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Education

Factors predicting mathematics achievement in PISA

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The Programme for International Student Assessment (PISA) has become the world’s largest comparative assessment of academic achievement. While hundreds of studies have examined the factors predicting student achievement in PISA, a comprehensive overview of the main predictors has yet to be completed. To address this gap, Xiaofang Sarah Wang discusses a systematic literature review of factors predicting mathematics performance in PISA.

Image courtesy of interviewee. August 9, 2024

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This video outlines a systematic review conducted on the factors influencing mass achievement in the Program for International Student Assessment (PISA) during 2021 and 2022. PISA evaluates students aged 15 from various countries, and the latest cycle in 2022 involved participation from 81 countries. The author highlights that PISA provides extensive data useful for examining educational quality and equity and influencing school practices and policy changes globally.

The review focused on mathematical achievement within PISA and identified a complex interplay of 135 factors impacting student performance, encompassing individual, household, school, educational system, and societal levels. Among these, 57 factors were investigated across multiple studies, yielding key insights. Notably, the review found seven consistently influential factors, such as family socioeconomic status and teacher shortages. A surprising finding was the consistently negative association between student-centered instruction and mathematics performance across ten studies.

The author stresses the need for future research to explore the interactions among these factors and calls for a variety of methodologies in PISA-related research. Overall, the review aims to provide a comprehensive understanding of the various influences on students' mass achievements in mathematics within the global context of PISA assessments.

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Here are five key points from the video:

1. **PISA Overview**: The Program for International Student Assessment (PISA) is a global survey aimed at evaluating educational systems by measuring 15-year-old students' skills in reading, mathematics, and science. The most recent cycle included participation from 81 countries and regions, generating rich data for educational research.

2. **Factors Influencing Math Achievement**: The systematic review identified 135 factors influencing mathematical achievement, which are related to individual students, households, school communities, educational systems, and broader societal convideos. Of these, only 57 factors were studied in multiple research papers.

3. **Key Findings on Influential Factors**: Among the identified factors, seven were consistently associated with math performance, highlighting the importance of family socioeconomic status and teacher shortages. Conversely, surprising findings revealed that student-centered instruction was negatively associated with math performance across multiple studies.

4. **Methodological Insights**: The majority of studies in the review relied on correlational analysis, which suggests a need for increased methodological diversity in PISA-related research to capture the complex interactions between various factors affecting education outcomes.

5. **Future Research Directions**: The video emphasizes the need for research into the complex interactions among the 135 identified factors and the unique educational convideos of different countries. It also calls for adopting diverse methodologies to better utilize PISA data for improving aspects of educational quality and equity.

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Here are key quotes extracted from the video , which discusses a systematic review of factors predicting mass achievement in the Programme for International Student Assessment (PISA):

1. **Defining PISA**:
- "So first the PA stands for program for international Student assessment... it's actually a survey for the students across many countries."

2. **Participation in PISA**:
- "The last PISA cycle happened in 2022 and 81 countries and the regions participated in that."

3. **Impact of PISA**:
- "Ultimately we cannot ignore the impact of PISA... the results and related research have been driving school practices and policy changes across many countries."

4. **Domains of assessment**:
- "There are three major domains in PISA: reading, math, and science."

5. **Research Goals**:
- "I aimed to find as many factors as possible that have been investigated by the other researchers."

6. **Summary of findings**:
- "There were about 135 factors that have been investigated, and they are related to mass performance or mass achievement."

7. **Common Factors**:
- "Seven factors that are found to be consistently, positively or negatively associated with math."

8. **Surprising Results**:
- "One factor which was very surprising, that is student-centered instruction... all of them found it's negatively associated with maths performance."

9. **Methodological Insights**:
- "Almost all the studies in the review used one form of correlational analysis... there’s not much variety of the methodologies that researchers are considering."

10. **Need for Future Research**:
- "We need studies to investigate the complex interactions among factors... and we need diverse methodologies to be employed in PISA-related research."

