Implementing a learning technology strategy: top-down strategy meets bottom-up culture

Using interview-based 'insider case study' research, this paper outlines why the University of Salford has adopted a Learning Technologies Strategy and examines the factors which are likely to lead to its successful implementation. External reasons for the adoption focused on the need to: respond to 'increased Higher Education (HE) competition', meet student expectations of learning technology use, provide more flexibility and access to the curriculum, address the possible determining effect of technology and establish a Virtual Learning Environment (VLE) presence in this 'particular area of the HE landscape'. Internal drivers centred on the need to: continue a 'bottom-up' e-learning pilot project initiative, particularly given that a VLE is a 'complex tool' which requires effective strategic implementation, and promote the idea that learning technology will play an important role in determining the type of HE institution that the University of Salford wishes to become. Likely success factors highlighted the need to: create 'time and space' for innovation, maintain effective communication and consultation at all levels of the organization, emphasize the operational aspects of the strategy, establish a variety of staff development processes and recognize the negotiatory processes involved in understanding the term 'web presence' in local teaching cultures. Fundamentally, the paper argues that policy makers should acknowledge the correct 'cultural configuration' of HE institutions when seeking to manage and achieve organizational change. Thus, it is not just a question of establishing 'success factors' per se but also whether they are contextualized appropriately within a 'correct' characterization of the organizational culture

IMPROVING STUDENTS’ READING COMPREHENSION THROUGH TOP-DOWN STRATEGY

The objective of this research was  to find out the improvement of the students’ reading comprehension by using Top-Down strategy in learning reading comprehension at SMA Negeri 1 Wajo. This research employed Pre-experimental method. The population of this research was eight classes of the Tenth grade students at SMA Negeri 1 Wajo in academic year 2019/2020 with the total population were 242 students. The researcher took random sampling and chosen X MIA 1. The data of the research were collected by using the instrument, namely reading test. The result of data analysis showed that there was significant difference between the students’ score after they were taught by top-down strategy and before they were taught by using top-down strategy. It was proved by the mean score of post-test was higher than the mean score of pre-test (67.2042.56). Furthermore, the result of the p- value (0.00) was lower than α (0.000.05) which meant that H1 was accepted.

Evaluation of bottom-up and top-down strategies for aggregated forecasts: state space models and arima applications

Abstract. In this research, we consider monthly series from the M4 competition to study the relative performance of top-down and bottom-up strategies by means of implementing forecast automation of state space and ARIMA models. For the bottomup strategy, the forecast for each series is developed individually and then these are combined to produce a cumulative forecast of the aggregated series. For the top-down strategy, the series or components values are first combined and then a single forecast is determined for the aggregated series. Based on our implementation, state space models showed a higher forecast performance when a top-down strategy is applied. ARIMA models had a higher forecast performance for the bottom-up strategy. For state space models the top-down strategy reduced the overall error significantly. ARIMA models showed to be more accurate when forecasts are first determined individually. As part of the development we also proposed an approach to improve the forecasting procedure of aggregation strategies

From cognitive science to cognitive neuroscience to neuroeconomics

As an emerging discipline, neuroeconomics faces considerable methodological and practical challenges. In this paper, I suggest that these challenges can be understood by exploring the similarities and dissimilarities between the emergence of neuroeconomics and the emergence of cognitive and computational neuroscience two decades ago. From these parallels, I suggest the major challenge facing theory formation in the neural and behavioural sciences is that of being under-constrained by data, making a detailed understanding of physical implementation necessary for theory construction in neuroeconomics. Rather than following a top-down strategy, neuroeconomists should be pragmatic in the use of available data from animal models, information regarding neural pathways and projections, computational models of neural function, functional imaging and behavioural data. By providing convergent evidence across multiple levels of organization, neuroeconomics will have its most promising prospects of success

How Top-Down AI Introduction Leads to Incremental Business Improvement

Artificial intelligence offers the opportunity for radical improvements such as completely new business solutions. It also enables the improvement of existing business. This paper reports on a case study that tests two strategies to identify AI use cases: top-down and bottom-up. The use cases are differentiated according to whether they promise incremental or radical business improvements and whether they are realizable in the short or long term. The top-down strategy identifies use cases that promise short-term but incremental improvements. They relate to existing business, but no disruptive ideas emerge. The bottom-up strategy allows for a broader understanding of AI’s potentials to improve business. Completely new and disruptive ideas emerge, but require huge upfront effort. Organizations best start with AI pilot projects that are feasible in the short term: Either by first applying a bottom-up strategy that is supplemented and evaluated with the top-down strategy, or top-down only

Perceptual-based textures for scene labeling: a bottom-up and a top-down approach

Due to the semantic gap, the automatic interpretation of digital images is a very challenging task. Both the segmentation and classification are intricate because of the high variation of the data. Therefore, the application of appropriate features is of utter importance. This paper presents biologically inspired texture features for material classification and interpreting outdoor scenery images. Experiments show that the presented texture features obtain the best classification results for material recognition compared to other well-known texture features, with an average classification rate of 93.0%. For scene analysis, both a bottom-up and top-down strategy are employed to bridge the semantic gap. At first, images are segmented into regions based on the perceptual texture and next, a semantic label is calculated for these regions. Since this emerging interpretation is still error prone, domain knowledge is ingested to achieve a more accurate description of the depicted scene. By applying both strategies, 91.9% of the pixels from outdoor scenery images obtained a correct label

Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization

One of the fundamental goals in proteomics and cell biology is to identify the functions of proteins in various cellular organelles and pathways. Information of subcellular locations of proteins can provide useful insights for revealing their functions and understanding how they interact with each other in cellular network systems. Most of the existing methods in predicting plant protein subcellular localization can only cover three or four location sites, and none of them can be used to deal with multiplex plant proteins that can simultaneously exist at two, or move between, two or more different location sites. Actually, such multiplex proteins might have special biological functions worthy of particular notice. The present study was devoted to improve the existing plant protein subcellular location predictors from the aforementioned two aspects. A new predictor called “Plant-mPLoc” is developed by integrating the gene ontology information, functional domain information, and sequential evolutionary information through three different modes of pseudo amino acid composition. It can be used to identify plant proteins among the following 12 location sites: (1) cell membrane, (2) cell wall, (3) chloroplast, (4) cytoplasm, (5) endoplasmic reticulum, (6) extracellular, (7) Golgi apparatus, (8) mitochondrion, (9) nucleus, (10) peroxisome, (11) plastid, and (12) vacuole. Compared with the existing methods for predicting plant protein subcellular localization, the new predictor is much more powerful and flexible. Particularly, it also has the capacity to deal with multiple-location proteins, which is beyond the reach of any existing predictors specialized for identifying plant protein subcellular localization. As a user-friendly web-server, Plant-mPLoc is freely accessible at http://www.csbio.sjtu.edu.cn/bioinf/plant-multi/. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results. It is anticipated that the Plant-mPLoc predictor as presented in this paper will become a very useful tool in plant science as well as all the relevant areas