Curriculum Development Process

An effective educational program can only be successful if it is prepared appropriately.

Curriculum is the backbone of educational program which needs proper designing.

The curriculum process consists mostly of five elements or phases.

1 Situational analyses.

2 Formulation of objectives.

3 Selection of content/Scope and sequence.

4 Methods/ Strategies/Actives.

5 Evaluation.

Situational analyses

In order to develop a procedure of Curriculum Organization, one must keep in mind the realities of the situation. The curriculum is the mirror of the traditions, environment and ideas of the concern society. The main purpose of education system to prepare a curriculum to the actual needs of the society. The main functions of curriculum is to preserve and transmit the cultural heritage to next generation.  Language, social needs of the society, political and religious situation must be considered while developing curriculum.

The identification of the areas mentioned here help the planner in curriculum development and the selection of objectives, learning material (learning) and appropriate evaluation procedure.

Important aspects of existing situation are as follows:

Ø  Geographical condition of the country.

Ø  National and international trends.

Ø  Cultural and social needs.

Ø  System of examinations etc.

Ø  Economical conditions.

Ø  Age, level and interests of the learners.

Ø  Pattern of curriculum to be followed.

Ø  Religious condition

Ø  Teacher training programmes.

Determination and formulation of learning outcomes-sources of learning outcome

Formulation of Objectives Validation of Educational Objectives:

The work of the curriculum planners to identify such Goals and objectives which fulfill the desires of the society and according the need of national and international demands. In order to reach these goals the curriculum planners clearly state the aims which are related to various fields of studies or subject areas and even related to classroom management and teaching.

Teachers and learner are the first who have the responsibility to achieve the initial targets in class-room. The achievement of these objectives ultimately leads the learner to-wards various categories of life activities, such as:

Continued learning: motivation toward Learning and create thinking skills in learner

Citizenship: Prepare a good citizen who contribute in the economic, local, national, and international level,

Vocational effectiveness: To help the individual on the vocational aspect and made him an economic assets of society

Home responsibilities: Inculcate the sense of responsibility in the Learner to help each other, respect people, and take care of elders and other related experiences, such as food, social and individual activity.

Validation of Educational Objectives

General principles for stating valid objectives for curriculum.

1.            Consistency with the ideology of a nation.

2.            Consistency and non-contradiction of    educational objectives.

3.            Behavioristic interpretation.

4.            Consistency with social condition.

5.            Democratic ideals/relationship.

6.     Fulfillment of basic human needs.

                                                (Reman.M, 2000)

Consistency with the ideology of a Nation

Every nation has certain beliefs and the philosophy of Pakistan is based upon Islam therefore the objectives of our educational system must be regarding the teaching of Islam. The validity and foundation of educational objectives in Pakistan depends upon Quran and Sunnah.

                Fulfillment of basic human needs

Man Need the fulfillment of basic needs in life to maintain equilibrium. The objective must help in the attainment of these needs. E.g. Among the basic needs that have been identified are food, clothing, shelter etc...

Consistency and non-contradiction of education objectives:

Educational objectives must not be contradictory to each other. There should be consistency in the objectives at all levels. The objectives must inculcate certain Islamic value and skill in the individual to help him in the real life situation.

Behavioristic interpretation:

Objectives expressed in terms of student behavior are called as behavioral objectives. To create valid, clear and achievable objectives, the curriculum planners have to articulate these objectives for the development and integration of the personalities, economic efficiency, self realization, critical thinking, problem solving ability, understanding of rights.

The objectives that are not put in terms of human behavior are invalid.

Consistency with social conditions:

The objectives of an educational programme are always related to social and culture realities of a nation. In a develop society that is undergoing little or no change, objectives usually are closely related to conditions  of that time (updated) , And when a society ideas and progress is slow in adopting new ways of doing things, because of the repaid advancements in the field of science and technology and inability  of a society to cope with time. 

The fact is that Mass media of communication has grown and Computers are replacing the manpower which is new social realities of life. The old objectives formulated on the basis of old realties now need a revisit and need modification according to the new social and scientific conditions.

Curriculum planners sometimes avoid the new conditions and they feel no need to improve educational objectives with changing time resulting put the development of a society in danger. Hence the curriculum developers have to formulate such type of objectives which are valid with respect to changing needs and the aspects of past culture which they feel essential to preserve as heritage.

Democratic ideals relationship:

Only the democratic ideology fulfills the basic needs of a society and this is the only one that can be used in validating educational objectives in Pakistan.

To apply the democratic values in a society the curriculum planners must relate the objectives and keep in view democratic values and principles.

As the principles of democracy are very difficult so no summarized statement of these can be used successfully in the justification of educational objectives.

But if the objectives are directly related to democratic principles based upon reasoning and critical thinking then they are also called valid.

Selection and organizing of Learning experiences   (Content)

Selection of course content for a subject to teach. The subject should encompass all the possible experiences a learner need at that level. All the material which was taught in past and now should be the part of the subject. With the expansion in knowledge new topics emerged with time, they must be incorporated into each subject. With the expansion of knowledge the principle of complete coverage was replace with the principle of representative content because the attainment of total coverage became difficult.

The principles of subject matter selection

1. The course content must be significant in the same field of knowledge:

This is principle for program of studies consisting of specialized courses, with each course being followed by a more advanced course (from simple to complex).

 2. The subject matter selected must possess the principle of survival:

The subject matter should have the ability to survive. Those subjects content survived for long which inspire people and benefit mankind, to fulfill the need of the society in spite of continues change in society and culture

3. The subject matter must have the principle of interest:

Keep in view the principle of interest to motivate the learners to learn more.

4. The content or subject matter must be utilized:

(The Principle of Utility)

The Principle of Utility means that the knowledge presented must be helpful in real life situation and the society well benefit from the outcome of the subject content.

5. The course content should contribute to the development of an Islamic society:

The content selected should helpful in the development of character of Muslims. It should inculcate the ideology and values of a true Muslim. It should inculcate the required skills which is beneficial for an Islamic society. There should be no such content which contradict Islamic values

Some common Considerations for the curriculum organizers

•       Subject matter should consist of physical and mental activities.

•       the subject Content should be helpful in the development of creative abilities in individual

•        logical sequence from simple to complex

•       the subject Content Help in the attaining of the objectives of the relevant course

•       incorporate the best information from all sources in Subject matter

Procedure of Content Selection:

Different procedures for content selection.

The judgmental procedure

The analytical procedure

The consensual procedure

The experimental procedure

The judgmental procedure:

This procedure is all about the vision of the curriculum planner, the success or failure depends upon the curriculum planner. The curriculum planner should have the vision of past, present and future to see the potentialities of all three dimensions. It is very hard that the judgment of curriculum planner will lead to the best selection of subject matter, for that the curriculum planner must be objective in the selection of content and have knowledge for the benefit of others.

The analytical procedure:

The analytical method is the most commonly used method of content selection. It consists of various techniques to collect information regarding subject matter selection:

1. Conducting interviews
2. Collecting information through questionnaires
3. Gathering information through documentary analysis.
4. Observing the performance of people.

The consensual procedures:

It is the opinion of the people in society who reached to some level of expertise in a field; excellent in the fields of business, industry, agriculture and Experts as teachers, physicians, engineers, and artists etc. these are the people who represent the society.

