Author: Niko

Table of Contents

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LessonPlan 2.0

Market Analysis

Design and Engineering

(Pre- and Post-)Production considerations

Contextual factors

Project Management

Future functionality

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Version 1 of ā€˜LessonPlanā€™ will be released as a full functioning platform, but due to time constraints more advanced features will be implemented in future versions.
Some of the planned features are detailed below. The extra features simplify and make more robust some of the functionality of the platform, but all said functionality is completely operational in version 1 without them.

  • Suggestion algorithm
    One of the main goals of ā€˜LessonPlanā€™ is to facilitate module selection, allowing students to make more informed choices when selecting optional modules. This is based on the premise that providing students with information apart from the limited details of the module overview pages ā€“ and especially feedback on the practical applications of the module descriptions ā€“ will allow them to have a clearer picture of the moduleā€™s learning outcomes and requirements. In the initial release of the platform this is largely based on the inferences that each student can make from the feedback, comments and discussions that other students have posted. Implementing a suggestion algorithm would offer some automation on this task: The algorithm will be able to notice patterns in module selection and relate them to satisfaction. It will then be able to offer those observations as suggestions. For example, if a student A has rated Module X and Module Y as very interesting in Semester 1, and rates Module Z as very interesting in Semester 2, there’s a probability that students that have also found Modules X and Y interesting will have an interest in Module Z as well.

 

  • Postsā€™ ratings
    As detailed in the post about Trust here, implementation of ratings for comments and discussion posts is intended in the future. This would allow users to up-vote a post that they found particularly helpful, useful or interesting. That would provide new users with an instant indication about the validity of the post, while encouraging users to post more responsibly.

 

  • Ā Flags for abusive content
    For the initial release of ā€˜LessonPlanā€™ it was decided that no active monitoring or moderation of posts was needed to keep content clean and clear. From studies of Graph Theory on Online Social Networks it has been noted that an accentuated characteristic of a network shapes the behaviour of its members. Since members of the platform will be University students, with their credentials logged into the system, it is a safe assumption that the majority of them will behave ethically, in line with the Acceptable Use policies. This then, as a network effect, constrain potentially deviant behaviour from other members. This assumption is re-enforced by what is detailed in the post about Trust: Where social capital and trust exist, members can rely on informal fairness rather than constant active monitoring and exhausting rules of provenance.
    Nevertheless, future versions of ā€˜LessonPlanā€™ can address this issue if need be. The feature will allow users to flag up abusive posts. Posts that gather a certain amount of flags, will be marked for inspection. And after a certain amount of flagged up posts, prevention policies could be set in place (for example, warnings, suspension or even deletion from the system).

Trust considerations

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In Online Social Networks (OSNs), trust is a key element, serving a connecting function between (initial) strangers. ā€˜Lessonplanā€™ has manyĀ OSN elements, and, therefore, issues of trust- and rapport-building should be considered.

Web 2.0 environments and social media platforms can cause situations of uncertainty, as they usually provide some level of anonymity. Even though this topic is the subject of a large volume of literature,[1] for the scope of this post we will only look at how trust serves as a mechanism to reduce the uncertainty of users in online interactions, e.g. in disclosure of personal opinions.

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Data protection

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Stemming from the previous post about Privacy Settings, the platform needs to conform to various guidelines and policies on Data Protection. The protection of personal data (and especially sensitive personal data) is in large mandated by the Data Protection Directive (Directive 95/46/EC), which was subsequently implemented in national legislation of the Member States. In the UK, it was incorporated into the Data Protection Act 1998 ā€“ with amendments pending to reflect the newly reformed Directive.

Under the DPA, any organisation, site or provider (data controller) who gathers user information needs to specify and inform the users on what data are being collected, how they are used, who has access to them and long these data are preserved.

Since this project aims to be in close relation to the University and its network, it is necessary to not only adhere to the DPA but also to more specific policies implemented by the University. Continue reading →

Privacy considerations

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Privacy is always a concern in social media; especially in systems hosted in or accessible by your workplace, where wrong privacy settings can cause trouble. If we consider Facebook, for example, there have been many incidents where accidental over-sharing with an employer led to severe implications at work. In our case the platform is student-centric and the bulk of information exchange relate to academic performance. Fear of unwanted access to personal information, from a lecturer for example, could lead to hesitation from the users to share content.

For this reason, it was decided that any decisions on privacy will be dealt with by the user. Continue reading →

‘LessonPlan’ as a game

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Game Theory is the mathematical modelling of decision making processes. In simple terms, it uses mathematical expressions to analyse strategies employed by players in order to maximize their payoffs from the game. When applied in a social perspective, game theory can model individualsā€™ behaviours (strategies) in relation to the pursued benefit (payoff). In the context of Online Social Networks (OSNs), Game Theory is used to study user patterns in structural balance (inter-personal relations that have produced gains in the past), strategies for social decision problems and evolution of co-operation in dynamic environments.[1] Prediction of behaviour can be immensely valuable in the designing of an OSN. After all, Social Networks rely on user interaction to derive content. In the case of ā€˜LessonPlanā€™, user interaction is what will provide the added value on top of the initial information about the modules. Through module feedback, commentary on the practical aspects of teaching or coursework, students will be able to collaborate, solve problems, improve their performance and communicate necessary or desirable changes to the academics and the University. In other words, social interaction in the platform is essential for students to derive gains from it. Below we will consider the likelihood of random students to see this potential value of participating in the platform.

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Survey conducted on Module Feedback opinions and interests

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In order to confirm interest in the suggested platform, as well as any inefficiencies of the outlets already out there by the University of Southampton, we have conducted an online survey.

The survey was introduced by a short text, informing the participants on what the surveyā€™s goals where, which aspects of module feedback they would be asked to give their opinions on, as well as a brief description of the suggested project and its aims.

The questions were, therefore, divided into three different parts. Questions in part one aimed to discover opinions and satisfaction about the official module feedback forms that the University provides. Users were asked to rate both its usefulness and its effectiveness on module planning. Part two was designed to discover which factors are considered when students select an optional module. Some of those factors are informed by the module feedback forms (such as Coursework style or Student satisfaction, for example), while others were additional (such as the reputation of the lecturer). Participants were given the option to include any additional factors in an ā€˜Other:ā€™ free text field. Finally part three was investigating interest in the suggested platform (ā€˜Lessonplan 2.0ā€™) and opinions on its features and ambitions.

The results of the survey re-affirmed our assumptions about the deficiencies of the current model and the need for an alternative solution. In summary, the survey highlighted that while half of the students are willing to provide feedback to the University, the majority of them would like some access to the results and does not trust that it has any significant impact on module shaping. All of the participants also agree that ā€˜Lessonplanā€™ would be a valuable addition, who could assist them in choosing optional modules, meeting the module requirements and co-ordinating better with their fellow classmates.

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Shortcomings of feedback and student satisfaction in Higher Education

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One of the projectā€™s main aims is to provide a platform for feedback on the courses, accessible to the students undertaking ā€“ or considering to undertake ā€“ studies at the University of Southampton. Even though student feedback is valued by the University as an integral part of the module assessment ā€“ currently being provided by the Module Feedback Surveys at the end of each semester ā€“ it is not, at the moment, a transparent process, meaning students do not have access to either the surveys data or their impact on module planning.

It is a hypothesis of this project that access to student feedback on the offered modules will assist current and future cohorts in having more realistic expectations out of the modules, adapting quicker and better to the expectations of teachers, improving their performance and making more informed decisions when choosing optional modules.

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