Eliciting metacognitive experiences and reflection in a year 11 chemistry classroom : an activity theory perspective

Concerns regarding students' learning and reasoning in chemistry classrooms are well documented. Students' reasoning in chemistry should be characterized by conscious consideration of chemical phenomenon from laboratory work at macroscopic, molecular/sub-micro and symbolic levels. Further, students should develop metacognition in relation to such ways of reasoning about chemistry phenomena. Classroom change eliciting metacognitive experiences and metacognitive reflection is necessary to shift entrenched views of teaching and learning in students. In this study, Activity Theory is used as the framework for intepreting changes to the rules/customs and tools of the activity systems of two different classes of students taught by the same teacher, Frances, who was teaching chemical equilibrium to those classes in consecutive years. An interpretive methodolgy involving multiple data sources was employed. Frances explicitly changed her pedagogy in the second year to direct students attention to increasingly consider chemical phenomena at the molecular/sub-micro level. Additonally, she asked students not to use the textbook until toward the end of the equilibrium unit and sought to engage them in using their prior knowledge of chemistry to understand their observations from experiments. Frances' changed pedagogy elicited metacognitive experiences and reflection in students and challenged them to reconsider their metacognitive beliefs about learning chemistry and how it might be achieved. While teacher change is essential for science education reform, students are not passive players in the change efforts and they need to be convinced of the viability of teacher pedagogical change in the context of their goals, intentions, and beliefs.

Enhancing foreign language learning through listening strategy delivered in L1: an experimental study

Listening used in language teaching refers to a complex process that allows us to understand spoken language. The current study, conducted in Iran with an experimental design, investigated the effectiveness of teaching listening strategies delivered in L1 (Persian) and its effect on listening comprehension in L2. Five listening strategies: Guessing, making inferences, identifying topics, repetition, and note-taking were taught over 14 weeks during a semester. Sixty lower intermediate female participants came from two EFL classrooms in an English language institute. The experimental class (n = 30) who listened to their classroom activities performed better (t value = 10.083) than the control class using a methodology that led learners through five listening strategies in Persian. The same teacher taught the students in the control class (n = 30), who listened to the same classroom listening activities without any of the above listening strategies. A pre and post listening test made by a group of experts in the language institute assessed the effect of teaching listening strategies delivered in L1. Results gathered on the post intervention listening test revealed that listening strategies delivered in L1 led to a statistically significant improvement in their discrete listening scores compared with the control group.

Learning domain-specific sentiment lexicons for predicting product sales

Generic sentiment lexicons have been widely used for sentiment analysis these days. However, manually constructing sentiment lexicons is very time-consuming and it may not be feasible for certain application domains where annotation expertise is not available. One contribution of this paper is the development of a statistical learning based computational method for the automatic construction of domain-specific sentiment lexicons to enhance cross-domain sentiment analysis. Our initial experiments show that the proposed methodology can automatically generate domain-specific sentiment lexicons which contribute to improve the effectiveness of opinion retrieval at the document level. Another contribution of our work is that we show the feasibility of applying the sentiment metric derived based on the automatically constructed sentiment lexicons to predict product sales of certain product categories. Our research contributes to the development of more effective sentiment analysis system to extract business intelligence from numerous opinionated expressions posted to the Web

Unexpected issues and solutions: Emerge from environmental manipulation strategy

Environmental manipulation removes students from their everyday worlds to unfamiliar worlds, to facil- itate learning. This article reports that this strategy was effective when applied in a university design unit, using the tactic of immersion in the Second Life online virtual environment. The objective was for teams of stu- dents each to design a series of modules for an orbiting space station using supplied data. The changed and futuristic environment led the students to an important but previously unconsidered design decision which they were able to address in novel ways because of, rather than in spite of, the Second Life immersion.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

Australian higher education evaluation through assurance of learning

A collaborative research project conducted by five Australian universities inquired into the philosophy and motivation for Assurance of Learning (AoL) as a process of education evaluation. Associate Deans Teaching and Learning representing Business schools from twenty-five universities across Australia participated in telephone interviews. Data was analysed using NVIVO9. Results indicated that articulated rationale for AoL was both ensuring that students had acquired the attributes and skills the universities claimed they had, and the philosophy of continuous improvement. AoL was motivated both by ritualistic objectives to satisfy accreditation requirements and virtuous agendas for quality improvement. Closing-the-loop was emphasised, but was mostly wishful thinking for next steps beyond data collection and reporting. AoL was conceptualised as one element within the larger context of quality review, but there was no evidence of comprehensive frameworks or strategic plans.