Psy 270 wk7

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Psy 270 wk7

This work considers a range of social learning behaviours supported in our recently designed and implemented collaborative learning system which supports students giving and receiving feedback on each other s developing work and practice.

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The course was delivered to several thousand students on Coursera during which students were directed onto our social learning environment to take part in group work and assessment activities. This work introduces a swarm intelligence technique, Stochastic Diffusion Search SDSand shows how it can be adapted and applied to our data in order to perform classification tasks.

The novelty of the approach is not only in using this technique, but also applying it to data linked to social behaviour i. This paper investigates what combined activity is the best predictor of success or failure in the course. The aim is to argues that the results obtained using the proposed approach indicate the promising potential of predicting students performance through applying swarm intelligence technique to social behaviours.

This work has a number of potential benefits including designing better social learning systems, designing more effective social learning and assessment exercises, and encouraging disengaged students. In addition, this work is an important step in addressing our long term goal of evidencing how critical student learning takes place as they give and receive feedback to and from each other on work in progress.

Corresponding author Increasingly researchers are focusing on the significance of social learning and investigating its impact within the various online learning environments. Acknowledging the importance of collaboration and teamwork, as an embedded element in the Massive Open Online Courses MOOCsthis method of learning is desirable for many employers who rely on highly collaborative and online-based works.

Our programme of work is concerned with designing a novel learning technology, online courses and assessments, which provide us with a range of data we can use to understand how learning takes place through online social interaction.

Psy 270 wk7

Our pedagogy is influenced by our home institution s art-school pedagogy across practice-based subjects such as art, music and design where students learn by sharing work in progress within tutor groups and giving and receiving feedback to each other.

The aim of this work is to use learning analytics to build strong arguments for the adoption of social learning pedagogies supported by innovative technology. Therefore this paper focuses on extracting information from social learning activity logs, not the full range of more traditional courseware access and activity logs.

The objective is to gain a better understanding if these activities have any measurable relation to learning, and if so which are the most important activities and in which combinations. The analysis presented here is a first step in that direction, where the attempt is to predict if students will pass or fail a course, using only low level user interface telemetry data gathered from our social learning platform.

Given the undeniable significance of data classification in different and diverse scientific domains e. Nature-inspired metaheuristic algorithms are among one of the categories which aimed at providing solutions to this problem.

In this paper a novel method in addressing data classification in the context of educational data is used where a swarm intelligence algorithm is adapted for this purpose. A recent review [2] details the extensive applications of this algorithm in the last two decades in various fields e.

Proceedings of the 9th International Conference on Educational Data Mining 3 The research questions which drive this paper are as follows: How can the proposed swarm intelligence technique SDS be applied to educational data? What kinds of social learning activities, and what combinations of social learning activities are the best predictors?

Does social interaction data contain strong predictive potential of student success? In this paper, first Stochastic Diffusion Search SDS algorithm is explained, detailing its behaviour and highlighting one of its main features i.

Then, an introduction is given to the classification problem in general followed by a brief section on the nature of the educational dataset used in this paper and the features available from the dataset.

After elaborating on the data in the datasets in the context of the work, the swarm intelligence algorithm used is adapted for the purpose of the experiments conducted in this paper and the results are reported.

A discussion on the behaviour of the proposed algorithm is presented showing its potential in using all the available features as well as identifying the most significant features.

Finally, the paper is concluded with the summary of the research reported in the paper along with directions for future research. This topic of research is of importance because, for example, only in the United States several hundred thousand students drop out of high school every year and perhaps interventions can provide the means to reduce the number of those falling behind in their studies [1, 7].

With the growing interest in MOOCs as alternative or adjunct learning platforms, behaviour prediction has attracted the attention of many educational data analyst, such as Brady et al. The predictive power of demographics versus activity patterns in MOOCs are discussed by Brooks et al.

Psy 270 wk7

Other researchers, such as Coleman et al. In addition to attempting to improve students performance, Yang et al. Considering the above recent work, it is evident that extracting useful knowledge from education data should ultimately be incorporated in the design of the online systems.

In another work, by Rollinson and Brunskill, [12] emphasis has been put on the importance of coupling predictive models with an alternative student model and policy which constitute the core of the Intelligent Tutoring Systemsfocusing again on the importance of using predictive models along with other tools.

Having mentioned the above research, it is important to state that arguably one of the important features in MOOCs is enabling learners to discuss their work with their peers and receive feedback.

In a recent research, Olsen et al. Tightly related to the mentioned work, the importance of social centrality in the context of MOOCs is discussed by Dowell et al.

While this work does not endorse or reject the impact of social learning, it clearly shows an increasing interest in exploring the impact of collaborative learning.

SDS, as a multi-agent population-based global search and optimisation algorithm, is a distributed mode of computation utilising interaction between simple agents. Its computational roots stem from Geoff Hinton s interest in 3D object classification and mapping and its applications span from continuous optimisation to medical imagining.

The SDS algorithm commences a search or optimisation by initialising its population and then iterating through two phases: In the test phase, SDS checks whether the agent hypothesis is successful or not by performing a hypothesis evaluation which returns a boolean value.{VERSION 4 0 "SUN SPARC SOLARIS" "" } {USTYLETAB {CSTYLE "Maple Input" -1 0 "Courier" 0 1 0 0 1 0 1 0 0 1 0 0 0 0 1 }{CSTYLE "2D Math" -1 2 "Times" 0 1 0 0 0 0.

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