Integrated Learning Analytics for Personalised Experience

Students, Teachers and Parents mostly get into conflict due to wrong choices made in the course of learning. Determining a learner's preferences in terms of learning styles, talents, and career paths has been of great challenge. This affects students during subject selection, formation of study groups, and during career choices. The teachers are also affected in determining the right teaching method. This therefore calls for a solution that will make the process of determining the learning preferences easy.

Teachers, parents and students tend to carry out methods of determining individualized learning preferences. During teaching, the teachers walk between rows and desks, and select students to subjects based on performance. On the other hand, students tend to follow their friends' preferences, while the parents tend tend to force learners to take a career path similar to that of a neighbour or just their preferences. this therefore begs the question as to whether these methods lead to the right data and information. there is therefore a need to have a system that will guarantee collection of data that lead to individualized preferences and learning paths

To result into a personalized learning paths require that the solution integrates submitted data and extracted data into arriving at a learning pattern. The solution integrates two data sources and uses decision tree modelling to determine learning patterns. An online questionnaire is used to collect student data on relevant learning experiences. By use of Rapid miner software, decision tree modeling is then carried out to determine the learning patterns. Finger print patterns are then collected and analyzed using DMIT software. The results from the two processes are then integrated into one rule set that results into individualized experience patterns. Therefore, collection of data from the two sources, analyzing them then integrating the two will move a great step forward in implementing personalized learning experiences.

The solution serves the students in schools, teachers, and parents at home. Currently, students follow what their friends are doing and their attitude towards the teacher. The parents on the other other hand tend to force their children to do the same courses that they themselves had desired to do before. These measures can be said to bring conflicts between parents and children as a number of attributes are not considered.

This solution addresses their needs by providing them with information from the students opinions and views, and from the fingerprint data. The rapid miner software and the DMIT software then help to analyze these data, which are then integrated to come up with individualized learning paths.

Implementing this solution also requires well positioning of the implementing team. I am a teacher and therefore in a position to link up with learners, other teachers and parents. I n the pilot phase, the data collected from the teachers, learners and parents help determine their expectations and needs.

They are engaged through the use of the questionnaire where they give their views of the learning preferences. The career masters who advise students on subject selection and careers are also well engaged to digest the information on the determined patterns, then advice the students accordingly.

I am applying so as to secure an opportunity to be a solver. If selected a Solver, I look forward to joining a cohort of peers that will be offering inspiration and guidance on more solution projects. I further look forward to link up with impact minded leaders across industries and sectors to form a synergy. This will further help access mentorship, coaching and strategic advice from experts, and Solve and MIT networks.

Its my wish to get support from these networks in terms of monitoring and evaluation to an impact measuring practice, gaining exposure in media and conferences, and finally, to access resources such as software licenses ans legal services.

I finally wish to recieve the support of funding of $10000, to help expand the impact activities.

There hare various methods of analysis that go on in the learning system. However, my solution tends to integrate two different sources to provide personalized learning paths. Data is collected based on examinations and analyzed as stand alone data. Teachers use this data on performance to create learning groups, place students in various subjects. However, what makes this solution innovative is use of data from questionnaire, and from finger print, to come up with individual learning preferences

Impact of my solution

-Facilitates stress-free childhood  

-Reduces time, money and efforts wasted over irrelevant courses and classes 

-Accesses your child’s quotients  

-Develops child’s confidence

 -Determines child’s learning and acquiring method

 -Improves relationship between parent and child

 -Helps your child to choose right career path

How to ensure them

By use of Questionnaire data, where learners are subjected to several questions that require their input on what goes on in their educational contexts. The set of responses are then fed into the rapid miner software, where analysis is carried out using decision tree modelling, and DMIT, which is a finger print analysis that works by scanning the finger prints. The ridges on our fingers can be related to the functioning of the brain and this relationship is developed at the embryonic stage.

The test takes 10 minutes and it involves the placement of the fingers
on a scanner which is connected to a software that takes fingertips
impressions. The report is generated and you get to know about the
strength, weakness, learning style, acquiring style, quotients, career
path, behavior pattern of the child as well as the inborn talent.

DMIT can be instrumental in finding out how the brain of a particular child functions- whether he would be interested in science or arts, in dance or sports, will he respond more to light or sound or would assimilate more without much external stimuli and from nature. This is then integrated together with Questionnaire data. A learning pattern that suits an individual student is then generated.

It can be definitely said that a lot of misunderstandings can be avoided between parents and children in this regard. Also, it has often been seen that quite a few years are wasted unless a child discovers his true calling- such wastage can be reduced and the child would find a proper place at the very beginning.

Many parents and other adults often regret that they have wasted precious years of their early life in pursuing something at first they were not cut out for and they do not want their children to have the same fate.

Progress indicators

-Observing stress level during childhood  

-Reduced change from one course to another, changing of subjects after selection process

-Number with access to a child’s quotients  

-Level of confidence developed in children with education

 -Determined child’s learning and acquiring method

 -Improved relationship between parent and child  

Data mining Software

Finger Print Scanner

DMIT software

Incorporation of different gender, and professions

Key Resources

Finger print scanner

DMIT

Rapid miner software

Senior masters

Class teachers

Students

Parents

Key Activities

Needs Assessment

Preparation of questionnaire

Collection of data through questionnaire

Training of stakeholders

Collection of Fingerprint data

Analysis of data

Creation of patterns

Type of intervention

This is a product intervention

Segment

Schools are the customers

Value Proposition

Facilitates stress-free childhood

-Reduces time, money and efforts wasted over irrelevant courses and classes

-Accesses your child’s quotients

-Develops child’s confidence

-Determines child’s learning and acquiring method

-Improves relationship between parent and child

-Helps your child to choose right career path

Partners + Stakeholders

Schools

Churches

Ministry of Education

Channels

The clients will be reached through the class teachers, and career masters

Cost Structure

The most expenditure areas are : DMIT software, Training, Rapid miner server set up. Facilitation of personell on the ground.

Surplus

Surplus will be invested in more research for better programmes

Revenue

Initial setup   30%

Analysis of learner information and generation of reports 70%

The children will be required to pay 100 kenyan shillings for the analysis f data, in order to get their personalised report. Given the number of enrolments each year, the amount raised will be able to sustain the operations of the organisation.

This is the first programme where I intend to have financial sustainability