The experimental procedure

In this method the content is selected after applying different test;

1. Selection of content matter against some standard criterion (say interest).

2. Hypothesis is formulated that the selected content matter meets the criterion (interest)

3. Hypothesis is tested after gathering information from students and teachers and tested with instrument.

Teaching Methods/Strategies

Teaching Methods/Strategies

Teaching method play a very important role in the process of curriculum development, this element of curriculum development process help in the attainment of desired objectives. It is the dynamic side of the curriculum because without proper teaching methods one can’t think of achieving the targets goal of education planner, it’s largely depends upon the methods adopted in the classroom for teaching learning process. This process includes:

Teacher’s activities.

Student’s activities.

This is also the work of curriculum planners to suggest proper teaching methods for the suggested curriculum because during planning curriculum they have to keep the methodology in mind, one can’t include such topics in curriculum which are difficult to teach and for such curriculum, and the curriculum planner must have some proper method in mind.

There are various methods of teaching  but the curriculum have to  keep financial restraint in mind so for that reason they have to suggest such methods which are applicable in classroom . There are methods as lecture, lecture demonstration, problem solving, project, programmed learning etc.  For achieving the aims and objectives of the curriculum the curriculum planner must suggest and impose the innovative and active approaches of teaching and learning to initiate the interest of students and teacher as well as the parents.

Bases for Selecting Instructional Methods:

As discuss earlier there are multiple methods of teaching/ instruction, teacher always face the problem of to adopt which method for different lessons. Therefore it is the work of curriculum planner to suggest such methods in advance so the teacher has no problem to search for it.

Guidelines for the selection of teaching / instructional methods:

Achievement of objectives:

For the teacher to adopt a method for teaching learning he must keep in mind the objective of the curriculum, how to get that objective, instructional objectives is the first consideration in planning for teaching. Such a general objective could be achieved through multiple ways, but specific objectives like the student will be able to write an easy on a given topic narrow the choices considerably.

Principles of learning

The teacher should have the knowledge of individual differences, principles and theories of learning while selecting a teaching method for instruction. It would help him in the adaptation of proper method for teaching learning process in the classroom for a larger number of students with different IQs.

Individual learning styles:

Researchers believes that the most effective learning takes place when the teacher used interactive  techniques in teaching learning process, the method that suited to the individual student keeping in view the individual differences of the students and impart knowledge to the learner according to his mental ability. “Optimal for one person is not optimal for another”.                                      Cont….

The Rand Corporation Study (1971) supports these findings by stating that “teacher, student, instructional method, and perhaps, other aspects of the educational process interact with each other. Thus a teacher who works well (is effective) with one type of student using one method, he might be ineffective when working with another method. The effectiveness of a teacher, or method, or whatever varies from one situation to another”.

Self-fulfilling processes and educational stratification

The teachers know about the potentialities of the students in classroom  and the fact that every child differ from other in the learning process , keeping in view this, the teacher adopt such methods which help all the students in their own way  of learning . B.F Skinner stated “we need to find practices which permit all teachers to teach well and under which all students learn as efficiently as their talents permit”

The teacher should try to develop the potentialities of students which they already have and give them opportunities to develop those potentialities to the utmost level.

Facilities, equipment and resources

The teacher know what he has in hand in the form of equipment, audio-visual aids, resources and facilities, therefore he should plan Instructional planning In the light of available resources. It is the ability of a teacher to use minimum resources, equipments of school and achieve maximum outcome, using methods and activities that involve student in a highly active role with the minimum resources.

Accountability

Accountability is also very important factor in the curriculum development process, Teachers, administrators and others who has the responsibility of the education held responsible for the quality of education

The process of accountability means that someone has to justify his work and responsibility to someone else, so it is the circle of accountability where everybody is answerable to other. It involves continued evaluation, review of the people in the process.

It demands results, costs of producing these results. All the stockholders observe and judge the  school,  the teachers, the administrators and the supervisors  whether  the  students gain certain skills and knowledge  for which  curriculum are developed, and what methods are they adopting for imparting knowledge  of different subjects to students.

Some professionals consider pupil behaviors as the source of methods

 According to Shepherd and Regan (1982; p 127) “Methods are content free and not derived from organized subject matter.

It has been argued that methods are derived from an analysis and application of learning theories. The actions, procedures and manipulations of the teacher are not different during instruction or reading or teaching mathematics. Method is like a vehicle, which is empty but can carry a variety of subject matter. This vehicle is created and constructed from generalizations, principles and assumptions”.

Some, professionals feel that every teacher has its own unique method of teaching which represent a form of his personality. Teachers used those methods which they found easy, as It evident  from the everyday practice of teachers, even some teacher create their own methods in the classroom to teach, keeping in view the behavior of the students and the situation of that time

Assessment:

Assessment is very important process of the curriculum development. Assessment of student’s academic achievement for the purpose of evaluation of the overall progress of education. Evaluation gives curriculum planner the tools, techniques and processes for defining, gathering and interpreting data relevant to the goals and objectives of the curriculum.

Evaluation helps on all aspects of curriculum planning, administrating and evaluation of the curriculum. it tells, to what extent the curriculum is good and what are the weaknesses of the curriculum and implementing the  curriculum, what need to be done to improve the curriculum development process , the administrating process and the teaching methods of the teacher and where system lack the resources .

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Statistics for a Psychology Thesis

