Kashiva

MISSION

We are connecting people to participate in the sustainable development of their communities.

VISION

We see a future in which our communities are shaped by the positive contribution of all stakeholders.

CORE VALUES

Community-led development

Communities need the ability to organize all kinds of capital — cash, social networks, volunteers, advocacy — to achieve sustainable development. 

Social Impact Leadership

We are leveraging technology to build an exciting community of social impact leaders.

Collaboration

We are unlocking the transformative power of collaboration to deliver on sustainable development objectives.

The Team Lead is Kashiva's Chief Executive Officer.

His role is to plan, develop, implement and direct Kashiva’s strategic, operational and fiscal performance.

He is also the product owner for WorkBook.

The Team Lead is well-positioned to effectively support the LEAP Project because he is the product owner for WorkBook; a top priority for Kashiva.

He is working with Kashiva's team to ensure that WorkBook delivers on its promise.

As Kashiva's CEO, he is able to allocate resources, including budget and personnel, to effectively support the LEAP Project.

As WorkBook's product owner, his knowledge of the product and close collaboration with users and product team would make a significant impact on the successful implementation of the LEAP Project.

He is supported by a product team whose involvement in WorkBook's design, development, and improvement processes makes them well-positioned to effectively support the LEAP Project.

He and the team will appreciate the opportunity to work with LEAP fellows to document the proof of WorkBook's effectiveness and leverage on this strong evidence to grow the product's adoption and build strategic partnerships for Kashiva.

Facilitating tech-enabled low-cost personalized instruction to close learning gaps for primary school children ages 5-12.

Students often experience gaps in their learning when a particular concept or subject taught in school was not fully understood.

In everyday practice, teachers can provide students who are struggling in their learning with extra support. However, higher student-to-teacher ratios in low- and middle-income countries make it difficult for teachers to personalize learning. As a result, some students develop learning gaps. A significant number of students with learning gaps do not learn effectively and hence become disengaged. These learning gaps can eventually lead to students dropping out, with low-income learners at greater risk.

Low-income students are less likely to pass exams in math, and reading. A study by AEC Foundation, Early Warning Confirmed, found that nearly a quarter of students who don’t read proficiently by the end of third grade won’t graduate from high school on time, compared to 9% of children with basic reading skills and only 4% of proficient readers. Low-income students are less likely to graduate from high school, less likely to enroll in college, and less likely to graduate from college. Once they are adults, they’re more likely to be unemployed and likely to earn less.

Analysis by McKinsey & Company, The Economic Impact of the Achievement Gap in America’s Schools, found that narrowing the achievement gap between low-income students and their high-income peers would have added up to $525 billion (or 4%) to the GDP in a single year; closing the gap for low-income students could add $670 billion (or 5%) to the GDP in a single year.

By 2040, the global economic impact of learning gaps could lead to annual losses of $1.6 trillion worldwide, or 0.9 percent of total global GDP.

In low and mid-income countries, an ambitious, coordinated effort is required to close learning gaps for low-income students.

SOURCES

McKinsey & Company, The Economic Impact of the Achievement Gap in America’s Schools (2009)
NAEP, Grade 4 and 8 – reading
NAEP Grade 4 and 8 – math
AFGR (grad rate)
Percent of recent high school completers enrolled in 2- and 4-year colleges
4-year college graduation rate
Unemployment Rate (BLS) – quarterly averages Median Weekly Earnings (BLS)
Annie E. Casey Foundation, Early Warning Confirmed (2013)
James Heckman, Schools, skills, and synapses (2008)
McKinsey & Company, COVID-19 and education: An emerging K-shaped recovery (2021)

WorkBook is an online remedial learning program aimed at closing learning gaps for primary school children in low-income communities through personalized instruction.

It identifies the topics and exercises where the students are currently struggling in literacy and numeracy and works with the tutors and families to get them on track.

Approach

To close learning gaps, WorkBook uses a three-step approach to identify, report, and resolve.

Identify:

It analyses the student's previous and current classroom workbooks to identify topics and exercises where the student is struggling.

Report:

It provides educators and families with a dashboard to monitor the student’s current learning gaps.

Resolve:

Educators and families work with recommended materials to close identified learning gaps.

Components

WorkBook is made up three purpose-built components:

Phone Stand:

A purpose-built phone stand that positions the mobile phone to serve both as a learning screen and a document scanner. The scanning of used worksheets is done remotely by the tutor with minimal input from the learner.

Reporting Dashboard:

A web application dashboard (for tutors and families) that shows the topics and exercises where the student is currently struggling. It also shows recommended lessons that can help get the child on track.

Online Tutors and Learning Materials:

Workbook provides the specific online learning materials and remedial classes recommended by the reporting dashboard so that learning gaps can be closed in hours or days.

