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Regardless of which camp you pitch your tent at, here are four research papers I believe are helpful for understanding the role of CTR in search engine rankings and SEO.

Thorsten Joachims and the Study of CTR

Thorsten Joachims is a researcher associated with Cornell University. He has produced many influential research papers, among them research on the use of CTR for the purposes of search engine algorithms.

If you are interested in understanding the possible roles of CTR in search engines, then these four research papers authored by Joachims will prove enlightening.

1. Optimizing Search Engines with CTR

That this research paper is from 2002 shows just how old research into CTR is. Studying CTR for relevance information is a mature area of research. Search engine related research has progressed far beyond this area.

Here’s what the research paper states:

In my opinion, this paper recognizes limitations in the algorithm. The algorithms are limited to learning which of the top 10 links are most relevant. But it learns nothing about the webpages in second and third or fourth pages of the search engine results pages (SERPs).

This is what the research paper observes:

“…there is a dependence between the links presented to the user, and those for which the system receives feedback.”

Right from the beginnig of CTR research it was understood that CTR data from the top 10 of the SERPs was of limited but important value. The research paper also notes that using this kind of algorithm was open to spamming and that steps would need to be taken to make it immune to spamming.

This is what Thorsten Joachims noted:

2. The Concept of CTR as Biased Feedback

Here is how the CTR research paper expresses the idea that CTR data is noisy:

Yet the paper was optimistic that because there is a large amount of data to be mined, machine learning could be applied in order to reach accurate determinations of what links are more relevant than other links.

The research paper on CTR reached this conclusion:

I believe it is important to note that this research paper is not concerned with finding spam or with finding low quality sites to exclude. It is simply concerned with finding relevant sites that satisfy users.

3. Machine Learning and Simulated CTR

The third research paper is also from 2005. This paper is titled: Evaluating the Robustness of Learning from Implicit Feedback . The goal of this paper is to understand when CTR data is useful and when CTR data is biased and less useful.

This is how the paper framed the problem and the solution:

“…this data tends to be noisy and biased… In this paper, we consider a method for learning from implicit feedback and use modeling to understand when it is effective.”

This paper is especially interesting because it introduces the possibility of modeling user behavior and using that data instead of actual user behavior. This paper also mentions reinforcement learning, which is machine learning.

Here’s a link to an introduction to reinforcement learning.  It uses the example of a child learning that a fire is good because it gives off heat. But later learns the fire is bad if you get to close.

This is how the research paper presented it:

“This type of interactive learning requires that we either run systems with real users, or build simulations to evaluate algorithm performance.

This is really cool. It shows how a search engine can use machine learning to understand user behavior and then train the algorithm without actual CTR data but with simulated CTR.

This means that a search engine can theoretically model user behavior on webpages even if those pages do not rank on the first page of the SERPs. This overcomes the limitations noted in the research way back in 2002.

4. User Intent and CTR – 2008

The final research paper I want to introduce to you is, Learning Diverse Rankings with Multi-Armed Bandits (PDF). This research paper does not use the phrase user intent. It uses the phrase, user satisfaction.

Satisfying all users means showing different kinds of webpages. The user intent for many search queries is different.

What’s relevant for one user is less relevant to another. Thus, it’s important to show diverse search results, not the same kind of answer ten times.

Here’s what the paper says about showing multiple kinds of results:

And this is what the papers says about user satisfaction:

“…previous algorithms for learning to rank have considered the relevance of each document independently of other documents. In fact, recent work has shown that these measures do not necessarily correlate with user satisfaction…”

And here is the part that really nails the problem that search engines today have solved:

“…web queries often have different meanings for different users… suggesting that a ranking with diverse documents may be preferable.”

The only downside to this kind of CTR algorithm for determining user satisfaction is that it may not work well for topics where what users want is in a state of change.

“We expect such an algorithm to perform best when few documents are prone to radical shifts in popularity. ”

Read the CTR Research

And there you have it. These are, in my opinion, four important research papers to read before forming an opinion about the role of CTR in ranking webpages.