Statistics for a Psychology Thesis

The Big Picture: It all starts with a research question. We design or obtain empirical data that might assist in answering a research question. Statistics is a tool for summarising empirical reality and answering questions. Knowing how to link statistical analysis with the research question is a critical skill. One reason that psychology is special is that it attempts to ground its knowledge in empirical reality. We put our ideas to the test. We are taught to be scientist-practitioners.
Staying open minded: There is often a lot of pressure to obtain certain results, support certain hypotheses or test various complex statistical models. My advice: Stuff them all. Be ethical. Stay true to yourself. Let the data speak to you in all its non-conforming brutal honesty. When you analyse data, discard all agendas. If the sample size is too small to say much conclusively, acknowledge this. If the data does not support your hypotheses, accept it and try to understand why. If you have data based on a correlational design, acknowledge that there are many other competing explanations to the particular causal relationship you might be proposing. The whole point of the empirical process is ABSOLUTELY NOT to act as a checkbox for some ill-conceived theory.
Democracy and statistics: Ideologically based positions are common in public debate. Well designed and analysed empirical studies can be powerful in setting out the “facts” that any side of a debate needs to acknowledge. However, empirical research can be biased and hijacked for particular agendas. Having citizens that are able to critically evaluate empirical research and are able to honestly and skilfully conduct and analyse their own research is important for maintaining a healthy democracy. The rhetorical question I ask often is: “Can you create knowledge from empirical observations? Or must you rely on others to digest it for you?”
Statistics as reasoned decision making: Perhaps because of statistics association with mathematics or perhaps because of the way we are taught statistics and associated rules of thumb, it may appear like there is always a right and wrong way to do statistics. In reality, statistics is just like other domains. There are different ways of doing what we do, and the key is to justify our choices based on reasoned decision making. Reasoned decision making involves weighing up the pros and cons of different choices in terms of such factors as the purpose of the analyses, the nature of the data, and recommendations from statistics textbooks and journals. The idea is to explain your reasons in a logical and coherent way just as you would justify any other decision in life.
Null Hypothesis Significance Testing (NHST): a p value indicates the probability of observing results in a sample as or more extreme as those obtained assuming the null hypothesis is true. NHST is a tool for ruling out random sampling as an explanation for the observed relationship. Failing to reject the null hypothesis does not prove the null hypothesis. Statistical significance does not equal practical importance.
A modern orientation to data analysis: Answers to research questions depend on the status of population parameters. Empirical research aims to estimate population parameters (e.g., size of a correlation, size of group differences, etc.). NHST is still relevant. However, confidence intervals around effect sizes and a general orientation of meta-analytic thinking leads to better thinking about research problems, results interpretation and study design than does NHST.
Effect Size: Thinking about effect sizes is a philosophical shift which emphasises thinking about the practical importance of research findings. Effect size measures may be standardised (e.g., cohen’s d, r, odds ratio, etc.) or unstandardised (e.g., difference between group means, unstandardised regression coefficient, etc.). Think about what this means for practitioners using the knowledge. Contextualise the effect size in terms of its statistical definition, prior research in the area, prior research in the broader discipline and only finally using Cohen’s rules of thumb.
Confidence Intervals: Confidence intervals indicate how confident we can be that the population parameter is between given values (e.g., 95% confidence). Confidence intervals focus our attention on population values, which is what theory is all about. They highlight our degree of uncertainty. If the confidence interval includes the null hypothesis value, we know that we do not have a statistically significant result. In this way confidence intervals provide similar information as NHST, but also much more.
Power Analysis: Having an adequate sample size to assess your research question is important. Statistical power is the probability of finding a statistically significant result for a particular parameter in a particular study where the null hypothesis is false. Power increases with larger population effect sizes, larger sample sizes and less stringent alpha. G-Power 3 (just Google G Power 3) is excellent free software for running power analyses.
Accuracy in Parameter Estimation (AIPE): Power analysis is aligned with NHST. AIPE is aligned with confidence intervals around effect sizes and meta-analytic thinking. AIPE attempts to work out the size of the confidence interval we will have for any given sample size and effect size. The aim is to have a sample size that will give us sufficiently small confidence intervals around our obtained effect sizes to draw the conclusions about effect sizes that we want to draw.
Meta Analytic thinking: Meta analytic thinking involves "a) the prospective formulation of study expectations and design by explicitly invoking prior effect size measures and b) the retrospective interpretation of new results, once they are in hand, via explicit, direct comparison with the prior effect sizes in the related literature" (Thompson, 2008, p.28). This approach incorporates the idea that we read the literature in terms of confidence intervals around effect sizes and we design studies with sufficient power to test for the effect size and sufficient potential to refine our estimate of the parameter under study.
Sharing data with the world: Imagine the potential for knowledge advancement if data underlying published articles was readily assessable to be re-analysed. You could learn about data analysis by trying to replicate analyses on data similar to your thesis. You could do meta-analyses using the complete data sets. You could run analyses that the original authors did not report. You could be an active consumer of their results, rather than a passive receiver. Others would be more receptive to your ideas if they could subject your analyses to scrutiny. Such a model fits with the idea of being open minded, distributing knowledge, and emphasising meta-analytic thinking. In many situations concerns about confidentiality, intellectual property, and the data collector’s right to first publish can be overcome. The message: Consider making your data publicly available after you have published it in a journal.
Software: Be aware of the different statistical packages that are available. SPSS is relatively easy to use. “R” (www.r-project.org/) is an open source (i.e., free software) alternative and is worth learning if you want to become a serious data analyst. It has cutting edge features (e.g., polychoric correlations, bootstrapping, reports for psychological tests, meta analysis, multilevel modelling, item analysis, etc.) , amazing potential for automation and customised output, and encourages a better orientation towards running analyses. Results can be fed back into subsequent analyses; graphs and output can be customised to your needs; it forces you to document your analysis process; it generally requires that you know a little more about what you are doing; and it leads to an approach of being responsive to what the data is saying and adjusting analyses accordingly. For an introduction for psychologists, see (personality-project.org/r/r.guide.html).
Learning Statistics: For many people in psychology, statistics is not something done everyday. A strategy is needed to identify and acquire the skills required to analyse your thesis data. Set out a statistical self-development plan possibly in conjunction with a statistical adviser, identifying things such as books and chapters to read, practice exercises to do, formal courses to do, etc. It is important to get practical experience analysing other datasets before you tackle your thesis dataset.
The right books: It is critical to have the right resources. Get a comprehensive multivariate book (Tabachnick & Fiddel – Using Multivariate Statistics or Hair et al – Multivariate Data Analysis). Get a clear, entertaining, insightful and SPSS-focused book (Field – Discovering Statistics Using SPSS). Get an easy to follow SPSS cookbook for doing your thesis (Pallant – SPSS Survival Manual).
Using statistical consultants: be prepared; be clear about your questions; recognise that statistical consultants are there to provide advice about options and that many decisions are intimately tied up with theoretical considerations and should be made by the researcher.
Taking your time: As Wright (2003) so aptly put it: “Conducting data analysis is like drinking a fine wine. It is important to swirl and sniff the wine, to unpack the complex bouquet and to appreciate the experience.” A good dataset often has a lot to say. When we’ve often spent many months designing and collecting data, it is important to give the data the time to speak to us. Often, this will require us to change how we conceptualise the phenomena. Explore the data; produce lots of graphs; consider the individual cases; assess the assumptions; reflect on the statistical models used; reflect on the metrics of the variables used; and value basic descriptive statistics.
Telling a story: The results section should be the most interesting section of a thesis. It should show how your results answer your research question. It should show the reasons for your statistical decisions. It should explain why the statistical output is interesting. You’ve whet the reader’s appetite with the introduction and method, the results section is where you get to convert your empirical observations into a contribution that advances the sum of all human knowledge.

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Evaluating the Potential Incorporation of R into Research Methods Education in Psychology

I was recently completing some professional development activities that required me to write a report on a self-chosen topic related to diversity in student backgrounds. I chose to use the opportunity to reflect on the potential for using R to teach psychology students research methods. I thought I'd share the report in case it interests anyone.

Abstract

Research methods is fundamental to psychology education at university. Recently, open source software called R has become a compelling alternative to the traditionally used proprietary software called SPSS for teaching research methods. However, despite many strong equity and pedagogical arguments for the use of R, there are also many risks associated with its use. This report reviews the literature on the role of technology in research methods university education. It then reviews literature on the diversity of psychology students in terms of motivations, mathematical backgrounds, and career goals. These reviews are then integrated with a pedagogical assessment of the pros and cons of SPSS and R. Finally, recommendations are made regarding how R could be best implemented in psychology research methods teaching.

