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Eduspace : Space For Education


Published on Jan 30, 2019

Abstract

The main aim of this project is to provide the students the better training for placement activities by providing articles and videos regarding preparations for placement activity. It also helps the placement cell within an organization to identify the prospective students and pay attention to and improve their technical as well as interpersonal skills. A number of companies place importance on the academic performance of the candidate and place requirements for minimum marks. Students can also take mock tests and they are able to view their results. Online attendance facility is provided. The dataset used for training students is obtained from the database of the placement cell in the institution.

Keywords: - IOT(Internet of Things), android, Educspace

Introduction:

The role of technology, in a traditional school setting, is to facilitate, through increased efficiency and effectiveness, the education of knowledge and skills. In order to fully examine this thesis, we must first define several terms. Efficiency will be defined as the quickness by which we obtain knowledge, while the term effectiveness is associated with the amount of imparted knowledge that is operationally mastered. When technology is directly applied to an educational setting, such as a school, both the students and teachers can be viewed as learners. Thus, we can operate under the assumption that any increase in teacher knowledge and utilization has the impact of increased learning in students. Ultimately, technology should serve to increase student achievement in schools. Technology can aid in educational achievement through two primary methods: the removal of physical barriers to learning and the transition of focus from the retention of knowledge to its utilization. Each of these methods must be examined in the context of their relation to both the student and the instructor in order to see their value and effect in educational settings.

Technology plays a crucial role in today’s education system. Mobile learning has become widespread, and higher educational institutions have started adopting mobile technology to cope with the needs of students. It allows students to access learning content from various locations with no time. The goal is to design and develop an application that assists students and educational institutions to enhance placement rate in the institution. The application users are now able to receive and access instructions through several platforms, such as mobile apps, web-based, or combined mobile learning environments.

Objectives:

1. To enhance placement rate.

2. To assist students in preparing for placement tests.

3. To assist rural students to get better placement opportunity.

4. To provide an one stop solution for students looking forward for a job.

Methodology:

Processing of the data

In academic institutions like colleges, universities data increases day by day and storage becomes difficult. Data like student’s attendance, internal marks, external marks, his personal details, health details will be coming and the entire data has to be stored in a database. So, in order to store data there are data bases in which various tupels are created and data is stored into them. Problem of storing is solved but now a problem arises that, how to accesses the stored data. When a particular data to be extracted how to extract it from data base. Data mining technique is used, It is a phenomenon in which we can retrieve the relevant data which is called as Knowledge discovery in database (KDD). Data mining procedures are those which help us to extract the data from heavy or large data bases. At present techniques related to data mining are being used in academic institutions. There are many data mining procedures or approaches like clustering, classification, decision tress, association rules, outlier analysis, regression, pattern mining and so on. The methods classification and decision tree are being used in academic or Educational data mining.

Data processing undergoes two types of functions namely clustering and classification. Either clustering or classification can be applied to the dataset. Generally the data knowledge discovery involves certain steps like cleaning of data, integration of data, selection of data, conversion of data, evaluation of arrangements and finally representation of knowledge. In data warehouse information is collected from various sources and stored. Multiple data are stored. Data warehouse can be viewed in four different ways namely top-down view, data warehouse view, data source view, and business query view. In multidimensional data mining, usually search for interesting patterns among various multiple combinations of data. Functionalities of data mining are used to tell us the types of patterns present in the tasks of data mining. Noise may be present in the data, they have been organized very carefully.

Classification:

The act or process of class systematic arrangement in groups or categories according to established criteria. It is a general process related to categorization, the process in which ideas and objects are recognized, differentiated, and understood. A classification is a division or category in a system which divides things into groups or types. It simplifies our environment. The system helps us to reduce chaos and confusion among the things that surround us. Classification assigns things in a collection to classes or groups. The aim of categorization is to anticipate accurately to the target class or group for each and every case within the knowledge. The data of the students must be efficient, and it should be maintained correctly within the educational institution.

The approach begins from the collection of data of graduate students, and later the pre-processing procedures are tested to the dataset. The data pre-processing approach is utilized to make the data more deserved for data mining .Choosing of trait is mostly utilized to minimize the data dimensionality. The principle thought of choosing the element is to acknowledge a subset of info factors by disposing of or ending highlights with no prescient or little data which is important in model improvement and to deliver better execution. Finally the dataset is classified into a training data set and a testing data set.

