1,342,792 research outputs found

    Learning Outcomes For Economists

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    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


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    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


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    Teaching and learning linguistics in higher education is very important especially for English students because of learning language aspects. Linguistics is a course learnt by English students in Department of English Education. In the process of teaching and learning linguistics, the lecturers should focus on the outcome of the learning. They do not only demonstrate how to understand the branches of linguistics such as morphology, semantics, discourse but they also should be able to make a successful teaching and learning. One of the ways is by applying Outcome Based Learning (OBL) which is rarely applied. This approach covers three basic elements: designing the course intended learning outcomes, designing teaching and learning activities, and designing assessment. That is why the literature study is used to know whether OBL can be a potential approach in teaching and learning a linguistics course in Department of English Education. This article focuses on how OBL contributes in the teaching and learning a linguistics course

    Explicit and Implicit Processes in Human Aversive Conditioning

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    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

    Factors affecting e-Learning effectiveness in a higher learning institution in Malaysia

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    The purpose of this research was to investigate factors that influence the effectiveness of the e-learning system in a higher learning institution. The participants were students randomly selected from diploma and degree programs. The main instrument was a questionnaire that was distributed to the students. The researchers collected 205 completed questionnaires out of a total of 300. Four factors were chosen as independent variables namely: reaction and satisfaction,learning outcome and achievement, familiarity with online learning technology, and participation and interaction. It was found that the effectiveness of the e-learning system was significantly affected by reaction and satisfaction, learning outcome and achievement, and familiarity with online learning technology. The participation and interaction factor had no apparent effect on the effectiveness of the e-learning system. Therefore, it is recommended that higher learning institutions interested in introducing e-learning should focus on students’ reaction and satisfaction towards the system.E-learning should focus on learning outcomes and achievement. It is also recommended that institutions first look into the issue of familiarity with online learning technology among students before introducing the e-learning system so as to determine whether students are comfortable with the online learning tools

    Labels direct infants’ attention to commonalities during novel category learning

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    Recent studies have provided evidence that labeling can influence the outcome of infants’ visual categorization. However, what exactly happens during learning remains unclear. Using eye-tracking, we examined infants’ attention to object parts during learning. Our analysis of looking behaviors during learning provide insights going beyond merely observing the learning outcome. Both labeling and non-labeling phrases facilitated category formation in 12-month-olds but not 8-month-olds (Experiment 1). Non-linguistic sounds did not produce this effect (Experiment 2). Detailed analyses of infants’ looking patterns during learning revealed that only infants who heard labels exhibited a rapid focus on the object part successive exemplars had in common. Although other linguistic stimuli may also be beneficial for learning, it is therefore concluded that labels have a unique impact on categorization

    Active Learning with Expert Advice

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    Conventional learning with expert advice methods assumes a learner is always receiving the outcome (e.g., class labels) of every incoming training instance at the end of each trial. In real applications, acquiring the outcome from oracle can be costly or time consuming. In this paper, we address a new problem of active learning with expert advice, where the outcome of an instance is disclosed only when it is requested by the online learner. Our goal is to learn an accurate prediction model by asking the oracle the number of questions as small as possible. To address this challenge, we propose a framework of active forecasters for online active learning with expert advice, which attempts to extend two regular forecasters, i.e., Exponentially Weighted Average Forecaster and Greedy Forecaster, to tackle the task of active learning with expert advice. We prove that the proposed algorithms satisfy the Hannan consistency under some proper assumptions, and validate the efficacy of our technique by an extensive set of experiments.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013

    Auditing: Active Learning with Outcome-Dependent Query Costs

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    We propose a learning setting in which unlabeled data is free, and the cost of a label depends on its value, which is not known in advance. We study binary classification in an extreme case, where the algorithm only pays for negative labels. Our motivation are applications such as fraud detection, in which investigating an honest transaction should be avoided if possible. We term the setting auditing, and consider the auditing complexity of an algorithm: the number of negative labels the algorithm requires in order to learn a hypothesis with low relative error. We design auditing algorithms for simple hypothesis classes (thresholds and rectangles), and show that with these algorithms, the auditing complexity can be significantly lower than the active label complexity. We also discuss a general competitive approach for auditing and possible modifications to the framework.Comment: Corrections in section
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