It’s important to note that the first research paper cited in this article is from 2002. The last one is from 2008. This gives an idea of how mature research into CTR is. Most research today is no longer focused on CTR. It is focused on artificial intelligence.

Nevertheless, if you are interested in CTR data and how it may play a role in ranking, you will benefit from reading these four research papers.

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Is Using Google Analytics A Search Ranking Factor?

Google Analytics (GA) is a powerful tool that lets website owners learn how users interact with their webpages.

The amount of information we can get from Google Analytics is so in-depth that a theory has been circulating, for over a decade, that GA data is a ranking factor.

Is Google Analytics really powerful enough to influence Google search results?

Let’s take a closer look.

[Recommended Read:] Google Ranking Factors: Fact or Fiction

The Claim: Google Analytics As A Ranking Factor

In Google’s How Search Works documentation, we can see that a webpage’s relevance is one of the many factors used to rank webpages.

The most basic relevancy signal is that the content contains the same words as the search query.

Additional information about how Google determines a page’s relevance is provided.

Beyond simple keyword matching, Google says, “We also use aggregated and anonymized interaction data to assess whether search results are relevant to queries. We transform that data into signals that help our machine-learned systems better estimate relevance.”

What is “interaction data,” and where does Google get it?

That makes sense because those are the metrics marketers are familiar with and understand to represent the interactive data Google may be looking for.

Marketers may also notice a correlation between the metrics improving as their position in the SERP improves.

Is it possible that we are somehow improving Google’s understanding of our website’s user experience using Google Analytics?

Like some sort of SEO bat signal?

Can we directly influence rankings by giving Google more “interaction data” to work with?

[Ebook:] Download The Complete Guide To Google Ranking Factors

The Evidence Against Google Analytics As A Ranking Factor

While we don’t have direct access to Google’s algorithm, evidence shows Google Analytics as a ranking factor is not a plausible theory.

First, Google representatives have been clear and consistent in saying that they don’t use Google Analytics data as a ranking factor.

As recently as March 16, 2023, John Mu has responded to tweets about Google Analytics impacting rank.

In jest, a marketer suggested if Google wanted people to use GA4, they could just say it would improve ranking.

John Mu replied, “That’s not going to happen.”

Google seems to continuously be batting down the idea that its analytics services influence ranking in any way.

Back in 2010, when we were tweeting to snag the top spot in results for a few moments, Matt Cutts said, “Google Analytics is not used in search quality in any way for our rankings.”

And you don’t have to take Google’s word for it.

Here are three websites ranking in the top 10 for highly competitive keywords that do not have the Google Analytics tag on their site.

1. Ahrefs, an SEO tool, famously does not use Google Analytics.

Tim Soulo, CMO at Ahrefs, tweeted in December 2023, “Every time I tell fellow marketers that we don’t have Google Analytics at chúng tôi they react with ‘NO WAY!’”

And the Ahrefs domain ranks in the top 10 positions for over 12,000 non-branded keywords.

2. Another famous example is Wikipedia.

Wikipedia articles dominate Google search results, ranking very well for definition-type searches such as computer, dog, and even the search query “Google.”

And it ranks for all this with no Google Analytics code on the site.

3. One more example is Ethereum.

Ethereum is ranking in the top 10 for [nft]. NFT is an enterprise-level keyword with over one million monthly searches in the United States alone.

Ethereum’s website does not have Google Analytics installed.

[Discover:] More Google Ranking Factor Insights

Our Verdict: Google Analytics Is Not A Ranking Factor

Google Analytics is a powerful tool to help us understand how people find our website and what they do once there.

And when we make adjustments to our website, by making it easier to navigate or improving the content, we can see GA metrics improve.

However, the GA code on your site does not send up an SEO bat signal.

The GA code is not a signal to Google, and it does not make it easier for Google to assess relevance (whether your webpage fulfills the user’s search query.)