The dataset which is trained is utilized to compose the classification model. The dataset which is treated as testing dataset is either utilized to examine the assessment of the developed order demonstrate or to contrast the forecasts with the known target values. The dataset containing student marks are analysed in the form of decision tree by considering a constraints as 10th percentage and 2nd PU percentage once and, B.E 1st year marks, 2nd year, 3rd year and 4th year marks . The results of these 3 decision trees are analysed and process continues till final decision tree appears. The decision tree which is generated finally results to the forecasting of the student job.

Decision tree:

The concept used is decision tree analysis. A decision tree which is a decision making support tool which uses a tree-like design of decisions and their feasible importance, which includes chance event results resource costs, and utility. It is one way to display an algorithm. A tree can be "learned" by dividing the source set into various subsets basing on an attribute value test. This process is repeated on every subset which is derived in a way of recursive manner called recursive partitioning. The recursion is said to be completed when the subset at a node has all the values same as the target variable, or while dividing no longer increases the value of the expectations. A few systems frequently known as ensemble methods, which ambiguous decision tree. In decision tree every node mean a choice of number of substitutes and every leaf node mean a decision.

Filtering:

Selective presentation or deliberate manipulation of information to make it more acceptable or favorable to its recipient. The filtering process helps to filter the students based on their aggregate obtained, the data which are obtained from the institution is carried out for the classification is further carried out for filtering where the students who are not eligible for attending the placements are filtered out.

Hardware specification

 Processor : i3 and above

 Processor speed : 2.9 GHz

 RAM : 3GB and above

 Hard disk space : 20 GB+

Software specification

 Framework : Model View Controller

 Operating System : Windows

 Design Tool : Net beans and Android studio

 Front End : HTML, CSS

 Back End :Java

 Database :MySQL

 Internet : Google Chrome

 Software : SQL yog 9.3.2-0 Community, Android Studio,NetBeans-8.0,mysql- essential 5.1.61- win(32),jdk-7u45-windows-i586

Block Diagram:

Eduspace

Results

The present study has been made to suggest and develop some tools which will eventually be useful to the students, educational institutions, placement officers for timely help for the students to get placed. The project targeting towards sorting, designing, implementing and operating facilities and services that were traditionally provided by the application for the improvement of placement activities of students.

Future Scope

The work was carried out by analyzing the data of students belonging to our college student details alone. It will be of great benefit to the institution if the particular work can be spanned to the other institution such that the data may reflect different cultures, different characteristic as a whole and can be generalized in a better way. Similarly one can analyze the data from other domains like medical, legal and other P.G studies to get some interesting hidden information. Since the nature of data may be different, the deciding attributes and the data mining models may vary giving rise to more contributions to the data mining field.

Another area to which this research work can be extended is association rule mining where new interesting hidden patterns can be found from the data. Assuming a student is opting for computer science branch and is given admission in a particular college, it can be investigated whether he has better chances of placement. Finding out the information about dropouts from Engineering courses and finding the reasons for it can be a useful future work.

Similarly one can investigate whether going for higher studies is giving any extra push for the employment chance of a student. As a summary when data mining techniques are effectively applied over authentic, up-to-date, national level data base, many hidden and useful information can be retrieved, which can be efficiently used both at government and general public level for future planning policies. The current application consists of videos, articles related to the placement it can be added with more contents to make much efficient.

Conclusion

The application provides the articles, videos, technology information to the students who are in the final year of their engineering and help them to crack the respective rounds in the placement. It also helpful for the students to gain knowledge related to their placement. The application mainly concentrates on sorting of the students based on their percentage or CGP gained in their academics, it is application easier for the topmost companies where their profile will be displayed through which students can get notification from the company so that the students who are eligible for their placement can able to get notification so that they can able to take the test as well as they can prepare for the placement test where the articles videos are available and the notification will be sent to the students via gmail for registered students.

References

[1] Bratton-Jeffery, M.F., Hoffman, S.Q., Jeffery, A.B. (2007). Trends and Issues in Instructional Design and Technology Reiser, R.A. & Dempsey, J.V. (Eds.). Upper Saddle River, NJ: Pearson.

[2] Dempsey, J.V. & Van Eck, R.N. (2007). Trends and Issues in Instructional Design and Technology Reiser, R.A. & Dempsey, J.V. (Eds.). Upper Saddle River, NJ: Pearson.

[3] Driscoll, M.P. (2007). Trends and Issues in Instructional Design and Technology Reiser, R.A. & Dempsey, J.V. (Eds.). Upper Saddle River, NJ: Pearson.

[4] Kirkpatrick, D. (1996). Great Ideas Revisited. Training and Development.

Project Done By Ms. Kruthika M B, Ms. L Gaanashree, Ms. Nikhitha M S, Ms. Vidhursha P






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