Learning Variability

WorkBook is designed to contribute to our understanding of learning variability in two phases of data capture and analysis.

Phase 1: Data Capture

By identifying topics and exercises where each student is struggling over time, WorkBook is able to capture within-person variability data in real-time.

Phase 2: Data Analysis

This data when analyzed alongside other information about each student can enable us understand why a child may struggle today with a math concept she seemed to know yesterday.

Foundational research (literature reviews, desktop research).

Thus far, research evidence on personalized learning is sparse. From 2015 to 2018, a team of RAND Corporation researchers led by John F. Pane, conducted the largest and most rigorous studies of student achievement effects of personalized learning to date.

They found that 11,000 students at 62 schools trying out personalized-learning approaches made greater gains in math and reading than similar students at more traditional schools. The longer students experienced “personalized-learning practices,” the greater their achievement growth.

Despite the positive results on average, the study revealed some reasons for concern. Estimated effects for individual schools varied widely, including negative effects for some of the personalized learning schools. In mathematics, schools were estimated to produce significant effects ranging from +14 to -17 percentile points. In reading, the range was +16 to -19 percentile points. In both subjects, seven schools had negative effects strong enough to be statistically significant.

If personalized learning was responsible for the modestly positive average effects, it is clear that it did not work equally well in every school. There are many possible explanations for this disparity, including the specific models each school designed or implementation challenges identified, such as intensive demands on teachers’ time, difficulties integrating data from multiple technology systems, and tensions between personalized learning practices and state or local policies. Many of the same challenges were echoed in a more recent study of personalized learning implementation conducted by the Center for Reinventing Public Education (that study did not examine achievement effects).

Implementation challenges and wide variations in achievement results raise key questions about the effectiveness of personalized learning for improving student outcomes. In a rigorous evaluation that enables causal attribution, would personalized learning lead to meaningfully positive student outcomes? What specific strategies or models work best? Which are not effective? What contextual factors are important to the success of personalized learning? Do the answers to these questions vary across different student populations or subgroups? How can we minimize the risk of negative effects? These and related questions will take many years to address.

There are aspects of personalized learning that seem to hold promise for improving the K–12 education system, based on some limited research.

However, more work is necessary to establish causal evidence that the concept leads to improved outcomes for students. And because personalized learning is composed of so many interrelated strategies, considerable additional research will be needed to sort out the fine details of which strategies, and in which combinations, are most effective for which students.

Presently, early implementers of personalized learning are working with imperfect evidence, underdeveloped curricular resources, and policies that might hinder their efforts.

As personalized learning approaches become widespread, there are risks that these conditions may cause early implementations to fail. This could lead to the larger concept being abandoned before it can be tested under more-favorable conditions. As a protection against these risks, implementers should use some guiding principles to help discern the aspects of personalized learning that are most likely to lead to success. Following these principles could increase the chance that early efforts are productive, which will help to spur momentum for the development of the tools necessary to sustain personalized learning and put it on a path toward meeting its full potential as a major reform of the K–12 education system.

Principles to Guide Personalized Learning Adoption

In the absence of comprehensive, rigorous evidence to help select the personalized learning components most likely to succeed, what is the path forward? Pane suggested five guiding principles aimed at using existing scientific knowledge and the best available resources. I have listed three of these principles and how we applied each to our solution.

Focus on the Productive Use of Student Time and Attention

Student time and attention might be the most valuable resources for a student’s education; learning simply cannot occur without them. If a student expends time and attention on activities that are distracting or otherwise unproductive, those resources are permanently lost. Moreover, even productive activities have an opportunity cost: What else could the student have learned while expending the same time and attention?

Application: We focus on topics and exercises where the student is struggling.

Maximize the Productive Use of Teacher Skill

Teachers are the next-most-valuable resources available to students when their skills are properly focused on providing instruction and related support to students. Successful personalized learning strategies or models likely will be designed to conserve teachers’ time and effort for activities that are most directly helpful to students.

Application: Our system reduces teacher’s reporting and administrative work by seventy percent.

Use Rigorous Instructional Materials

Decades of work have gone into developing rigorous academic standards and aligned instructional materials. But teachers trying to personalize learning naturally seek out new materials and lessons. These materials often are not carefully evaluated for rigor and can lack coherence when cobbled together. Before shelving traditional materials, educators should consider how they might be redeployed in a personalized learning classroom, supplemented by strategies to meet such personalized learning objectives as increased motivation and agency.

Application: Our solution uses the approved classroom workbooks.

We are at the prototype stage and need a clear proof of concept to validate our solution.

At this stage, we need to establish (with causal evidence) that our solution leads to improved outcomes for children ages 5-12.