The “bat signal” is for you.

Google Analytics is not a ranking factor, but it can help you understand whether you’re heading in the right or wrong direction.

Featured Image: Paulo Bobita/Search Engine Journal

Seo Ranking Factors Correlation Research – Love Or Loathe?

How can Digital Marketers use SEO ranking factors correlations?

Love them or loathe them, correlation studies seem to be everywhere when discussing ‘best practices’ in SEO. So we have to ask – ‘how can we use them’ and ‘should we trust them’?

Moz and Searchmetrics are the best known publishers of correlation studies who produce reports from sample sizes in the order of hundreds of thousands of webpages, with the purpose of identifying what’s important and what’s not in the world of search marketing.

So how do you use them to inform your online strategy – do you try to remove yourself from all human emotion and blindly go with these numbers or do you ignore them and trust your gut instincts?  Or is there a middle ground? You can see the challenge from this recent Searchmetrics SEO Ranking factors infographic.

This infographic highlights some of the dangers of relying on ranking factors correlation compilations without wider knowledge and your own tests. For one, correlation doesn’t equal causation. Sharing on social networks have a strong correlation, yet this may simply be because of the quality of content which increases dwell time on the post – a factor not shown here. Likewise the occurrence of keywords within the title tag of a post or anchor text linking to a post remain important, yet they aren’t clearly shown here. And you will likely fall foul to the Penguin filter if you generate too many links with the same anchor text linking to one page. Subtleties you just can’t see from a chart.

Don’t see them as a means to trick the search engines into liking you

Long gone are the days where marketers could realistically expect to understand the interplay between all the variables explicitly considered in a search engine’s algorithm – even if one had the resources to achieve the modern day equivalent, the victory would likely only be short-lived. The machine learning capabilities available to search engines and the regularity of their updates mean any exploitable weakness you may find are going to be patched quickly.

In short: relying on exploiting search engine’s algorithms with clever tricks is not an effective long term SEO – or broader marketing – strategy.

Moz’s Rand Fishkin explains it well but to summarise: if you chase the algorithms religiously, you are likely to lose sight of the larger goal of appealing to your human audience. When the search engines (who are ultimately trying to predict what humans will like) change their metrics to decide this – and, rest assured, they will! – the chances are you will come up short and all that effort will be for nought.

Let me illustrate with an example.

Imagine that you have the means to infer to a virtually undeniable level (which is very difficult, far beyond the capability of a correlation study) that Facebook ‘likes’ are directly used in Google’s algorithm. Naturally, you develop a strategy to capitalise on this and base a large portion of your efforts on getting ‘likes’. Focusing aggressively on ‘likes’, less user-friendly tactics start to seep into your strategy – things such as hiding content until they like the page or producing mediocre content just for likes; users are put off and not really engaging but you have the likes and that’s all that matters, algorithmically.  Basically, you start teaching to the test.

All goes well until but Google suddenly decides – by virtue of its ongoing evaluations (or, indeed just arbitrarily) – that Facebook ‘likes’ are no longer so valuable.  Your strategy is exposed and your rankings drop to what they once were.  Worse still, because your approach has been neglecting your true audience, your rankings continue to drop.  You’ve been burned.

And that’s before we even consider the fact that Google’s analysis may already have them implementing an algorithmic update before you’ve finished your study and rolled out your strategy.  The size, speed and sophistication of search engine evaluations are almost certainly going to outstrip your capacity to exploit loopholes.

Understand the limits of correlations

The vast majority of people will be able to tell you that correlation does not imply causation.  There is nothing particularly special about correlation studies in this sense – truth be told, no statistical model truly implies causation.  There will always be a level of uncertainty over what is actually causing something to happen.

There is an underlying problem with mean spearman correlations though. Mainly in what way(s) they can be interoperated with.

The figures given in mean spearman studies are not the correlation of one sample for each variable. They are the summary of a set of correlations. When one sees for instance, the number 0.22 associated with the variable “Total number of links to page”, that 0.22 is not a correlation. It is the attempt of the person conducting the study to summarise his or her, possibly very large, collection of correlations.

