedagogically informed parameterised questions for self assessment

Self-assessment is a crucial component of learning. Learners can learn by asking themselves questions and attempting to answer them. However, creating effective questions is time-consuming because it may require considerable resources and the skill of critical thinking. Questions need careful construction to accurately represent the intended learning outcome and the subject matter involved. There are very few systems currently available which generate questions automatically, and these are confined to specific domains. This paper presents a system for automatically generating questions from a competency framework, based on a sound pedagogical and technological approach. This makes it possible to guide learners in developing questions for themselves, and to provide authoring templates which speed the creation of new questions for self-assessment. This novel design and implementation involves an ontological database that represents the intended learning outcome to be assessed across a number of dimensions, including level of cognitive ability and subject matter. The system generates a list of all the questions that are possible from a given learning outcome, which may then be used to test for understanding, and so could determine the degree to which learners actually acquire the desired knowledge. The way in which the system has been designed and evaluated is discussed, along with its educational benefits

Learning object relationships which determine the outcome of actions

Peer reviewedPublisher PD

Effective Assessment in Art and Design : writing learning outcomes and assessment criteria in art and design

This document has been written to help teachers in art and design who are writing project briefs or unit outlines in learning outcomes form for the first time. It is not meant to be prescriptive but rather a general guide that attempts to clarify the purposes of outcome-led learning and identify some of the pitfalls you might encounter. You will find that the most successful examples of outcome-led learning come from competency-based learning where it is relatively straightforward for students to provide evidence of their learning because the outcomes are almost always skills oriented. Increasingly, universities are adopting the learning outcomes approach (student-centred) in preference to the aims and objectives approach (teacher-centred). Many examples now exist of text-based subjects working with learning outcomes. One of the major challenges for them is to take the term 'understanding' and redefine it in terms of more specific measurable cognitive (thinking) outcomes. In art and design our challenge is greater because we work with rather more ambiguous terms such as 'creativity', 'imagination', 'originality' etc as well as 'understanding'. A significant challenge for you then will be to articulate learning outcomes in a way which promotes these important cognitive attributes but at the same time provides some useful methods of measuring their achievement

Impact of Biases in Big Data

The underlying paradigm of big data-driven machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. Is having simply more data always helpful? In 1936, The Literary Digest collected 2.3M filled in questionnaires to predict the outcome of that year's US presidential election. The outcome of this big data prediction proved to be entirely wrong, whereas George Gallup only needed 3K handpicked people to make an accurate prediction. Generally, biases occur in machine learning whenever the distributions of training set and test set are different. In this work, we provide a review of different sorts of biases in (big) data sets in machine learning. We provide definitions and discussions of the most commonly appearing biases in machine learning: class imbalance and covariate shift. We also show how these biases can be quantified and corrected. This work is an introductory text for both researchers and practitioners to become more aware of this topic and thus to derive more reliable models for their learning problems