Development and evaluation of a web-based learning system based on learning object design and generative learning to improve higher-order thinking skills and learning

This research aims to design, develop and evaluate the effectiveness of a Webbased learning system prototype called Generative Object Oriented Design (GOOD) learning system. Result from the preliminary study conducted showed most of the students were at lower order thinking skills (LOTS) compared to higher order thinking skills (HOTS) based on Bloom’s Taxonomy. Based on such concern, GOOD learning system was designed and developed based on learning object design and generative learning to improve HOTS and learning. A conceptual model design of GOOD learning system, called Generative Learning Object Organizer and Thinking Tasks (GLOOTT) model, has been proposed from the theoretical framework of this research. The topic selected for this research was Computer System (CS) which focused on the hardware concepts from the first year Diploma of Computer Science subjects. GOOD learning system acts as a mindtool to improve HOTS and learning in CS. A pre-experimental research design of one group pretest and posttest was used in this research. The samples of this research were 30 students and 12 lecturers. Data was collected from the pretest, posttest, portfolio, interview and Web-based learning system evaluation form. The paired-samples T test analysis was used to analyze the achievement of the pretest and posttest and the result showed that there was significance difference between the mean scores of pretest and posttest at the significant level a = 0.05 (p=0.000). In addition, the paired-samples T test analysis of the cognitive operations from Bloom’s Taxonomy showed that there was significance difference for each of the cognitive operation of the students before and after using GOOD learning system. Results from the study showed improvement of HOTS and learning among the students. Besides, analysis of portfolio showed that the students engaged HOTS during the use of the system. Most of the students and lecturers gave positive comments about the effectiveness of the system in improving HOTS and learning in CS. From the findings in this research, GOOD learning system has the potential to improve students’ HOTS and learning

The CKC Challenge: Exploring Tools for Collaborative Knowledge Construction

The great success of Web 2.0 is mainly fuelled by an infrastructure that allows web users to create, share, tag, and connect content and knowledge easily. The tools for developing structured knowledge in this manner have started to appear as well. However, there are few, if any, user studies that are aimed at understanding what users expect from such tools, what works and what doesn't. We organized the Collaborative Knowledge Construction (CKC) Challenge to assess the state of the art for the tools that support collaborative processes for creation of various forms of structured knowledge. The goal of the Challenge was to get users to try out different tools and to learn what users expect from such tools /features that users need, features that they like or dislike. The Challenge task was to construct structured knowledge for a portal that would provide information about research. The Challenge design contained several incentives for users to participate. Forty-nine users registered for the Challenge; thirty three of them participated actively by using the tools. We collected extensive feedback from the users where they discussed their thoughts on all the tools that they tried. In this paper, we present the results of the Challenge, discuss the features that users expect from tools for collaborative knowledge constructions, the features on which Challenge participants disagreed, and the lessons that we learned

FraudDroid: Automated Ad Fraud Detection for Android Apps

Although mobile ad frauds have been widespread, state-of-the-art approaches in the literature have mainly focused on detecting the so-called static placement frauds, where only a single UI state is involved and can be identified based on static information such as the size or location of ad views. Other types of fraud exist that involve multiple UI states and are performed dynamically while users interact with the app. Such dynamic interaction frauds, although now widely spread in apps, have not yet been explored nor addressed in the literature. In this work, we investigate a wide range of mobile ad frauds to provide a comprehensive taxonomy to the research community. We then propose, FraudDroid, a novel hybrid approach to detect ad frauds in mobile Android apps. FraudDroid analyses apps dynamically to build UI state transition graphs and collects their associated runtime network traffics, which are then leveraged to check against a set of heuristic-based rules for identifying ad fraudulent behaviours. We show empirically that FraudDroid detects ad frauds with a high precision (93%) and recall (92%). Experimental results further show that FraudDroid is capable of detecting ad frauds across the spectrum of fraud types. By analysing 12,000 ad-supported Android apps, FraudDroid identified 335 cases of fraud associated with 20 ad networks that are further confirmed to be true positive results and are shared with our fellow researchers to promote advanced ad fraud detectionComment: 12 pages, 10 figure

Taxonomy Induction using Hypernym Subsequences

We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms. Unlike all previous approaches, which typically extract direct hypernym edges for terms, our approach utilizes a novel probabilistic framework to extract hypernym subsequences. Taxonomy induction from extracted subsequences is cast as an instance of the minimumcost flow problem on a carefully designed directed graph. Through experiments, we demonstrate that our approach outperforms stateof- the-art taxonomy induction approaches across four languages. Importantly, we also show that our approach is robust to the presence of noise in the input vocabulary. To the best of our knowledge, no previous approaches have been empirically proven to manifest noise-robustness in the input vocabulary

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

A framework for selecting workflow tools in the context of composite information systems

When an organization faces the need of integrating some workflow-related activities in its information system, it becomes necessary to have at hand some well-defined informational model to be used as a framework for determining the selection criteria onto which the requirements of the organization can be mapped. Some proposals exist that provide such a framework, remarkably the WfMC reference model, but they are designed to be appl icable when workflow tools are selected independently from other software, and departing from a set of well-known requirements. Often this is not the case: workflow facilities are needed as a part of the procurement of a larger, composite information syste m and therefore the general goals of the system have to be analyzed, assigned to its individual components and further detailed. We propose in this paper the MULTSEC method in charge of analyzing the initial goals of the system, determining the types of components that form the system architecture, building quality models for each type and then mapping the goals into detailed requirements which can be measured using quality criteria. We develop in some detail the quality model (compliant with the ISO/IEC 9126-1 quality standard) for the workflow type of tools; we show how the quality model can be used to refine and clarify the requirements in order to guarantee a highly reliable selection result; and we use it to evaluate two particular workflow solutions a- ailable in the market (kept anonymous in the paper). We develop our proposal using a particular selection experience we have recently been involved in, namely the procurement of a document management subsystem to be integrated in an academic data management information system for our university.Peer ReviewedPostprint (author's final draft