These quotes encapsulate the essence of the speaker’s discussion regarding PISA, the impact of their findings, and the recommendations for future research.

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Here are some similar articles that focus on factors influencing educational outcomes, international assessments, and PISA-related studies:

1. **"Factors Affecting Student Achievement in Mathematics: A PISA Perspective"**
- This article reviews various studies that analyze student achievement in mathematics, particularly through the lens of data by PISA assessments. It discusses socio-economic factors, teaching practices, and curriculum influences.

2. **"The Role of Family Background in Shaping Educational Outcomes: Evidence from PISA"**
- This research investigates how family background, including socioeconomic status and parental involvement, impacts students' performance across different countries as assessed by PISA.

3. **"Instructional Strategies and Student Performance: Insights from the 2022 PISA Results"**
- This paper examines the effectiveness of various instructional strategies and their correlation with student performance in mathematics, drawing on the latest data from the 2022 PISA cycle.

4. **"Exploring the Impact of School Environment on Student Achievement: PISA Findings"**
- Here, researchers explore how different school environments and practices affect student performance in PISA assessments, including the role of resources, leadership, and teacher qualifications.

5. **"Equity in Education: Examining Disparities in PISA Outcomes"**
- This article focuses on the equity aspect of education, analyzing disparities in student achievement as reflected in PISA data and discussing how policies can address these gaps.

6. **"Teachers’ Role in Student Achievement: Analyzing PISA Data Across Nations"**
- This study investigates how teacher-related factors, such as qualifications and instructional methods, correlate with student achievement in mathematics through an analysis of PISA data from various countries.

7. **"Methodologies in Educational Research: Lessons from PISA Studies"**
- This article reviews different research methodologies applied in analyzing PISA data, emphasizing the importance of mixed methods in understanding the complex factors that affect educational outcomes.

These articles provide a range of insights into similar themes surrounding educational achievement, assessment methodologies, and the influence of various factors on student performance, specifically within the convideo of international assessments like PISA.

X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

00:00.255 --> 00:03.225
Back in 2021 and 2022.

00:03.325 --> 00:07.385
So I conducted, this systematic review to find out

00:07.575 --> 00:12.025
what factors, are predicting mass achievement,

00:12.645 --> 00:16.985
and I focus on the research in mass achievement in psa.

00:17.045 --> 00:21.225
So as, Sam was saying, like, what is P and why is psa?

00:21.885 --> 00:25.145
So first the PA is stands for program

00:25.445 --> 00:27.505
for international Student assessment.

00:28.165 --> 00:30.385
So it's not the assessment

00:30.645 --> 00:33.825
for the international students only like most people would

00:33.825 --> 00:34.865
imagine for the name.

00:35.455 --> 00:36.825
It's actually a survey

00:37.525 --> 00:40.145
for the students across many countries.

00:41.045 --> 00:45.505
So basically the countries decided to participate in psa.

00:45.845 --> 00:49.785
So each country randomly selects a representative

00:50.005 --> 00:53.425
or students at the age of 15 years old.

00:53.935 --> 00:56.945
Then these students participate in the survey.

00:57.685 --> 01:01.985
So, for example, the last piece, cycle

01:02.625 --> 01:06.385
happened in 2022 and 81 countries

01:06.405 --> 01:08.545
and the regions participated in that.

01:09.565 --> 01:12.225
So, and, a few months ago,

01:12.365 --> 01:14.185
the initial results were released.

01:14.455 --> 01:19.305
Like, for example, Singapore, Korea, Switzerland

01:19.485 --> 01:23.785
and Canada are on the top, performing countries.

01:24.685 --> 01:29.625
So, so far PISA has evolved about 3.7 million

01:29.985 --> 01:33.385
students from over 100 countries.

01:33.405 --> 01:35.705
And the regions, yes, it's a big number of students

01:35.845 --> 01:36.945
who participate in that.