Introduction

Training in research methods is a fundamental component of university education in psychology. However, for many reasons subjects in research methods are challenging to teach. Students have diverse mathematical, statistical, and computational backgrounds; students often lack motivation as they struggle to see the relevance of statistics. These issues are compounded by undergraduate majors in psychology that typically have several compulsory research methods subjects. Given the competition for entry into fourth year and post-graduate programs, such research methods subjects can be threatening to struggling students.
As with many other universities, research methods in psychology at Deakin University has largely been taught using software called SPSS. This software is typically taught as a menu driven program that is used to analyse data enabling standard data manipulation, analyses, and plotting. While SPSS is relatively user-friendly for standard analyses, there are several problems with teaching students how to use it. In particular, it is very expensive; thus, students can not be assumed to have access to it either from home for doing assignments or in future jobs. In addition, while SPSS makes it easy to perform standard analyses, it is very difficult to alter what SPSS does to perform novel analyses. Thus, for many reasons some lecturers are seeking alternative statistical software for teaching research methods.
While there are many programs for performing statistical analysis, one particularly promising program, known simply as "R", has emerged as a viable alternative to SPSS. R is open source so it is free for students and staff. Thus, students can use R from home when completing assignments, and can use it in any future job. It has a vast array of statistical functionality. Despite these benefits, it does present several challenges to incorporation into psychology. Analyses are typically performed using scripts. It is often less clear how to run certain analyses. The program often assumes a mental model of a statistician rather than an applied researcher.
Thus, the current report had the following aims. The first aim was to evaluate the pros and cons of using R to teaching psychology students research methods. The second aim was to evaluate how best R could be incorporated. In order to achieve these aims, the report is structured into several parts. First, the general literature of software in statistics education is reviewed. A particular focus is placed on diversity in student backgrounds in applied fields. Second, the backgrounds and career goals of psychology students are presented with reference to the literature and practical experience. Third, the pros and cons of using R versus SPSS is presented. Finally, ideas about how best to incorporate R into statistics education are reviewed.

Statistics education and the role of software

There is a substantial literature on statistics education and the role of statistical software in statistics education. Tiskovskaya and Lancaster (2012) provide one review of the challenges in statistics education. Their review is structured around teaching and learning, statistical literacy, and statistics as a profession. Of particular relevance to teaching statistics in psychology they outline several problems and provide relevant references to the statistical literature. With references taken from their paper, these issues include: inability to apply mathematics to real world problems (e.g., Garfield, 1995); mathematics and statistics anxiety and motivation issues in students (e.g., Gal & Ginsburg, 1994); inherent difficulty in students understanding probability and statistics (e.g., Garfield & Ben-Zvi, 2008); problems with background mathematical and statistical knowledge (e.g., Batanero et al 1994); the need to develop statistical literacy which translates into everyday life (e.g., Gal, 2002); and the need to develop assessment tools to evaluate statistical literacy. Tiskovskaya and Lancaster (2012) also reviewed potential statistics teaching reforms. They note that there is a need to provide contextualised practice, foster statistical literacy, and create an active learning environment.
Of particular relevance to the current review of statistical software, Tiskovskaya and Lancaster (2012) discuss the role of technology in statistics education. The importance of technology has increased as computers have become more powerful. This has enabled students to run powerful statistical programs on their computer. Some teachers have used this power to focus instruction on interpretation of statistical results rather than computational mechanics. Chance et al (2007) further note the value of using interactive applets to explore statistical concepts and taking advantage of internet resources in teaching.
Chance et al's (2007) review also summarises several useful suggestions for incorporating technology in statistics education. Moore (1997) notes the importance of balancing using technology as a tool with remembering that the aim is to teach statistics and not the tool per se. Chance et al (2007) notes particularly valuable uses of technology include analysing real datasets, exploring data graphically, and performing simulations. Chance et al (2007) also review statistical software packages for statistics education noting both the advantages and disadvantages of menu-driven applications such as SPSS.
Chance et al (2007) offer several recommendations for incorporating technology into statistical education. First, they highlight the importance of getting students practicing not just performing analyses, but also focusing on interpretation. Second, they recommend that tasks be carefully structured around exploration so that students see the bigger picture and do not get overwhelmed with software implementation issues. Third, collaborative exercises can force students to justify to their fellow students their reasoning. Fourth, they encourage the use of cycles of prediction and testing, which technology can facilitate (e.g., proposing a hypothesis for a simulation and then testing it).
Chance et al (2007) summarise the GAISE report by Franklin and Garfield (2006) on issues to consider when choosing software to teach statistics. These include (a) "ease of data entry, ability to import data in multiple formats, (b) Interactive capabilities, (c) Dynamic linking between data, graphical, and numerical analyses, (d) Ease of use for particular audiences, and (f) Availability to students, portability" (p.19). Franklin and Garfield (2006) also discuss a range of other implementation issues, such as the amount of time to allocate to software exploration, how much the software will be used in the course, and how accessible the software will be outside class. Garfield (1995) suggest that computers should be used to encourage students to explore data using analysis and visualisation tools. Running simulations and exploring resulting properties is also particularly useful. Thus, overall these general considerations regarding statistics education can inform the choice of statistical software. However, the above review also highlights that choice of software is only a small part of the overall unit design process.

Psychology students and the role of statistics

Pathways of psychological studies in Australian universities typically involve completing a three year undergraduate major in psychology, then a fourth year, followed by post-graduate professional or research degrees at masters or doctoral level. As a result of student interest, specialisation, and competition for places, there is a reduction over year levels. From my experience both at Melbourne University and Deakin University, a ball park estimate of the student numbers as a percentage of first year load, would be 40% at second year, 35% at third year, 10% at fourth year, and 3% at postgraduate level. This is from one to two thousand students at first year. Of course these are just rough estimates, but the point is to highlight that there are huge numbers of students getting a basic undergraduate education in psychology; in contrast, the few that go on to fourth year have both a high skill level in psychology also different needs regarding research methods.
Psychology students are taught using the scientist-practitioner model. A big part of science in psychology is research methods and statistics. Students typically complete two or three research methods subjects at undergraduate level, another unit in fourth year, and potentially further units at postgraduate level. The diverse nature of psychology student backgrounds, motivations, and career outcomes can make research methods a difficult subject to design and teach. Psychology undergraduate students also have diverse career goals and outcomes. Many go on to some form of further study. Those that exit at the end of third year have diverse employment outcomes. For example, Borden and Rajecki describe one US sample finding that income was lower than many other majors and that roles included administrative support (17.6%), social worker (12.6%), counsellor (7.6%) along with a diverse range of other jobs. Of those that go on, some will continue with research, but others will go into some form of applied practice.
In terms of research methods in psychology, there are a diverse range of goals. First, research methods is meant to help all students learn to reason about the scientific literature in psychology. Second, for students who continue with psychology research methods should give students the skills to be able to complete a quantitative fourth year and postgraduate thesis. For a subset of students, quantitative skills is part of their marketable skillset that they can take into future employment. Furthermore, for a small group of students who go on to do their PhD and then join academia, research methods skills are fundamental to the continuation of good research and the vitality of the discipline.
In addition to diverse aims are the diverse student backgrounds in psychology. In particular, there are typically no mathematics pre-requisites. By casual observation many students seem motivated to find work in the helping professions, and particularly as clinical psychologists. Many studies have discussed the challenges of teaching statistics to psychology students. For example LaLonde and Gardner (1993) proposed and tested a model of statistics achievement that combined mathematical aptitude and effort with anxiety and motivation as predictors.
Thus, in combination this diversity in background and student goals introduce several challenges when teaching research methods. For some students the main goal is to introduce a moderate degree of statistical literacy. For others, it is essential that they are at least able to analyse their thesis data in a basic way. A final group of advanced students needs skills that will allow them to model their data in a sophisticated way to contribute to the research literature. Thus, there is a tension between presenting ideas in an accessible way for all students versus tailoring the material for advanced students so they can truly excel.
This tension exists in many different aspects of research methods curriculum. Research methods can be taught with varying degrees of mathematical rigour and abstraction. Teaching can emphasise interpreting output or it can emphasise computational processes. It can also vary in the prominence of software versus ideas. In particular the correct choice of statistical software can substantially interact with these issues of balancing rigour with accessibility. In particular, tools like SPSS are more limited than R, but such limits can make standard analyses easier.
Aiken et al (2008) reviewed doctoral education in statistics and found that most surveyed programs were using SAS or SPSS primarily. They described a case study in curricular innovation in terms of novel topics emerging followed by initiatives from substantive researchers. Textbooks and software that make techniques accessible to psychology graduates also facilitate the teaching process. In some respects, as R has become more accessible through usability innovation and as the needs of data analysts have become more advanced, the argument for R has become more compelling.