Providing this strong evidence base at this stage is needed for:

1. Proof of Concept

Strong evidence of effectiveness at this stage serves as proof of concept. It showcases the feasibility and viability of our solution, bolstering confidence in its potential success. It is crucial for generating interest, support, and partnerships that are essential for scaling our solution and driving broader impact.

2. Early Validation

Gathering evidence at this prototype stage helps validate the viability and potential impact of our solution. It provides an opportunity to assess whether the core features and mechanisms are indeed effective in addressing learning gaps. Early validation allows for adjustments and fine-tuning of our prototype to ensure it is on the right track before significant resources are invested in full-scale implementation.

3. Stakeholder Feedback and Engagement

Presenting strong evidence of WorkBook's effectiveness at this early stage encourages stakeholder engagement and feedback. Parents, educators, and other stakeholders can witness it's potential benefits and impact firsthand. Their involvement in the evaluation process provides valuable insights and perspectives, helping to identify any necessary modifications or enhancements to optimize the prototype for real-world implementation.

4. Iterative Development

Collecting evidence of effectiveness during this prototype stage allows for iterative development and refinement of our solution. By evaluating the prototype's impact on learning outcomes, user feedback, and engagement, the development team can make informed decisions to enhance and optimize the solution before scaling it up. Evidence guides the iterative process, ensuring that subsequent iterations of the prototype are more effective and aligned with the desired outcomes.

5. Securing Funding and Support

Strong evidence of effectiveness during this stage strengthens the case for securing funding and support from investors, grant providers, or philanthropic organizations. Demonstrating early positive results and potential impact increases the credibility and attractiveness of our solution. It instills confidence in potential funders or partners that their resources will be allocated to a promising and impactful initiative.

A strong evidence base at this stage will give us the necessary validation and groundwork to move forward with confidence, ensuring that WorkBook has a higher likelihood of making a meaningful difference in closing learning gaps.

We would like our LEAP Project to help us answer the following research questions:

1. In a rigorous evaluation that enables causal attribution, would our personalized learning model lead to meaningfully positive student outcomes?

2. What and how did each component of WorkBook contribute to the meaningfully positive student outcomes?

During the LEAP Project sprint, our desired outputs would be:

1. A causal attribution research design to determine the impact of WorkBook on learning outcomes.

2. A research design to understand the role of each component of WorkBook in producing the learning outcomes.

We look forward to having research recommendations, guidance and strategies that:

a.) Provides a systematic investigation to determine the causal relationships between our variables.

b.) Focuses on understanding the cause-and-effect relationships between specific components and their effects on outcomes.

c.) Identifies and establishes the components that are responsible for producing a particular outcome.

d.) Examines the extent to which a specific variable influences another variable of interest.

e.) Determines whether an observed relationship between variables is a result of a causal effect or is simply due to chance or other factors.

f.) Attributes improved outcomes to specific causes rather than mere associations or correlations.

g.) Provides evidence for causality by controlling for potential confounding variables and using appropriate research methodologies.

h.) Establishes a cause-and-effect link by systematically manipulating independent variables and observing their effects on dependent variables.

i.) Involves the use of control groups to compare the effects of our solution to a baseline condition.

j.) Provides deeper insights into the factors that produce desired outcomes.

At the conclusion of the LEAP Project sprint, the research designs, recommendations, guidance and strategies produced by our LEAP fellows will be used in the testing and pilot of our solution to establish causal link between our solution and the learning outcomes.

This outputs will be used to establish causal attribution at two levels:

1. Solution level

At the solution level, we want to know whether the observed improvement is caused by our solution.

2. Component level

At the component level, we want to know what and how each component of our solution contributed to the observed improvement in learning outcomes.

We are looking for strong evidence base to prove our concept, grow the adoption of WorkBook, generate partnerships for Kashiva and hopefully create new jobs for remedial lesson teachers.

SHORT-TERM OUTCOMES

1. Strong Evidence Base

Our desired short-term outcome is to strengthen the evidence base of WorkBook’s effectiveness.

2. Proof of Concept

We want to demonstrate the effectiveness of WorkBook through a stronger connection to evidence.

LONG-TERM OUTCOMES

1. Growth

Our desired long-term outcome is to leverage the strong evidence base of WorkBook's effectiveness to grow it's adoption and attract new partnerships for Kashiva.

Introducing lower prices can make personalized instruction more affordable and accessible to a wider customer base. This expanded market can lead to increased demand and revenue, driving business growth.

2. Job Creation

Increased demand for personalized instruction due to new pricing would generate a domino effect across the supply chain. More educators will start offering the service using our model to meet the growing demand. This, in turn, can create new job opportunities.