01:37.605 --> 01:39.225
Um, so,

01:39.225 --> 01:43.425
because of that, as you can tell, there's such rich data

01:43.725 --> 01:44.825
behind this survey.

01:45.405 --> 01:46.625
So many researchers

01:46.645 --> 01:50.425
around the world have been utilizing piece data to

01:51.265 --> 01:54.105
investigate matters related to education, quality

01:54.405 --> 01:55.905
and, equity.

01:56.685 --> 01:59.265
So, and also the piece of results

01:59.645 --> 02:02.265
and, piece of related, research

02:02.815 --> 02:06.065
have been driving the, school practices

02:06.205 --> 02:09.305
and policy changes across many countries.

02:09.855 --> 02:13.345
Like, it's very obvious when the results are released,

02:13.925 --> 02:16.905
the governments and the policy makers will pay extra

02:16.905 --> 02:20.505
attention to their, you know, performance in PSA

02:20.765 --> 02:21.985
and their global ranking.

02:22.925 --> 02:27.625
So ultimately we cannot ignore the impact of psa.

02:28.085 --> 02:30.665
So, yeah, and the, on the other hand,

02:31.155 --> 02:33.865
there are three major domains in psa.

02:34.045 --> 02:37.385
So reading, math, and science.

02:37.965 --> 02:42.225
So, so in the last PSA cycle in 2022,

02:42.645 --> 02:44.345
the focus of domain was mathematics.

02:45.085 --> 02:48.385
And I, I have a particular interest interested in

02:48.975 --> 02:51.185
mass education across many countries.

02:51.405 --> 02:55.905
So since PSA provides rich data in mass education across

02:55.935 --> 02:58.665
many countries, and also the PSA

02:58.665 --> 03:02.085
Results and related research are so impactful

03:02.705 --> 03:05.285
and in driving all sorts of areas,

03:05.505 --> 03:07.525
school practices, policy making.

03:07.905 --> 03:10.445
So I thought I narrowed down my research

03:10.905 --> 03:13.045
to the mass education in pi,

03:13.065 --> 03:15.725
and that's why the research, question came.

03:16.395 --> 03:19.285
It's interesting that, you know, most of people ask you,

03:19.395 --> 03:21.725
okay, there are so many theoretical frameworks out there.

03:21.825 --> 03:23.885
So why did you particularly choose that one?

03:24.425 --> 03:28.245
So that's, when I have to take you back to the days that

03:28.585 --> 03:30.885
before my systematic review, so

03:30.885 --> 03:32.605
before the systematic review, I

03:33.285 --> 03:37.885
actually conducted a preliminary review on the factors of,

03:37.945 --> 03:41.765
predicting mass achievement in PSA in a single database,

03:42.185 --> 03:45.685
and even in a single database, it,

03:46.195 --> 03:49.565
produce over 1000 articles in the area.

03:50.145 --> 03:52.845
So I read all the titles and abstracts

03:52.865 --> 03:54.485
and trying to get my head around.

03:55.105 --> 03:58.405
And I realized that the factors related

03:58.505 --> 04:01.205
to the students' mass achievement are not

04:01.235 --> 04:02.325
from the single level.

04:03.075 --> 04:05.365
They could have related to the students themselves.

04:05.995 --> 04:07.645
They could have related to their parents

04:07.745 --> 04:09.245
and the household situations,

04:09.825 --> 04:12.565
and they also could relate to the teachers', peers,

04:12.705 --> 04:13.885
and the schools they go to.

04:14.385 --> 04:17.285
And also they could relate it to the education systems

04:17.985 --> 04:20.605
in different countries and also their society.

04:21.625 --> 04:23.445
So I was like, wow, there are

04:23.445 --> 04:25.365
so many levels actually of factors.

04:25.515 --> 04:27.485
It's not just purely students themselves.

04:28.185 --> 04:31.005
And then besides some,

04:31.245 --> 04:33.245
researchers have conducted the research

04:33.625 --> 04:37.045
to look at the interactions among these factors.

