A methodological evaluation of business process compliance management frameworks

Existing compliance management frameworks (CMFs) offer a multitude of compliance management capabilities that makes difficult for enterprises to decide on the suitability of a framework. Making a decision on the suitability requires a deep understanding of the functionalities of a framework. Gaining such an understanding is a difficult task which, in turn, requires specialised tools and methodologies for evaluation. Current compliance research lacks such tools and methodologies for evaluating CMFs. This paper reports a methodological evaluation of existing CMFs based on a pre-defined evaluation criteria. Our evaluation highlights what existing CMFs offer, and what they cannot. Also, it underpins various open questions and discusses the challenges in this direction.

Learner-focused evaluation cycles : facilitating learning using feedforward, concurrent and feedback evaluation

There is a growing trend to offer students learning opportunities that are flexible, innovative and engaging. As educators embrace student-centred agile teaching and learning methodologies, which require continuous reflection and adaptation, the need to evaluate students’ learning in a timely manner has become more pressing. Conventional evaluation surveys currently dominate the evaluation landscape internationally, despite recognition that they are insufficient to effectively evaluate curriculum and teaching quality. Surveys often: (1) fail to address the issues for which educators need feedback, (2) constrain student voice, (3) have low response rates and (4) occur too late to benefit current students. Consequently, this paper explores principles of effective feedback to propose a framework for learner-focused evaluation. We apply a three-stage control model, involving feedforward, concurrent and feedback evaluation, to investigate the intersection of assessment and evaluation in agile learning environments. We conclude that learner-focused evaluation cycles can be used to guide action so that evaluation is not undertaken simply for the benefit of future offerings, but rather to benefit current students by allowing ‘real-time’ learning activities to be adapted in the moment. As a result, students become co-producers of learning and evaluation becomes a meaningful, responsive dialogue between students and their instructors.

An evaluation of crowd counting methods, features and regression models

Existing crowd counting algorithms rely on holistic, local or histogram based features to capture crowd properties. Regression is then employed to estimate the crowd size. Insufficient testing across multiple datasets has made it difficult to compare and contrast different methodologies. This paper presents an evaluation across multiple datasets to compare holistic, local and histogram based methods, and to compare various image features and regression models. A K-fold cross validation protocol is followed to evaluate the performance across five public datasets: UCSD, PETS 2009, Fudan, Mall and Grand Central datasets. Image features are categorised into five types: size, shape, edges, keypoints and textures. The regression models evaluated are: Gaussian process regression (GPR), linear regression, K nearest neighbours (KNN) and neural networks (NN). The results demonstrate that local features outperform equivalent holistic and histogram based features; optimal performance is observed using all image features except for textures; and that GPR outperforms linear, KNN and NN regression