Whether to use R in psychology research methods

Pros and cons of R

The above review thus provides a background for understanding both statistics education in general and the diversity in the background and goals of psychological students. The following analysis compares and contrasts R and SPSS as software for teaching research methods in psychology. This initial comparison focuses on price, features, usability and other considerations.
In terms of price, an initial benefit of R is that it is free. It is developed under the GNU open-source licence. It is free to the university and free to students. In contrast a student licence to SPSS for a year is around $200; A professional licence is around $2,000; and SPSS charges expensive licencing fees to the university. R would make it easier to get students to complete analyses from home. Requiring students to purchase SPSS creates equity issues and may even encourage some students to engage in software piracy. If as Devlin et al (2008) suggest that essential textbooks create economic hardship, even more expensive statistical software would compound this problem.
In terms of features, SPSS and R both run on Windows, OSX, and Linux. They both support most standard analyses that students may wish to run. However, R has a larger array of contributed packages. SPSS has several features including a data entry tool, a menu-driven GUI, and an output management system for tables and plots that R does not have. R makes it a lot easier to customise analyses, perform reproducible research, and simulations.
In terms of flexibility SPSS and R both have options for performing flexible analyses. However, R makes it a lot easier to gradually introduce customisation by building on standard analyses. It is also flexible in how it can be used because of the open source licence. R is particularly suited to advanced students who can benefit from the easier pathway it provides for growing statistical sophistication.
In terms of usability R and SPSS are quite different. R assumes greater knowledge about statistics. SPSS has an interface that is more familiar to standard Windows-based programs. R is a programming language with a less consistent mental model to standard Windows programs. R has a steeper initial learning curve, but shallower intermediate curve. R encourages students to gradually develop statistical skills. In particular R has several quirks which create difficulties for the novices (e.g., learning details of syntax, escaping spaces in file paths, treating strings as factors versus character variables, etc.). There are also many things that are easy in SPSS that are difficult in R. Some examples include: variable labels and modifying meta data, editing loaded data, browsing loaded data, producing tables of output, viewing and browsing statistical output, generating all the possible bits of output for an analysis, importing data, standard analyses that SPSS already does, and interactive plotting.
R and SPSS can also be compared in terms of existing resources. There are many online resources for both R and SPSS. Psychology-specific R resources exist but are less plentiful than for SPSS. Furthermore, existing psychology supervisors, research methods staff, and tutors are probably more familiar with SPSS which may cause issues when transitioning teaching to R. That said, many supervisors either train their students directly in the software that they want their students to use or they let the student handle details of implementation.

Mental Models

When choosing between SPSS and R it is worth considering the mental models required to use SPSS and R. These mental models both guide what needs to be trained and also may suggest the gap that needs to be closed between students' initial mental models and that which is required by the software.
The SPSS mental model is centred around a dataset. The typical workflow is as follows: (a) import or create data; (b) define meta data; (c) menus guide analysis choice; (d) dialog boxes guide choices within analyses; (e) large amounts of output are produced; (f) instructional material facilitates interpretation of output; (g) output can be copy and pasted into Word or another program for a final report. Custom statistical functions or taking SPSS output and using it as input to subsequent functions is not encouraged for regular users. Thus, overall the system guides the user in the analysis.
In contrast, R requires that the user guides the software. Thus, the R workflow is as follows: (a) Setup raw data in another program; (b) import data where often the user will have multiple datasets, meta datasets, and other data objects (e.g., vectors, tables of output); (c) transform data as required using a range of commands; (d) perform analyses, where command identification may involve a Google search or looking up a book, and understanding arguments in a command can be facilitated by internal documentation and online tools; (e) because the resulting output is minimal, the user often has to ask for specific output using additional commands; (f) much of what is standard in SPSS requires a custom command in R, but also much of which does not exist in SPSS can be readily created by an intermediate user; it is much easier to extract out particular statistical results and use that as input for subsequent functions; (g) while output can be incorporated into Word or Excel, users are encouraged to engage in various workflows that emphasise reproducible research.

Summary

Thus, overall SPSS is well suited to a menu-driven standardised analysis workflow which meets the needs of many psychology students. R is particularly suited to statisticians that need to perform a diverse range of analyses and are more comfortable with computer programming and statistics in general. R requires greater statistical knowledge and it encourages students to have a plan for their analyses. R also requires students to learn more about computing including programming, the command-line, file formats, and advanced file management. The emphasis on commands creates a greater demand on declarative memory which in turn makes R more suited to students who will perform statistical analysis more regularly. However, the flexibility and nature of R means that it can be used in many more contexts than SPSS such as demonstrating statistical ideas through simulation.
Overall, there are clearly pros and cons of both SPSS and R. R is particularly suited to more advanced students. Occasional users may be more productive initially with SPSS. That said, the many students who never go on with any data analysis work, may learn as much or more by using R. It also remains an empirical question to see how different psychology students might handle R. Thus, the remainder of this report focuses on what implementation of how R could be implemented most effectively.

How to use R in psychology research methods

When considering implementation of R in psychology, it is useful to look at existing textbooks and course implementations. When considering textbooks, it is important to note that psychology tends to use a particular subset of statistical analyses. It also often has analysis goals that differ from other fields. For example, there is a greater emphasis on theoretical meaning, effect sizes, complex experimental designs, test reliability, and causal interpretation. While there are many textbooks that teach statistics using R, only recently have books emerged that are specifically designed to teach R to psychology students. The two main books are Andy Field's "Discovering Statistics Using R" and Dan Navarro's "Learning Statistics with R". An alternative model is to take a more generic R textbook or online resource and combine it with a more traditional psychology textbook such as David Howell's "Statistical Methods for Psychology". In particular, there are many user friendly online resources for learning R such as http://www.statmethods.net/ or Venables, Smith and the R Core Team's "An Introduction to R". Whatever textbook option is chosen an important part of learning R involves learning how to get help. Thus, training should include learning how to navigate online learning resources and internet question and answer sites that are very effective in the case of R (e.g., stackoverflow.com).
Dan Navarro (2013) has written a textbook that teaches statistics to psychology students using R. Navarro (2013) presents several argument for using R instead of a different commercial statistics package. These include: (1) the benefits of the software being free and not locking yourself into expensive proprietary software; (2) that R is highly extensible and has many cutting edge statistical techniques; and (3) that R is a programming language and learning to program is a good thing. He also observes that while R has its problems and challenges, overall it provides the best current available option. Thus, overall, his approach is to inspire the student to see the bigger picture about why they are learning R. Navarro then spends two chapters introducing the R programming language. Starting with simple calculations, many basic concepts of variables, assignment, extracting data, and functions are introduced. Then, standard statistical techniques such as ANOVA and regression are presented with R implementations.
Overall, both these textbooks provide insight into how R could be implemented. Teaching with R provides some opportunity to teach statistics in a slightly deeper way. However, various recipes can be provided to perform standard analyses. Teaching R also requires taking a little extra time to teach the language. The menu-driven interface to R called R-Commander also provides a way of introducing R in a more accessible way. The infrastructure provided by R also provides the opportunity to introduce many important topics such as bootstrapping, simulation, power analysis, and customised formulas. Weekly analysis homework not easily possible with SPSS could consolidate R specific skills.
An additional issue of implementation relates to when R should be introduced. Fourth year provides one such opportunity where the students that remain at this level tend to be more capable and have some initial experience in statistics. Fourth year research methods is a very important subject. It is often designed to prepare students to analyse multivariate data. It is also designed to prepare students to be able to analyse data on their own including preliminary analyses, data cleaning, and transformations. R supports all the standard multivariate techniques that are currently taught at fourth year level. These include PCA, factor analysis, logistic regression, DFA, multiple regression, multilevel modelling, CFA, and SEM. R also makes it easier to explore more advanced methods such as bootstrapping and simulations.