04:37.745 --> 04:38.965
So let me give you example.

04:39.835 --> 04:42.805
Like the, principles from the low,

04:43.105 --> 04:46.365
low social economic status schools tended

04:46.385 --> 04:50.485
to report a more severe shortage of all qualified teachers.

04:51.345 --> 04:53.845
So because of these interactions and also

04:53.845 --> 04:57.125
because of these complex levels of the factors,

04:57.865 --> 05:02.325
and that just agrees with, brand from brand's,

05:02.395 --> 05:03.605
ecosystem, theory

05:03.705 --> 05:05.405
of human development, you know what I mean?

05:05.405 --> 05:09.125
It's, it considers the wide range of factors

05:09.545 --> 05:12.285
and also it's complex interaction.

05:12.665 --> 05:17.085
So here we go. I had two major go, goals

05:17.265 --> 05:18.725
or aims for my review.

05:18.825 --> 05:23.125
So the first one is I aimed to find as many factors

05:23.385 --> 05:25.605
as possible that have been investigated

05:25.705 --> 05:26.805
by the other researchers.

05:27.405 --> 05:29.685
'cause I want to give them an overall picture.

05:30.305 --> 05:35.285
And then the second goal was to find the key patterns

05:35.505 --> 05:38.205
and key factors predicting,

05:38.985 --> 05:41.525
the mass achievement in PSA if possible.

05:41.525 --> 05:43.605
Right? So there were of course,

05:43.605 --> 05:45.765
because it was a massive review.

05:46.105 --> 05:47.485
So there are lots of results

05:47.505 --> 05:49.845
and you can read, in details in my paper.

05:50.265 --> 05:52.325
But here I would like to emphasize

05:52.585 --> 05:54.725
and highlight four findings.

05:55.345 --> 05:56.605
So the first one was

05:57.055 --> 05:58.765
There were about,

05:58.985 --> 06:02.805
or they're not about, there were 135 factors

06:03.775 --> 06:05.485
which have been investigated,

06:06.065 --> 06:09.205
and they are related to, mass performance

06:09.225 --> 06:10.285
or mass achievement.

06:10.865 --> 06:14.165
Now in this 135 factors,

06:14.875 --> 06:18.205
they actually are related to individual students,

06:19.105 --> 06:22.925
the household, context, the school community

06:23.745 --> 06:27.365
and education system and the macro society.

06:28.465 --> 06:32.525
So, and then the second, finding was that there were

06:33.295 --> 06:35.245
57 factors.

06:35.665 --> 06:39.845
So among 135, only 57 factors

06:40.355 --> 06:45.285
have been, investigated in more than five studies.

06:45.985 --> 06:48.205
So that's actually gives a clear picture

06:48.345 --> 06:49.565
to the researchers in the future.

06:50.385 --> 06:53.285
So, to tell people like what kind of factors

06:53.805 --> 06:55.645
actually haven't attract much attention.

06:56.625 --> 07:00.405
And so the number three is that there are seven factors,

07:00.975 --> 07:05.925
seven factors that are found to be consistently, positively

07:05.945 --> 07:07.845
or negatively associated with meth.

07:08.385 --> 07:12.685
So for example, the family socioeconomic status

07:14.065 --> 07:16.845
and the shortage of teachers and the general staff.

07:17.185 --> 07:21.445
So overall it was not surprising to see most factors,

07:22.265 --> 07:24.925
and their relationship with the,

07:24.925 --> 07:26.125
mathematics performance.

07:26.515 --> 07:31.005
However, there was one factor which was very surprising,

07:31.235 --> 07:33.885
that is a student centered instruction.

07:34.505 --> 07:39.365
So in my review, there were 10 studies investigated,

07:40.105 --> 07:43.365
the student centered instruction

07:43.465 --> 07:46.245
and its relationship with math achievement.