Separating the correlations in this manner initially is perfectly valid. After all, it is not sensible to attempt a numerical comparison between a web page being in the fifth position for “fancy dress costumes” and another one being third for “cheap hotels in London”.

The temptation is to try and summarize all these correlations by a single, more digestible number.  And, on the face of it, averaging them may seem valid. To understand why this can be problematic, we must first understand why we may choose to average our data in the first place.

What exactly is an average?

Normally, when we average a set of numbers we are estimating an unknown parameter. We do this because averaging has been shown to have a lot of favourable properties in these contexts; mainly that the number it produces is often an unbiased maximum likelihood estimator of our parameter and easy to calculate.

To put it simply: when we average a set of data we are actually just guessing some unknown number and given the right circumstances, this average can be mathematically shown to be best possible guess based on our data.

For example suppose you have a fair die. The “true” probability, P, of rolling a five is assumed to be a sixth. Imagine, however, that you don’t know how to work out this probability – you’d need to set an experiment to estimate your (now unknown) parameter P.  You roll the die 100 times and record the numbers of fives (N).  Based on this experiment what would your estimate of your parameter, P, be?

Anyone with a grasp of statistics will know, intuitively, blurt out “N/100” but can you explain why?

Clue: the answer is not as simple as “because it’s the average” – it actually involves a lot of sophisticated maths to explain that for estimating P from your 100 rolls your best option is to go with N/100. 

Averaging in correlation studies

But things become even more clouded when we wish to average correlation coefficients. What exactly is even being estimated?  What is the equivalent of our P in the above example?  And how does estimating this parameter help us in answering our underling questions?

Unfortunately a lot of people use them to interoperate with what they want them to give information on or suggest. Some of the most commonly claimed questions they are claimed to help answer are

What variables are more prevalent in higher ranking pages?

Can any of the variables be used to predict page positions?

Which variables improve my chances of ranking highly?

What do search engines think is popular with users?

And the problem here is there is a fundamental gap of logic between knowing the mean of a collection of correlations and being closer to answer these questions.

For more details on some of the more technical points of correlation studies and an alternative method specifically for the question “which variables improve my chances of ranking highly?”, I’ve gone into more detail in this piece  where I explore whether logistic regression may be better suited to answer the question.

So what CAN I take from correlation studies?

We have to accept that it can be more challenging that we’d imagine to use correlation studies to make concrete declarations.

However, that does not mean that a correlation study cannot teach us things for digital marketing. It just means we have to live with a level of uncertainty and, as with many things in life, base our decisions on broader context and experience.

So how can digital marketers make the most of these studies?

1. Recognise that search engine positions are a means to an end

They are not the ultimate goal. Your audience is.

2. Avoid over evaluating the numbers

Unless you are ready to start working with the raw data there really is no use in trying to fit a narrative to number of Google “+1”s having a mean spearman of 0.34 and Facebook ‘Likes’ having one of 0.27. The appropriate interpretations are not fully understood so be cautious of anyone claiming otherwise.

 3. Use them in combination with other sources

4. Be confident enough to ignore them

Any statistical study or model will work on a set of assumptions and a level of inaccuracy. They are not a guarantee for individual success especially if the assumptions are, on full reflection, not met.

Two common assumptions of correlation studies are namely that searches are location blind and that the webpages have already attained a minimum position for a search term (e.g. top 50). This means, for example, a small accountancy firm in Manchester ranking around 200th on Google for the query “Manchester accountants” should probably not base decisions purely on these correlation studies as they are for now not ranked highly enough. It is also unlikely that that their target audience is turning off local searches. 

Summary of Correlation Studies

Even with that caveat however, I’d always be sceptical with correlation studies in their current form. That is not to say correlation studies are wrong or not useful. Only that their usefulness is, so far (statistical safety valve!), formally unknown and you should ultimately be aware of this before overcommitting to the numbers they produce. Always remember to view these studies in the larger context and above all else, always put your human audience first.