Conclusion

Ultimately, it is an empirical question as to whether using R would provide a more effective tools for research methods education in psychology. It may be useful to explore the idea with some low-stakes optional post-graduate training modules in R. Such programs may give a sense of the kinds of practical issues that arise with students when learning to use R. If R is to be rolled out to all of fourth year psychology, this would be a high risk exercise. It would be important to evaluate the student learning outcomes in a broad way. In particular, it would be important to see any effect on analysis performance in fourth year theses.

References

• Aiken, L. S., West, S. G., & Millsap, R. E. (2008). Doctoral training in statistics, measurement, and methodology in psychology: Replication and extension of Aiken, West, Sechrest, and Reno's (1990) survey of PhD programs in North America. The American Psychologist, 63(1), 32-50.
• Batanero, C., Godino, J., Green, D., and Holmes, P. (1994). Errors and Difficulties in Understanding Introductory Statistical Concepts. International Journal of Mathematical Education in Science and Technology, 25 (4), 527–547.
• Borden, V. M., & Rajecki, D. W. (2000). First-year employment outcomes of psychology baccalaureates: Relatedness, preparedness, and prospects.Teaching of Psychology, 27(3), 164-168.
• Chance, B., Ben-Zvi, D., Garfield, J., and Medina, E. (2007). The Role of Technology in Improving Student Learning of Statistics. Technology Innovations in Statistics Education, 1(1). url: http://www.escholarship.org/uc/item/8sd2t4rr
• Devlin, M., James, R., & Grigg, G. (2008). Studying and working: A national study of student finances and student engagement. Tertiary Education and Management, 14(2), 111-122.
• Franklin, C. & Garfield, J. (2006). The GAISE (Guidelines for Assessment and Instruction in Statistics Education) project: Developing statistics education guidelines for pre K-12 and college courses. In G. Burrill (Ed.), 2006 NCTM Yearbook: Thinking and reasoning with data and chance. Reston, VA: National Council of Teachers of Mathematics.
• Gal, I. (2002). Adults' Statistical Literacy: Meanings, Components, Responsibilities. With Discussion. International Statistical Review, 70(1), 1-51.
• Gal, I. & Ginsburg, L. (1994). The Role of Beliefs and Attitudes in Learning Statistics: Towards an Assessment Framework. Journal of Statistics Education, 2(2). url: http://www.amstat.org/publications/jse/v2n2/gal.html
• Garfield, J. (1995). How Students Learn Statistics. International Statistical Review, 63(1), 25-34.
• Garfield, J. and Ben-Zvi, D. (2008). Developing Students' Statistical Reasoning: Connecting Research and Teaching Practice, Springer.
• Lalonde, R. N., & Gardner, R. C. (1993). Statistics as a second language? A model for predicting performance in psychology students. Canadian Journal of Behavioural Science, 25, 108-125.
• Moore, D.S. (1997). New pedagogy and new content: the case of statistics. International Statistical Review, 635, 123-165.
• Navarro, D. (2013). Learning statistics with R: A tutorial for psychology students and other beginners (Version 0.3) url: http://ua.edu.au/ccs/teaching/lsr
• Tishkovskaya, S., & Lancaster, G. (2012). Statistical education in the 21st century: a review of challenges, teaching innovations and strategies for reform. Journal of Statistics Education, 20(2), 1-55.
• Wiberg, M. (2009). Teaching statistics in integration with psychology. Journal of Statistics Education, 17(1), 1-16.

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The London Schools Effect - what have we learned this week?

Perhaps the biggest question in education policy over the past few years
is why the outcomes for London schools have been improving so much
faster than in the rest of the country. I wrote about it here
last year. Until now there's been little in the way of research into
the question but last week two reports came out - one by the IFS and one
from CFBT - that seek to provide some answers.



They both agree that the change in GCSE results has been spectacular.
There's plenty of data in both reports on this but I found this graph
from the IFS particularly powerful because it relates to a metric that
isn't something schools are held accountable to - and so feels like
authentic proof that something extraordinary has happened in London.

But what, exactly, has happened? Here the two reports seem to disagree. According to the IFS - whose analysis is purely quantitative the main reasons are:

• Changes in pupil and school characteristics - in particular London
  and other inner-city areas have seen an increase in pupils from a range
  of ethnic backgrounds (partly) as a result of immigration. The IFS
  analysis suggests this accounts for about half the improvement in London
  between 2002-2012.
• Changes in "prior attainment" - the authors argue that once higher
  levels of attainment in key stage 2 (end of primary) tests are taken
  into account then the "London effect" in secondaries looks less
  impressive. Indeed once prior attainment and changes in pupil/school
  characteristics have been controlled for the gap between London and the
  rest of the country falls from 21 percentage points in the  5 A*-C GCSE
  with English and Maths measure to just 5 percentage points. Moreover
  this gap is fairly stable between 2002-2012 - though it does increase a
  by about 2 percentage points over the period.
• There was a big increase in key stage 2 schools for disadvantaged
  pupils between 1999-2003 and that led to big increases in GCSE scores
  for these pupils between 2004-08 - but the GCSE improvement was actually
  the result of prior attainment. The authors hypothesise this may be due
  to the introduction of "national strategies" in primary literacy and
  numeracy in the late 90s - these were piloted in inner London
  authorities (as well as some other urban areas e.g. Liverpool).
• London secondaries do have a better record at getting disadvantaged
  pupils to stay in education post-16. After controlling for pupil/school
  characteristics they are around 10 percentage points more likely to stay
  in education.

The CFBT report
does include quantitative analysis but is much more focus on
qualitative research - specifically interviews with headteachers,
academics, civil servants and other experts. This report argues the key
reasons for London's improvement are:

• Four key "improvement interventions" between 2002 and 2014 - the
  "London Challenge" (a Labour initiative that used data to
  focus attention on weaker schools and used better schools to support
  their improvement); Teach First; the introduction of sponsored
  academies; and improvements driven by local authorities.
• They conclude that: "each of
  these interventions played a significant role in driving improvement.
  Evaluations of each of these interventions have overall been positive,
  although the absence of RCT evidence makes it impossible to identify the
  precise gains from each set of activities. The exact causal mix also
  varied from borough to borough because there were variations in the
  level of involvement in London Challenge, variations in the
  effectiveness of local authority activity, variations in the level of
  ‘academisation’ and variations in the level of input from Teach First."
• The authors argue that there were cross-cutting themes covering
  these interventions and the wider improvement story. In particular - the
  better use of data; practitioner-led professional development and,
  particularly, leadership - both politically and at school level.