07:46.785 --> 07:50.445
And all of them found it's negative

07:51.335 --> 07:54.685
associated with maths and performance.

07:55.025 --> 07:57.005
And I was really surprised,

07:57.005 --> 08:00.725
because I was a teacher for many years in K to 12 schools.

08:01.265 --> 08:04.325
Um, yeah, so and I presented some possible,

08:04.405 --> 08:05.845
explanations in the paper.

08:06.425 --> 08:08.165
And then maybe because like

08:08.585 --> 08:11.125
how do we call student centered instruction?

08:11.125 --> 08:12.885
Different people have different understanding

08:13.545 --> 08:18.365
and also mathematics as a more technical,

08:18.365 --> 08:21.765
subject domain maybe needs more guidance from the teacher,

08:21.945 --> 08:23.525
from the teachers we don't know.

08:23.745 --> 08:26.765
So I think in the future, if the more studies

08:27.315 --> 08:29.405
dive in deeper in this area with real

08:29.625 --> 08:31.245
and that will be super beneficial.

08:32.105 --> 08:35.525
And lastly, and I think I said four points, right?

08:35.625 --> 08:38.005
So lastly, and I would like

08:38.005 --> 08:40.365
to actually point out in all,

08:40.785 --> 08:45.765
almost all the studies in the review used one form

08:45.905 --> 08:48.725
of correlational analysis.

08:49.425 --> 08:51.965
So it was, not surprising

08:51.965 --> 08:54.445
because of the nature of the data, but

08:55.065 --> 08:58.405
The, there's not much variety of the methodologies

08:58.795 --> 09:02.085
that researchers are considering or have been considering.

09:02.115 --> 09:05.725
Yeah, I think many researchers have been spending their time

09:05.825 --> 09:10.805
and energy and their expertise in doing the research in,

09:11.235 --> 09:14.725
factors related to the mass education in psa.

09:15.025 --> 09:19.365
And I think this systematic review was very timely for,

09:19.665 --> 09:22.645
to provide a comprehensive assessment

09:23.145 --> 09:24.525
of the state of knowledge.

09:24.785 --> 09:28.365
So people now have a, a comprehensive understanding of,

09:28.715 --> 09:30.765
okay, what's the possible factors are there

09:31.305 --> 09:34.445
and what have been investigated a lot, what haven't

09:34.545 --> 09:36.125
and what other relationship?

09:36.665 --> 09:39.765
So, and that's a, I'm really, happy

09:39.765 --> 09:41.445
that I achieved these goals.

09:42.065 --> 09:45.365
And and of course it was very obvious from the research,

09:45.625 --> 09:48.285
the finding that, the

09:48.915 --> 09:52.005
mass achievement was not,

09:52.735 --> 09:56.445
attributed solely to the individual students.

09:57.385 --> 10:00.165
And they are possibly related to lots of factors

10:00.745 --> 10:02.045
at the household levels.

10:02.545 --> 10:04.845
So the parents can think about what they can do

10:05.105 --> 10:07.405
and at the teachers and the school community.

10:07.465 --> 10:10.205
So the teachers and the school leaders can think about

10:10.275 --> 10:14.045
what we can do and also the, in the education systems.

10:14.345 --> 10:17.085
So the policy makers have been paying attention to it

10:17.425 --> 10:20.965
and our micro society, what do we talk about,

10:21.775 --> 10:22.965
about mass education?

10:22.965 --> 10:25.365
What's our general attitude towards all this?

10:25.705 --> 10:29.325
And I think this is actually, quite interesting

10:29.345 --> 10:31.965
to see from the different perspectives that, you know,

10:31.965 --> 10:34.525
different people actually can contribute to these pieces.

10:35.945 --> 10:37.765
And, I actually

10:38.285 --> 10:40.445
provided, quite a lot of suggestions

10:40.505 --> 10:42.005
for the future research in my paper.

10:42.305 --> 10:44.445
But here actually I want to emphasize too,

10:44.745 --> 10:49.135
and it's sort of my passion and I deeply my heart.