How To Write A Research Proposal

A research proposal describes what you will investigate, why it’s important, and how you will conduct your research.

The format of a research proposal varies between fields, but most proposals will contain at least these elements:

Title page


Literature review

Research design

Reference list

While the sections may vary, the overall objective is always the same. A research proposal serves as a blueprint and guide for your research plan, helping you get organized and feel confident in the path forward you choose to take.

Research proposal purpose

Academics often have to write research proposals to get funding for their projects. As a student, you might have to write a research proposal as part of a grad school application, or prior to starting your thesis or dissertation.

In addition to helping you figure out what your research can look like, a proposal can also serve to demonstrate why your project is worth pursuing to a funder, educational institution, or supervisor.

Research proposal aims

Relevance Show your reader why your project is interesting, original, and important.

Context Show that you understand the current state of research on your topic.

Approach Demonstrate that you have carefully thought about the data, tools, and procedures necessary to conduct your research.

Achievability Confirm that your project is feasible within the timeline of your program or funding deadline.

Research proposal length

The length of a research proposal can vary quite a bit. A bachelor’s or master’s thesis proposal can be just a few pages, while proposals for PhD dissertations or research funding are usually much longer and more detailed. Your supervisor can help you determine the best length for your work.

One trick to get started is to think of your proposal’s structure as a shorter version of your thesis or dissertation, only without the results, conclusion and discussion sections.

Download our research proposal template

Research proposal examples

Writing a research proposal can be quite challenging, but a good starting point could be to look at some examples. We’ve included a few for you below.

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Title page

Like your dissertation or thesis, the proposal will usually have a title page that includes:

The proposed title of your project

Your name

Your supervisor’s name

Your institution and department

TipIf your proposal is very long, you may also want to include an abstract and a table of contents to help your reader navigate your work.


The first part of your proposal is the initial pitch for your project. Make sure it succinctly explains what you want to do and why.

Your introduction should:

Introduce your topic

Give necessary background and context

Outline your problem statement and research questions

To guide your introduction, include information about:

Who could have an interest in the topic (e.g., scientists, policymakers)

How much is already known about the topic

What is missing from this current knowledge

What new insights your research will contribute

Why you believe this research is worth doing

Literature review

As you get started, it’s important to demonstrate that you’re familiar with the most important research on your topic. A strong literature review shows your reader that your project has a solid foundation in existing knowledge or theory. It also shows that you’re not simply repeating what other people have already done or said, but rather using existing research as a jumping-off point for your own.

In this section, share exactly how your project will contribute to ongoing conversations in the field by:

Comparing and contrasting the main theories, methods, and debates

Examining the strengths and weaknesses of different approaches

Explaining how will you build on, challenge, or synthesize prior scholarship

TipIf you’re not sure where to begin, read our guide on how to write a literature review.

Research design and methods

Following the literature review, restate your main objectives. This brings the focus back to your own project. Next, your research design or methodology section will describe your overall approach, and the practical steps you will take to answer your research questions.

Contribution to knowledge

To finish your proposal on a strong note, explore the potential implications of your research for your field. Emphasize again what you aim to contribute and why it matters.

For example, your results might have implications for:

Improving best practices

Informing policymaking decisions

Strengthening a theory or model

Challenging popular or scientific beliefs

Creating a basis for future research

Reference list

Last but not least, your research proposal must include correct citations for every source you have used, compiled in a reference list. To create citations quickly and easily, you can use our free APA citation generator.

Research schedule

Some institutions or funders require a detailed timeline of the project, asking you to forecast what you will do at each stage and how long it may take. While not always required, be sure to check the requirements of your project.

Here’s an example schedule to help you get started. You can also download a template at the button below.