At first glance it's hard to reconcile the positions taken in the two
reports. The IFS focus on primary, and to a lesser extent pupil
characteristics, while CFBT focus on secondary policy changes. I think,
though, they are two different bits of an extremely complicated jigsaw
that hasn't been finished yet - and because of the lack of evidence/data
- never will be. Like the apocryphal blind men with the elephant
they're looking at different parts of the whole.



1) Both reports probably underestimate the importance of changes in
pupil characteristics. CFBT completely dismiss this as a driver based on
an inadequate analysis of ethnicity data. The IFS analysis is more
comprehensive and so does pick up a significant effect but may still
miss the true extent because of the limitations of available data on
ethnicity. I think this may explain the extent of the "primary effect"
in the IFS report. Essentially they're saying the big improvements in
GCSE results are partially illusory because they were already built
into those pupils' primary attainment. However, they are unable (because
of a lack of data) to analyse whether those primary results were also partly illusory because those pupils started primary at a higher level.



There is a clue that this may be a factor in their analysis of Key Stage
1 data for more recent years. Controlling for prior attainment at KS1
reduces the "London effect" at Key Stage 2 by about half. But the
authors are unable to do this analysis for the crucial 1999-2003 period
when results really improved. They are also unable to look from the
beginning of primary - because we don't have baseline assessments when
pupils start school.



2) The IFS report probably underestimates the secondary effect. As Chris Cook has shown the London secondary effect at least doubles if you exclude equivalents.



3) The CFBT report definitely underestimates the primary effect because
it doesn't look for it. Thought there are some quotes from people who
worked in local authorities during the crucial period who highlight
their focus on literacy and numeracy during the late 90s.



So pupil characteristics; primary schools and secondary schools all seem
to have played a role in boosting attainment in London. The CFBT report
is convincing on some of the factors at play in secondaries; the IFS
report is convincing that primaries also played some kind of a role. The
big questions for me after digesting both reports:

• Are there "London specific" pupil characteristics that wouldn't be
  apparent from the available data. E.g. are immigrants who go to London
  different to those who don't? Are some of the ethnicity effects stronger
  than indentified because key groups (e.g. Polish) are hidden in larger
  categories?
• Are there policy reasons why London primaries improved faster than
  those elsewhere in the crucial 1999-2003 period? I struggle to buy the
  idea that the national strategies were the key driver here as they were
  rolled out nationally (albeit that the pilots were focused on inner
  London). But the quotes in the CFBT report suggest their might be
  something here around a general focus on literacy/numeracy. This is a
  key area for further research.
• To what extent were the policy interventions (London Challenge,
  academies etc...) the main reasons for secondary improvement? Or was it
  more to do with the number of good school leaders during that period?
  One of the most interesting tables in the CFBT report - pasted below -
  shows that inner London is the only part of the country where
  headteacher recruitment has got easier in the last ten year. And the
  importance of leadership shines through in the interviews conducted for
  the CFBT report. Is it possible to more closely identify the
  relationship between individual leaders and school improvement? What can
  we learn from these leaders?

And of course the really big question - is any of this replicable in
other areas? We're starting to see a raft of local improvement
initiatives across the country - Wales Challenge; Somerset Challenge;
North East Challenge and so on. It's really important that in these
areas we do a better job of evaluating all the interventions put in
place from the start so that if we see big improvements we have a better
understand of the causes.

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75 education people you should follow

One of the most frequent conversations I have is people asking me who
they should follow on twitter. This is my attempt to answer. It is, of
course, a highly subjective list based on people I enjoy following. But
the people here represent a wide range of views / opinions. Follow this
lot and you'll get a feel for the debate; as well as a good stream of
useful links and some great blogs.



The list is ordered alphabetically in categories. The * indicates they also have a blog that's worth reading.

 
Academics and Writers

Annie Murphy Paul: Author of the forthcoming book Brilliant: The Science of How We Get Smarter



Becky Allen*: Reader in Economics of Education at Institute of Education. Quant wizard.



Becky Francis: Professor of Education and Social Justice at King's College.



Chris Husbands*: Director of the Institute of Education



Dan Willingham: Cognitive Psychologist and Author of Why Don't Students Like School?



Daisy Christodolou*: Research and Development manager at ARK, Author, super-smart.



David Weston*: Former teacher, Chief Executive of the Teacher Development Trust



Dylan William: Professor, expert in assessment and curriculum



Gifted Phoenix: (Not his real name) Education policy analyst specialising gifted and talented



Graham Birrell: Senior Lecturer in Education at Christchurch Canterbury



Laura McInerney*: Former teacher, blogger, columnist, complete genius if a bit too Fabian.



Loic Menzies*: Researcher, Author, Blogger, Teacher Trainer.



Martin Robinson: Author and teacher trainer



Rob Coe: Professor of Education at Durham

 

Headteachers

Duncan Spalding: Norfolk Headteacher



John Tomsett*: Headteacher in York



Geoff Barton: Head in Suffolk. Frequent tweeter, occasional blogger, not a big fan of Ofqual.



Liam Collins: Head in East Sussex



Rachel de Souza: CEO of the Inspiration Trust; a forward thinking academy chain in East Anglia



Ros McMullen: Principal of David Young Community Academy in Leeds.



Tom Sherrington*: One of the best blogging heads

Journalists

Ann Mroz: Editor of the Times Education Supplement



Greg Hurst: Education Editor at The Times



Helen Warrell: Covers education for the Financial Times



Jonn Elledge: Editor of Education Investor Magazine. More left wing than that makes him sound.



Michael Shaw: Director of TESPro



Nick Linford: Editor of FE Week



Reeta Chakrabati: BBC Education Correspondent



Richard Adams: Education Editor at the Guardian



Sanchia Berg: BBC education specialist; currently on Newsnight



Sean Coughlan: BBC online education correspondent



Sian Griffiths: Education Editor at the Sunday Times



Toby Young: Free School Founder, columnist, provocateur.



Warwick Mansell*: Guardian Education Diarist, freelancer, blogger.



William Stewart: Reporter at the Times Education Supplement

Policy and Politics 

Andrew Adonis: Former education Minister and Author



Brett Wigdortz: CEO of Teach First and my boss.



Conor Ryan: Former adviser to David Blunkett and Tony Blair. Now at Sutton Trust.



Dominic Cummings: Special Adviser to Michael Gove (until January)



Fiona Millar: Columnist and campaigner for comprehensives; former adviser to Cherie Blair.



Gabriel Sahlgren: Research Director at the Centre for Market Reform of Education.



Gerard Kelly: Former Editor of the Times Education Supplement.



Graham Stuart: Chair of the Education Select Committee



Jonathan Clifton: Senior Research Fellow at IPPR, working on education and youth policy.



Jonathan Simons: Head of Education at Policy Exchange. Ex-cabinet office.



Liz Truss: Schools Minister



Matt Hancock: Post-16 Minister



Michael Barber: Chief Education Advisor at Pearson. Former Head of the PM’s Delivery Unit.



Pasi Sahlberg: Finnish education expert - author of Finnish Lessons



Robert Hill*: Former adviser to Charles Clarke and Tony Blair. Currently advising Welsh Govt.



