Strengthening Incentives for Student Effort and Learning: Michigan’s Merit Award Program?

[Excerpt] One of the primary reasons American students learn a good deal less during secondary school than students in other industrialized nations is that they devote less time and intellectual energy to the task.1 Accountability systems designed to get teachers to try harder and set higher standards will not produce more student learning if [as one high school teacher put it] “students are sitting back in their desks, arms crossed, waiting for their teachers to make them smart (Zoch, 1998, p. 70).” Learning is not a passive act; it requires the time and active involvement of the learner. In a classroom with 1 teacher and 25 students, there are 25 learning hours spent for every hour of teaching time. Learning takes work and that work is generally not going to be as much fun as hanging out with friends or watching TV. If students cannot be motivated to give up some time socializing or watching TV so that they can learn difficult material and develop high level skills, the time and talents of teachers will be wasted.

Innovations in Knowledge and Learning for Competitive Higher Education in Asia and the Pacific

Competitive advantage in a knowledge economy is dependent on the ability to innovate and create new knowledge products and services, and to find innovative applications for them. Higher education institutions in Asia and the Pacific, modelled on industrial age thinking that demands excellence in routinized capacities, lack the ability to innovate and create new knowledge enterprises. The transition to a knowledge economy is affecting the purpose, content, pedagogy, and methodologies of higher education. Nontraditional stakeholders such as professional bodies, industry experts, think tanks, research institutes, and field experts/practitioners are now involved not only in planning but in providing higher education services. The traditional model of “knowledge versus skills” is no longer relevant. Higher education programs must consider lived experiences, contextual knowledge, and indigenous knowledge.

Creating the reasonable adventurer: The co-evolution of student and learning environment

Purpose: This paper seeks to address the issue of how are graduate skills developed. The focus is not on which skills, but rather what type of learning environments is required within Higher Education to support the development of skills valued and demanded by SMEs within Australia. Approach: This paper takes a step back to consider the underlying issue of how an individual student's habits of thought are altered. In doing so, the past works of Morgan, Dewey, Whitehead, and Tyler are synthesized with the modern work of Baxter Magolda, Heath, and Biggs. Findings: It is argued that that without the development of a student-centred learning environment, most graduates will not develop the types of skills demanded by SMEs in a meaningfully way. That the failure to treat knowledge and skills as equal drivers of curriculum design will result in an imbalance that relegates skill development to a secondary learning outcome. Practical Implications: By removing the distraction of what skills should be developed, a clearer focus is possible regarding how educators should assist students to develop a broad array of generic graduate skills. From this perspective, skills can be viewed as an essential element of the educational process, rather than a new element that must be squeezed in between content. Value of Paper: This paper extends recent discussion of skills development through the use of an evolutionary perspective. Viewed as a process of creating social change, education becomes increasingly connected to a world that lays beyond institutional boundaries, thus promoting the notion of developing graduates for the world that awaits them.

Editorial - TL Forum 2015: Teaching and learning uncapped

The authors discuss the teaching and learning forum and the number of submissions to its staff from 2006-2015.

The inside story: A survey of social work students' supervision and learning opportunities on placement

Practice learning accounts for half of the content of the bachelor of social work degree course requirements in Northern Ireland in their field education programmes and share a professional and ethical responsibility with practice teachers to provide appropriate learning environments to prepare students as competent and professional practitioners. The accreditation standards for practice learning require the placement to provide students with regular supervision and exposure to a range of learning strategies, but there is little research that actually identifies the types of placements offering this learning and the key activities provided. This paper builds on an Australian study and surveys social work students in two programmes in Northern Ireland about their exposure to a range of learning activities, how frequently they were provided and how it compares to what is required by the Northern Ireland practice standards. The results indicated that, although most students were satisfied with the supervision and support they received during their placement, the frequency of supervision and type of learning activities varied according to different settings, year levels and who provided the learning opportunities.

Single-machine scheduling with past-sequence-dependent setup times and learning effects: a parametric analysis

In this article, we consider the single-machine scheduling problem with past-sequence-dependent (p-s-d) setup times and a learning effect. The setup times are proportional to the length of jobs that are already scheduled; i.e. p-s-d setup times. The learning effect reduces the actual processing time of a job because the workers are involved in doing the same job or activity repeatedly. Hence, the processing time of a job depends on its position in the sequence. In this study, we consider the total absolute difference in completion times (TADC) as the objective function. This problem is denoted as 1/LE, (Spsd)/TADC in Kuo and Yang (2007) ('Single Machine Scheduling with Past-sequence-dependent Setup Times and Learning Effects', Information Processing Letters, 102, 22-26). There are two parameters a and b denoting constant learning index and normalising index, respectively. A parametric analysis of b on the 1/LE, (Spsd)/TADC problem for a given value of a is applied in this study. In addition, a computational algorithm is also developed to obtain the number of optimal sequences and the range of b in which each of the sequences is optimal, for a given value of a. We derive two bounds b* for the normalising constant b and a* for the learning index a. We also show that, when a < a* or b > b*, the optimal sequence is obtained by arranging the longest job in the first position and the rest of the jobs in short processing time order.

A Bacterial Foraging Optimization and Learning Automata Based Feature Selection for Motor Imagery EEG Classification

Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.

Case study: Using iPads to deliver a personalised IT and learning strategy

Case study on how 16 - 18 year old students at Portsmouth College have access to an iPad mini to support independent and personalised learning.

Sampling and learning the Mallows and Generalized Mallows models under the Cayley distance

[EN]The Mallows and Generalized Mallows models are compact yet powerful and natural ways of representing a probability distribution over the space of permutations. In this paper we deal with the problems of sampling and learning (estimating) such distributions when the metric on permutations is the Cayley distance. We propose new methods for both operations, whose performance is shown through several experiments. We also introduce novel procedures to count and randomly generate permutations at a given Cayley distance both with and without certain structural restrictions. An application in the field of biology is given to motivate the interest of this model.

Sampling and learning the Mallows and Weighted Mallows models under the Hamming distance

[EN]In this paper we deal with distributions over permutation spaces. The Mallows model is the mode l in use. The associated distance for permutations is the Hamming distance.