My surprise aside, I was not there to judge. My goal was to understand what had happened to the school’s educational technology program. I sat down with the principal and simply asked what kind of use the computer lab had been getting. “None,” he replied. “We just don’t have the time.” He told me about the problems he faced coming into the school as a new principal just that year: a higher-than-average truancy rate, a lack of basic school supplies and textbooks, major disciplinary problems, and a lack of parent participation in the educational process. Then we started to talk about his use of technology as the school principal. All communication with teachers and parents took place through printed memos and newsletters. Face-to-face staff meetings were required regularly for announcements and Q&A. Student grades and teacher performance evaluations were all kept on paper, despite having access to the district’s online portal for such information.

I walked back to the EvalMobile (read: rented minivan) in astonishment. Look at all he has access to that he’s not using, I thought. And if he would use that technology, his teachers would do the same. This man needs to lead!  Was I right? Well, yes and no.

On the one hand, this principal was old school, as evidenced by how much information and communication took place on paper even though he had access to email, a school Web site builder, and online tools for data management. I visited many schools facing similar challenges to those listed above where the principal led by example. And you know what? It caught on. Teachers began building class Web pages, even recruiting students to help, who could then learn HTML and Web design skills. Students’ study materials, performance and attendance data were kept online, and parents had access to that information so that they could more regularly participate in their children’s education.

On the other hand, the problems this principal faced were not just excuses for avoiding the time investment to get up-to-speed with his school’s technology resources. They were real, and they were staring him in the face every day. For example, email and Web-based communication with parents wasn’t going to go very far when a large percentage of those parents (possibly a healthy majority) didn’t have Internet access or a computer at home. Supplemental learning materials provided via the computers wouldn’t do much if there were not enough primary resources, i.e.,  textbooks, to supplement. And it would have been darn difficult to get students excited to use the computer lab if many of those students weren’t even coming in the front door.

In short, there is much that this principal, indeed any school leader, can do to encourage relevant and effective technology use in schools. But there are also social-structural problems that must be addressed (particularly in schools in low-income areas) that stand in the way of change — any change — that could have a positive impact on learning.

The post Educational Technology Leadership Matters. (But it’s not a panacea.) appeared first on FWD.

A theory of action that has guided educational reform movements and state assessment programs since the 1980s says that implementing better kinds of content standards—those that demand deeper learning of content knowledge and skills—require more demanding state assessments.  Further:

Eventually, the theory goes, the increased expectations and persistently rigorous teaching will result in deeper learning of academic content and increased student achievement.

Will the rigor and demands of next generation state assessment programs match the rigor of the Common Core standards? Twenty-three states participate in the PARCC state assessment consortium, the Partnership for Assessment of Readiness for College and Career. (Disclosure: Pearson conducts research and development work for both consortia; I play a small role on behalf of Pearson on the PARCC project.) Twenty-five states participate in the state assessment consortium, SMARTER Balanced. Both consortia are developing new Common Core-aligned assessments that school children in more than 40 states could face in spring 2015.

The National Center for Research on Evaluation, Standards, and Student Testing (CRESST) at UCLA released last month a report that addresses that question, On the Road to Assessing Deeper Learning: The Status of Smarter Balanced and PARCC Assessment Consortia. CRESST researchers Joan Herman and Bob Linn concluded that the PARCC and Smarter Balanced assessments are likely to influence classroom teaching and student learning in positive ways and are cautiously optimistic that the consortium assessments will assess “deeper learning.”  Herman and Linn are among the most renowned and well liked researchers on student assessment policy and its relationship to teaching and learning in the US. (Disclosure: I know both of them and have worked on committees with them.)

This is an exciting contrast to some current state assessments. As in the CRESST report, we can evaluate the rigor of state tests by examining the Depth of Knowledge (DOK) demands of the test items and the standards they assess. Here’s a simple example

DOK level 1: Items that test Recall, as in using a dictionary to find the meaning of a word.

DOK level 2: Items that require Skills and Concepts, as in using context clues to infer the meaning of an unfamiliar word.

DOK level 3: Items that require Strategic Thinking and Reasoning, as in determining an author’s purpose and determining credibility and whether you agree with the author’s point of view.

DOK level 4, Extended Thinking, as in analyzing and synthesizing information from multiple print and online text and video sources.

A RAND study (cited in the CRESST report) indicates that the majority of test items in 17 current state assessments assess DOK level 1. In contrast, the CRESST researchers’ evaluations of the Smarter Balanced test designs indicate that 68% of ELA and 70% of mathematics items will be at DOK levels 3 and 4. They expect similar findings for the PARCC assessments. The theory of action chain suggests that that is exactly what our children need.

When you plug the RAND finding into the theory of action and trace the links to current state assessment programs, you should find less demanding content standards and tests and student achievement that is lower than what we want. And that’s exactly what you’ll find. States are implementing new content standards because these standards are more demanding and require deeper learning than current standards. And as national (e.g.,NAEP) and international assessments (e.g., TIMSS) indicate, U.S. school children’s academic achievement is not where we want it—nor where it needs to be so that they’re well prepared for college, careers, and productive, gratifying lives.

