Personalizing Sample Databases with Facebook Information to Increase Intrinsic Motivation
Abstract
Motivation is fundamental for students to achieve successful and complete learning. Motivation can be extrinsic, i.e., driven by external rewards, or intrinsic, i.e., driven by internal factors. Intrinsic motivation is the most effective and must be inspired by the task at hand. Here, a novel strategy is presented to increase intrinsic motivation toward activities requiring the use of sample data sets, by automatically extracting the student’s social data from a social media network, to create a personalized sample data set. A tool was developed to access Facebook and create a database to be used in practical assignments on database queries. A user study measured intrinsic motivation before and after the assignment in three different groups of first-year university students. Each group had to create a set of queries of similar difficulty, with each group using a different database. Intrinsic motivation was found to be significantly higher for the group using their own social media data than for the groups using social media data from an unknown person or a business. The approach presented here can be applied to other subjects, such as data mining, image processing, or statistics.
That’s Project Implementation.(MVC).
MVC Image.
INTRODUCTION
Motivation is the process that initiates, guides, and maintains goal-oriented behaviors. A motivated student is more likely to engage, persevere, and make efforts to learn than one who is unmotivated. Motivation is usually characterized in two ways: extrinsic and intrinsic. Extrinsic motivation refers to the drive to do something because it leads to an external reward or to an outcome separated from the action. Intrinsic motivation is the determination to do something because it is inherently interesting or enjoyable. Intrinsic motivation is also referred to as interest . Extrinsic motivation has been considered an inferior form of motivation , and intrinsic motivation is considered to foster high-quality learning and creativity. Extrinsic motivation is easier to obtain through external means not related to the activity, such as rewards or punishments. In contrast, intrinsic motivation can only be conveyed from the activity itself, and thus, it is more complicated to obtain. Personalization is a pedagogic technique in which students are presented with learning materials customized to their interests, for example, by tailoring the wording of math problems to include friends’ names or objects of interest for the student. This has proven to be an effective way of increasing intrinsic motivation . By personalizing the instruction to the students’ interests and experiences, motivation and interest are enhanced. Student motivation to solve a problem can increase when names, locations, and events are changed to personal referents; the elements of the problem should pique students’ interest in solving it . It has also been shown that material is better learned and remembered when presented in connection with topics, characters, or ideas of high interest . The model of spreading interests explains this connection between personalization and interest, stating that the perceived interest of a task is the accumulation of the interest in each of the separate elements of the task. Therefore, when the entities involved in the task are of interest to the student, the task itself becomes inherently interesting. Despite its benefits, personalizing learning material is costly and time-consuming, requiring data to be gathered on students’ interests and personalized material to be created for each student. Here, the authors present and validate a novel approach to automatically create personalized learning materials in the form of sample data sets. Many learning activities need sample data sets on which students can practice, such as in statistics, where surveys provide the material on which statistical tests can be performed. In the approach presented, this material is replaced with data taken from the student’s social media data; these sample data are thus personalized to contain information about friends, hobbies, and pictures of the student. An application was developed to automatically create a sample data set with Facebook social media data, avoiding the cost and time required by previous approaches. This approach should be intrinsically motivating since the personalized data implies that the information should interest the student. In addition, Facebook’s high popularity with students suggests that they would have a positive perception of activities that involve it. The approach was validated in an experimental setting within the course Foundations of Database Systems. Database managements systems (DBMS) are the dominant data-processing software currently in use , and database courses are a part of undergraduate and postgraduate engineering-related programs . One topic covered on the course is Microsoft Access Queries The traditional assignment for this topic used nonpersonalized databases. For the purposes of this study, an alternative assignment that used students’ Facebook data was created. This alternative assignment was equivalent both in structure and in difficulty to the traditional assignment.
Modules
User:
- User login to the System.
- User is Trusted Create in Online social Profile.
- Users identify in Create profile photo.
- Users shared images.
- If original user, properly view for all templates.
Merchant:
- Merchant Login Here,
- To Search the Admin side user hobbies.
- Search the Product.
- To Add The Available Protects with Offer Price.
- View The Order Products send Delivery Address.
If original user, properly view for all Products
Admin
- Admin store all the User details.
2. Admin Store and view All Profile Details.
3. View in all shared in Photos details.
4. Untruth in view Product Details. check Address to Developed.
5. Delivery Status.
How to Find that Product?
User Identification.
Searching Merchant Details.
View Product Details.
View These Buy now the Projects.
Delivery Status user side merchant side verification.
- Database
-> Motivation (As My Database)
->I am using entity framework
Controller
- Admin controller
- User controller
- Product Controllers
There are 3 views have been created based on the Action method.
SYSTEM ANALYSIS
EXISTING SYSTEM
The pretest was completed oneweek before the intervention.During the intervention,
SelfSocial (condition 1) students used MySocialData2Access to generate a database with their own social media data.
UnkSocial (condition 2) students downloaded an existing database from the course webpage containing social media data from an unknown person.
Business (condition 3) students used the traditional database from the course webpage. At the end of the intervention, all the students completed the posttest.
PROPOSED SYSTEM
Correlations showed that the more friends a student had, the more competent he or she felt while using self-social media data. As a result, the approach presented here shows the best results for people with more than 150 contacts on Facebook. There is, however, a cognitive limit to the number of friends with whom someone can maintain a steady relationship [33]; this number is held to be about 150 people, but this is not exact, and proposed values vary between 100 and 230 contacts. The students in this study fell within this range.
Subjects such as data mining or image recognition could adopt this approach since more complex information, such as social graphs, temporal data, and images, is available on Facebook. For example, in data mining, a student could predict the favorite music groups or video games of a friend, based on the groups and preferences of other people. An image recognition exercise could have the student identify in their pictures the faces of friends and, from that information, determinewhich friends appear the most often in their photos.
Algorithm:
Searching Algorithm
To Searching that every user in view in all product.
To user side, admin side,merchant side.
Merge Algorithm
Since substrings shared by subsets of a campaign are crucial for template inference, a naive alternative is to break a campaign apart so that each part contains an invariant substring, then reuse the existing template generation algorithm.
CONCLUSION
A new approach has been presented to increase students’ intrinsic motivation toward courses that use sample data sets. The approach is based on creating personalized sample data sets from the student’s own social media information contained in a social network. The approach was applied to database queries, and intrinsic motivation was measured before and after creating queries for a database. Students using a database of self-social media information had a larger increase in intrinsic motivation than students using data from an unknown person or from a business. This is the first study presenting a strategy to personalize learning materials for university students. Furthermore, in previous studies, the personalization was superficial, only replacing some names and items, whereas this approach integrates fully personalized content into the core of the activity. The presented approach is automatic and does not require extra time for personalizing the materials or fetching data. Personalizing sample data sets with self-social media data does raise some issues, such as heterogeneity of the data obtained across students, or the concern for privacy.