10:49.595 --> 10:53.335
So the first one is that we need studies.

10:54.195 --> 10:58.415
We need studies to investigate the complex rela

10:58.995 --> 11:01.255
interactions among factors.

11:02.035 --> 11:05.415
So the factors in this 135,

11:05.795 --> 11:07.535
how do they interact with each other?

11:08.395 --> 11:12.575
We also need studies to investigate the uniqueness

11:12.875 --> 11:15.935
and the variation of different country context

11:16.955 --> 11:21.135
and also this interactions of factors in this context.

11:22.275 --> 11:25.615
And I think that, currently maybe it's,

11:25.845 --> 11:28.455
it's a little bit, it's getting more

11:28.475 --> 11:31.495
and it's getting more and more researchers attention,

11:31.595 --> 11:34.095
but I think we need more strength in this area.

11:34.755 --> 11:38.695
And then number two is that we need a diverse methodologies

11:38.915 --> 11:43.535
to be employed in piece related research is

11:43.645 --> 11:46.815
with, I think if we can,

11:47.555 --> 11:50.175
if we are more open to the different methodologies

11:50.555 --> 11:54.335
and we will be utilize piece data better

11:54.995 --> 11:58.775
to examine the different aspects in the,

11:59.285 --> 12:01.455
education quality and equity.

00:00 - Back in 2021 and 2022. So I conducted, this systematic review to find out what factors, are predicting mass achievement, and I focus on the research in mass achievement in psa. So as, Sam was saying, like, what is P and why is psa? So first the PA is stands for program

00:25 - for international Student assessment. So it's not the assessment for the international students only like most people would imagine for the name. It's actually a survey for the students across many countries. So basically the countries decided to participate in psa. So each country randomly selects a representative or students at the age of 15 years old. Then these students participate in the survey.

00:57 - So, for example, the last piece, cycle happened in 2022 and 81 countries and the regions participated in that. So, and, a few months ago, the initial results were released. Like, for example, Singapore, Korea, Switzerland and Canada are on the top, performing countries. So, so far PISA has evolved about 3.7 million students from over 100 countries. And the regions, yes, it's a big number of students

01:35 - who participate in that. so, because of that, as you can tell, there's such rich data behind this survey. So many researchers around the world have been utilizing piece data to investigate matters related to education, quality and, equity. So, and also the piece of results and, piece of related, research

02:02 - have been driving the, school practices and policy changes across many countries. Like, it's very obvious when the results are released, the governments and the policy makers will pay extra attention to their, you know, performance in PSA and their global ranking. So ultimately we cannot ignore the impact of psa. So, yeah, and the, on the other hand, there are three major domains in psa. So reading, math, and science.

02:37 - So, so in the last PSA cycle in 2022, the focus of domain was mathematics. And I, I have a particular interest interested in mass education across many countries. So since PSA provides rich data in mass education across many countries, and also the PSA Results and related research are so impactful and in driving all sorts of areas, school practices, policy making. So I thought I narrowed down my research

03:10 - to the mass education in pi, and that's why the research, question came. It's interesting that, you know, most of people ask you, okay, there are so many theoretical frameworks out there. So why did you particularly choose that one? So that's, when I have to take you back to the days that before my systematic review, so before the systematic review, I actually conducted a preliminary review on the factors of, predicting mass achievement in PSA in a single database,

03:42 - and even in a single database, it, produce over 1000 articles in the area. So I read all the titles and abstracts and trying to get my head around. And I realized that the factors related to the students' mass achievement are not from the single level. They could have related to the students themselves. They could have related to their parents and the household situations,

04:09 - and they also could relate to the teachers', peers, and the schools they go to. And also they could relate it to the education systems in different countries and also their society. So I was like, wow, there are so many levels actually of factors. It's not just purely students themselves. And then besides some, researchers have conducted the research to look at the interactions among these factors.