Download our research schedule template

Example research schedule

Research phase Objectives Deadline

1. Background research and literature review

Meet with supervisor for initial discussion

Read and analyze relevant literature

Use new knowledge to refine research questions

Develop theoretical framework

20th January

2. Research design planning

Design questionnaires

Identify channels for recruiting participants

Finalize sampling methods and data analysis methods

13th February

3. Data collection and preparation

Recruit participants and send out questionnaires

Conduct semi-structured interviews with selected participants

Transcribe and code interviews

Clean data

24th March

4. Data analysis

Statistically analyze survey data

Conduct thematic analysis of interview transcripts

Draft results and discussion chapters

22nd April

5. Writing

Complete a full thesis draft

Meet with supervisor to discuss feedback and revisions

17th June

6. Revision

Complete 2nd draft based on feedback

Get supervisor approval for final draft


Print and bind final work


28th July


If you are applying for research funding, chances are you will have to include a detailed budget. This shows your estimates of how much each part of your project will cost.

Make sure to check what type of costs the funding body will agree to cover. For each item, include:

Cost: exactly how much money do you need?

Justification: why is this cost necessary to complete the research?

Source: how did you calculate the amount?

To determine your budget, think about:

Travel costs: do you need to go somewhere to collect your data? How will you get there, and how much time will you need? What will you do there (e.g., interviews, archival research)?

Materials: do you need access to any tools or technologies?

Help: do you need to hire any research assistants for the project? What will they do, and how much will you pay them?

Other interesting articles

If you want to know more about the research process, methodology, research bias, or statistics, make sure to check out some of our other articles with explanations and examples.

Frequently asked questions about research proposals Cite this Scribbr article

McCombes, S. & George, T. Retrieved July 17, 2023,

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Pov: We Need To Be Better At Teaching Kids To Read

POV: We Need to Be Better at Teaching Kids to Read Wheelock dean addresses new report that criticizes area colleges for not adequately preparing future educators—and shares steps BU is taking

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Voices & Opinion

POV: We Need to Be Better at Teaching Kids to Read Wheelock dean addresses new report that criticizes area colleges for not adequately preparing future educators—and shares steps BU is taking

Being able to read independently is powerful. Despite its power, in Massachusetts schools today many children are not taught to read independently, significantly limiting their potential. These children are overrepresented by children from low-income communities, communities of color, and children with disabilities. Fortunately, we have the knowledge to change these outcomes. In the United States, few areas of education have been studied more than how children learn to read. For many decades researchers from several disciplines (e.g., psychology, linguistics, speech pathology, education, neuroscience) have studied how the human brain perceives and processes print and connects it into oral language and concepts in order to understand an author’s message. This area of study has recently been dubbed the science of reading.

Recently, the National Council on Teacher Quality (NCTQ) published a report that ranked Massachusetts 35th overall for preparing future educators to teach children to read. This included studying the programs of 19 Massachusetts higher education institutions that prepare teachers. Many of these institutions performed very poorly on the NCTQ’s ratings—including Boston University’s Wheelock College of Education & Human Development, which received a rating of D. As the dean of Wheelock, I feel it is important to address this disappointing rating directly and to talk about the recent change initiatives that have been underway at Wheelock to ensure that we are at the forefront of efforts to empower students through literacy.

Like other areas of research, there are many things we know and don’t know about learning to read. We do know that learning to read is not a natural process; it takes effort. Some children seem to learn relatively easily, while others struggle. This variation can be misleading to parents and teachers. Still, studies have continued to show that with explicit and systematic instruction, upwards of 95 percent of students can learn to read by the end of first grade, though in Massachusetts just 43 percent are reading proficiently by fourth. Clearly there is a disconnect here, and this disconnect is one that the BU Wheelock faculty is committed to doing our part to address.