Stephen Tall*: Development Director at the Education Endowment Foundation



Tim Leunig: Policy Adviser to David Laws



Tom Richmond: Policy Adviser to Matt Hancock



Tristram Hunt: Shadow Secretary of State for Education

Teachers

Alex Quigley*: Subject Leader of English & Assistant Head. One of my favourite bloggers.



Alex Weatherall: Science / Computer Science teacher



Andrew Old*: Anonymous teacher; caustic, brilliant, blogger and Man of Mystery



David Didau*: Teacher, Author and one of the most popular teacher bloggers.



Debra Kidd*: AST for Pedagogy, formerly Senior Lecturer in Education, MMU.



Harry Fletcher-Wood*: History teacher and CPD leader at Greenwich Free School.



Harry Webb*: Ex-pat Brit teaching in Australia. Great, analytic, blogger.



Katie Ashford*: Secondary English teacher



Keven Bartle*: Senior Leader and entertaining blogger



Kristopher Boulton*: Maths teacher at ARK King Solomon Academy



James Theobold: English teacher. Funny.



Jo (readingthebooks)*: Head of English in a London school.



Joe Kirby*: English teacher + prolific blogger



John Blake*: History teacher and Editor of Labour Teachers



Lee Donaghy*: Senior Leader at Parkview school, Birmingham.



Michael Merrick*: Teacher of many subjects and professional contrarian.



Michael Tidd*: Sussex middle school teacher (KS2) and blogger



Micon Metcalfe: Business Manager at Dunraven School. Edu-finance queen.



Red or Green Pen?*: Anonymous Maths teacher and great blogger



Stuart Lock*: Deputy Headteacher



Tessa Matthews*: Pseudonym for a English teacher + super blogger.



Thomas Starkey*: FE English teacher



Tom Bennett*: Teacher, Blogger, Author, ResearchED Founder, Scot.

And of course...

SchoolDuggery: Queen of Education on twitter; unclassifiable. Must follow.

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Rapid Progress For *Boys' Writing (*Girls Too)

There
are many challenges facing educators and students today. One of these
is the increase in the use and availability of digital technology. In
some quarters, there have been voices of concern that the widespread
prevalence of digital technology is affecting students’ social and
communication development and limiting interest and motivation for
learning.

At
Mr Andrews Online we take the opposite view. We have developed this
approach over 12 months with classroom trials in a large number and wide
range of schools, teacher consultation and feedback have led to this
unique and highly effective approach. We harness the incredible power of
technology these lesson plans teach students to write for a range of
purposes and audiences, and additionally they make the students want to
write and enjoy writing.

Rapid Progress For *Boys' Writing (*Girls' Too) is available for just £50

This book contains:

• A clear and structured approach to teaching all genres of writing using popular games as a stimulus.
• Easy to follow lesson plans which raise standards, secure rapid
  progress and make a sustained impact on all young writers, even those
  who are most reluctant.
• Note taking sheets, vocabulary ideas, sentence starters, writing prompts and over 80 ideas for writing activities.
• Typed and hand written examples from writing projects we have delivered in classroom in schools across the UK
• A transferable cross-curricular approach demonstrating a clear,
  accessible progression covering: Talk for writing/ oral rehearsal,
  planning, drafting, feedback/collaboration and digital publishing.

Inspiring outstanding writing using iPad/Android tablet games, this
revolutionary approach to teaching writing uses spectacular digital
games and tablet technology to engage all young learners with a love of
writing. Following the completion of an activity or challenge in a
chosen game, students begin a clearly structured sequence of activities
to develop language, expression and build sequenced ideas in preparation
for writing. Writing outcomes can take the form of handwritten work or
be presented as a digital book. All key genres of writing are covered.

Chris Williams presenting at the 'How to be Outstanding in the New Curriculum" conference for the National Literacy Trust

Working
this way, students develop a passion for writing and sharing their
ideas, standards improve both in print and with the spoken word, and a
lifelong engagement with the importance of written and oral
communication is started.

Classroom Projects: The writing approach has been developed in a large number and range of schools across the UK

This
unrivalled product provides a clear background and explanation of the
approach for educators, it also gives structured and sequenced
directions allowing class teachers and to share the approach with their
students. The step-by-step explanation ensures maximum impact of this
dynamic, innovative and highly effective approach to teaching students
to write for a range of purposes and audiences.

Resources from one of the Writing Modules

A
small number of expertly chosen apps and a tablet computer (iPad or
Android) are required to support this groundbreaking process, which was
first presented at the UK National Literacy Trust conference “How to be
Outstanding” in October 2013.

“I
have a number of reluctant writers and have been looking for ways to
engage them for some time. This product was just what I needed. The
immersion in the games gave the pupils real knowledge for the writing
tasks. They were hooked into writing straight away. It was clear that
there was an increase in motivation, application and enjoyment resulting
in higher standards of writing.”

Petra Rafferty, Senior Teacher, Highlands Primary School, Hull, UK

“I
am so thankful to have come across the work of David and Chris. Their
work inspired me and provided me with opportunity to engage and motivate
the struggling writers in my class. When I reached out to David and
Chris I was pleasantly surprised at the support and guidance they
provided me. Their leadership around the integration of technology,
student engagement and classroom innovation has benefited my practice
and the achievement of my students. I consider their counsel to be
invaluable and I am certain you would as well.”

Rolland Chidiac, Elementary School Teacher, Ontario, Canada

“I
continue to be amazed at the creative and innovative uses of classroom
technology developed by David and Chris. They recognise the power and
impact of technology in the classroom and provided exceptional support,
guidance and feedback. I am always excited to meet educators like David
and Chris and completely support the approach they use to engage
learners and raises standards.”

Dr Reshan Richards, Director of Ed Tech and App Designer (Explain Everything), New Jersey, USA

“I think your ideas have enormous potential and will be extremely useful for teachers and schools.”

Dr
Peter Rudd, Reader in Education, York University. Specialism:
Overcoming the barriers to educational disadvantage (especially using
technology)

“David and Chris are at the forefront of the
growing move towards using mobile technology as a way of engaging pupils
and accelerating learning. The philosophy is based on a clear approach
on how technology can be used to influence learners positively. I would
recommend their input for a range of purposes. They are real teachers
doing the job, not just talking about theory. I have been overwhelmed by
the progress this product has enabled. Standards of writing have
accelerated beyond my expectations.”

Chris Beazeley, Primary Headteacher, Essex, UK

“I
love your ideas! They are endlessly inspiring for me and the children I
work with. I work in schools consulting regarding integrating
technology into the curriculum.This has been such a good way to help our
boys WANT to write!”

Denise Hall, Educational Consultant, Victoria, Australia

I
attended one of David and Chris’ course and immediately recognised the
potential of their work in our school. To say that they have been a like
a shining beacon is an understatement. The whole staff have been
inspired by their work. Pupils with low self-esteem, low concentration
and low academic levels have positively glowed these last few weeks.
Truly magical. I thought it was Hogwarts at times!”

Paul Browning, Primary Headteacher, Hull, UK

“I
have been using your ideas with my class. One of the stand out moments
has been the work of one of the poorest children in my class. He is
working just within level 2 on a day to day basis. The buzz he has found
from writing has been such a rewarding thing to observe, where I am
normally faced with frustration and lack of engagement now he is keen as
mustard to write and it just seems to flow out of him. I know we have
some issues with grammar and few technical elements of writing but I’m a
firm believer that this bit can be taught whereas the want to write
can’t!”

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