04:37 - So let me give you example. Like the, principles from the low, low social economic status schools tended to report a more severe shortage of all qualified teachers. So because of these interactions and also because of these complex levels of the factors, and that just agrees with, brand from brand's, ecosystem, theory of human development, you know what I mean? It's, it considers the wide range of factors

05:09 - and also it's complex interaction. So here we go. I had two major go, goals or aims for my review. So the first one is I aimed to find as many factors as possible that have been investigated by the other researchers. 'cause I want to give them an overall picture. And then the second goal was to find the key patterns and key factors predicting, the mass achievement in PSA if possible.

05:41 - Right? So there were of course, because it was a massive review. So there are lots of results and you can read, in details in my paper. But here I would like to emphasize and highlight four findings. So the first one was There were about, or they're not about, there were 135 factors which have been investigated,

06:06 - and they are related to, mass performance or mass achievement. Now in this 135 factors, they actually are related to individual students, the household, context, the school community and education system and the macro society. So, and then the second, finding was that there were 57 factors. So among 135, only 57 factors have been, investigated in more than five studies.

06:45 - So that's actually gives a clear picture to the researchers in the future. So, to tell people like what kind of factors actually haven't attract much attention. And so the number three is that there are seven factors, seven factors that are found to be consistently, positively or negatively associated with meth. So for example, the family socioeconomic status and the shortage of teachers and the general staff. So overall it was not surprising to see most factors,

07:22 - and their relationship with the, mathematics performance. However, there was one factor which was very surprising, that is a student centered instruction. So in my review, there were 10 studies investigated, the student centered instruction and its relationship with math achievement. And all of them found it's negative associated with maths and performance. And I was really surprised,

07:57 - because I was a teacher for many years in K to 12 schools. yeah, so and I presented some possible, explanations in the paper. And then maybe because like how do we call student centered instruction? Different people have different understanding and also mathematics as a more technical, subject domain maybe needs more guidance from the teacher, from the teachers we don't know. So I think in the future, if the more studies

08:27 - dive in deeper in this area with real and that will be super beneficial. And lastly, and I think I said four points, right? So lastly, and I would like to actually point out in all, almost all the studies in the review used one form of correlational analysis. So it was, not surprising because of the nature of the data, but The, there's not much variety of the methodologies

08:58 - that researchers are considering or have been considering. Yeah, I think many researchers have been spending their time and energy and their expertise in doing the research in, factors related to the mass education in psa. And I think this systematic review was very timely for, to provide a comprehensive assessment of the state of knowledge. So people now have a, a comprehensive understanding of, okay, what's the possible factors are there and what have been investigated a lot, what haven't

09:34 - and what other relationship? So, and that's a, I'm really, happy that I achieved these goals. And and of course it was very obvious from the research, the finding that, the mass achievement was not, attributed solely to the individual students. And they are possibly related to lots of factors at the household levels. So the parents can think about what they can do

10:05 - and at the teachers and the school community. So the teachers and the school leaders can think about what we can do and also the, in the education systems. So the policy makers have been paying attention to it and our micro society, what do we talk about, about mass education? What's our general attitude towards all this? And I think this is actually, quite interesting to see from the different perspectives that, you know, different people actually can contribute to these pieces.

10:35 - And, I actually provided, quite a lot of suggestions for the future research in my paper. But here actually I want to emphasize too, and it's sort of my passion and I deeply my heart. So the first one is that we need studies. We need studies to investigate the complex rela interactions among factors. So the factors in this 135, how do they interact with each other?

11:08 - We also need studies to investigate the uniqueness and the variation of different country context and also this interactions of factors in this context. And I think that, currently maybe it's, it's a little bit, it's getting more and it's getting more and more researchers attention, but I think we need more strength in this area. And then number two is that we need a diverse methodologies to be employed in piece related research is with, I think if we can,

11:47 - if we are more open to the different methodologies and we will be utilize piece data better to examine the different aspects in the, education quality and equity.

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