For many children, with the right sequence of experiences and reading material that is motivating, the process results in learners becoming increasingly independent with texts early. However, for other children, learning to read requires special effort. Why is this? The answer to this question varies considerably. For some children, reading challenges are organic, the result of language delays or learning disabilities (e.g., dyslexia) that impact their early reading. For others, reading challenges may occur because they are not taught how to read effectively, and they develop poor word reading strategies. Poor text reading has a compounded impact on the overall process of reading and on children’s motivation to read. Evidence suggests that these children, in particular, benefit from carefully designed instruction and practice with reading in order to prevent them from developing greater challenges with reading later in their development.

It is important to acknowledge that the NCTQ review focused on the foundational skills required for teaching and learning how to read. As discussed, this knowledge and skill set is necessary for more equitable outcomes, but not entirely sufficient in building the literacy-rich, joyful love of reading we all envision for young learners. Language and literacy development is a complex and comprehensive endeavor, and it’s through that lens that we are moving urgently at Wheelock to work in a more interdisciplinary approach to preparing teachers aligned with evidence-based practices. 

What we are doing at Wheelock

More than two years ago, recognizing that our faculty had expertise in some areas of literacy development and not in others, we hired several new faculty members with expertise in foundational literacy and in working with children with dyslexia and other reading-related disorders. Building our expertise was an essential first step to addressing the gaps in our teacher preparation. These nationally recognized experts also brought with them the National Center for Improving Literacy, the federally supported center funded to help schools and districts improve reading outcomes for children. These new additions to our team helped complement our existing expertise in preparing reading teachers. Together, our faculty has begun revising course offerings and program sequences to better address these foundational skills.

This is no small feat. One thing we have recognized is the structural challenges that have been built up over time around the way we organize our programs and courses, which have not always lent themselves to the cross-discipline knowledge related to reading. For instance, faculty across early childhood, elementary, special, literacy and language, and English education have all been working to revise our literacy course offerings and experiences in order to ensure that our graduates are prepared to teach using the science of reading broadly, not just in early reading.

We have also engaged actively with state policymakers on this effort. This includes faculty serving on task forces around the early literacy standards or working groups to create a new early grades curriculum. With the support of the state department of education here in Massachusetts, we were among only a handful of teacher prep programs who invited an independent agency (TPI-US) to conduct a thorough review of our syllabi and lesson plans, observe our courses and students’ practical experiences, and interview our faculty, students, and alumni. The goal was to further explore the gaps we needed to address to improve our students’ preparation to teach reading and literacy skills more broadly and to align with the DESE early literacy standards.

The results of the TPI-US review have continued to push us further to improve. Our faculty went to work immediately using the findings to chart a new sequence of courses, retaining the content and experiences that are aligned with the science while improving on areas that were not aligned. For example, we will offer a new foundational literacy course for all our teacher candidates that will also require that students demonstrate their ability to enact these practices in classrooms with students.

NCTQ’s rating for BU Wheelock was based on a limited view of our work. They reviewed only two outdated syllabi, not the totality of our students’ experiences. However, NCTQ’s review was not entirely wrong and identified some of the same areas that were highlighted by TPI-US. We’ve invited TPI-US to come back to BU in the near future to review our revised program so that we can continue to learn from their feedback. We will continue to engage with NCTQ and welcome future reviews of our teacher preparation programs. I’m pleased that our faculty is engaging in a comprehensive revision of our teacher preparation to align with the science of reading. We are addressing the gaps in order to help our teacher candidates be prepared to teach all their students to read independently and critically.

David Chard is dean of BU Wheelock College of Education & Human Development; he can be reached at [email protected].

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A Free Online Screen Recorder You Need To Try

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In the Audio Input option (the icon with the microphone), you can either choose None, System Sound, Microphone and System Sound and Microphone. In the Recording options, you can decide if you want the mouse cursor to appear in your screenshots or not; it’s up to you. You also have the option to show the countdown before recording or not, beep on start recording, show recording boundary, and show recording toolbar.


Judy Sanhz

Judy Sanhz is a tech addict that always needs to have a device in her hands. She loves reading about Android, Softwares, Web Apps and anything tech chúng tôi hopes to take over the world one day by simply using her Android smartphone!

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