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Title page to Locke’s Some Thoughts Concerning Education (Photo credit: Wikipedia)

I just returned from London where I spoke at the OBHE conference: “Online and open-access learning in higher education: MOOCs, new pedagogies and business models.”  It was actually a fairly lively discussion, debate, and driving conversation about the massive online courses coming out of North America and now beginning to come out of other parts of the world.

What interested me most was what seemed to be about 80% fear and 20% excitement by the gathering of educators (mostly faculty).  It was also interesting that most of those who were currently building MOOCs were doing so under some kind of duress.  From pressure of being left behind to mounting pressure from administrators to even financial pressure to grab a “piece of the MOOC pie,” it seemed rare to find a MOOC builder who was doing so because of the innovation or excitement over the possibilities of helping facilitate learning for so many potential students.

Instead, much of the conversation surrounded problems with the MOOC concept.  Primary to this was concern over the educational efficacy of these experiences.  Most educators seem to understand that ALL eLearning will be judged on the effectiveness of MOOCs….right or wrong, these massive courses are going to be our litmus test.  Obviously this is problematic – imagine evaluating the safety of driving based solely on studying Nascar races.

The next concern was typically voiced with an idiom that I previously assumed to be from American football.  The term “end around” was used consistently as another reason that MOOCs had to be constructed by faculty.  Not new to me was the voiced feeling / assumption by some faculty that administrators would love nothing more than to find a way to provide education without the need to deal with teachers.  I think back to a Chronicle of Higher Education article I read a few years back which described faculty behavior as anything from “eccentric” to “combative” – behavior that would not be tolerated in any other context but higher education.  And so, some faculty believe MOOCs are seen by administrators as a potential way to rid themselves of faculty altogether.

Finally, there was a sense of being “left behind” by many educators and schools.  The assumption was that because X or Y University was launching a MOOC, that our institution should also build one.  Many speakers and delegates noted Teresa Sullivan’s firing last year as a sign that Universities needed to “do or die” in the MOOC game.

But at the heart of the conversation about the efficacy (or at least potential efficacy) of MOOCs were some important questions and concerns.  Many issues that even Coursera and Udacity advocates have noted are still relevant and should not only be discussed, but ultimately figured out.  MOOCs as they exist today are not very engaging.  Unless you are auto-didactic learner (think Abe Lincoln) who can take a piece of content, internalize it, and not only retain it but apply it, MOOCs are likely problematic for you.  That likely leads to another problem – 5-8% retention rates.  Couple that with weak (aka unauthentic) assessments, basing so much feedback on peer evaluation and assessment (even when the peers are unmotivated or so under-educated regarding the content that they have literally nothing which adds value to a conversation), and finding a way to associate credit with MOOCs makes it all that much more challenging.  Then, at least in the states, there are even more accreditation concerns with so much content being provided by outside organizations, which is why most accrediting agencies have a 25% rule.  In MOOCs today there is almost zero student choice, no curriculum integration, no sense of brain-learning interjected into the curricula, a lack of modeling or showcasing creativity and/or critical thinking, and the top-down model promotes a sterile, impersonal experience.  Finally (at least for this list), is the data captivity by MOOC platforms.  While most believe the data will soon be available for a price, most have made it clear that the data is THEIRS and not a school’s.

So what do we do?  I’ve blogged before about possible options and models for the 2nd generation MOOC, but more than that, I just would urge calculated strategy here.  Be thoughtful about the entirety of the process and try not to get caught up in the hoopla.  Build your own (2nd GEN!) MOOC with purpose, solid learning design, and good pedagogical / andragogical models.  Include neuroscience in the conversation as well as what we have learned from Education Psychology.  And don’t forget about 2 decades of learning about how to best deliver eLearning.  In other words, let’s make sure the driver of MOOCs is not solely about money and/or size.  Let’s make it about quality and learning too.  Build a MOOC because you are excited to innovate, not because you are feeling pressured to do something…anything!  Build a MOOC to show the world how to do it right.

Good luck and good teaching.

Dr. Jeff D Borden
VP of Instruction & Academic Strategy
Director of the Center for Online Learning (http://researchnetwork.pearson.com/online-learning)

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At the core of the CBL shift lies the question: are the decisions we’re making right for the student? In thinking beyond just the degree, we can start to view education more holistically and to see that the cycle of learning continues into employment and beyond (at Pearson, we call this “Always Learning”.) It is, of course, in the best economic and competitive interest of local communities, and more broadly, the U.S., to have an educated and informed workforce. When we view education as a holistic endeavor, we can determine the real value that students should be taking away from their college experience. And then we see that, at the macro level, big-picture competencies like critical thinking ability, problem-solving skills, and practical (vs. just theoretical) knowledge are key to ensuring our graduates have the requisite skills to succeed in the workplace and become productive citizens. And to me, CBL is about just that: changing the educational paradigm to put the student’s actual (not just perceived) needs first.

When describing what CBL means, I like to use the analogy of the student who wants to become a phlebotomist. He studies all of his requisite curriculum, learns about units of measure, drawing blood, and how to prepare the arm, etc. He studies, passes his course, and goes to work as a phlebotomist. On his first day on the job, he goes to draw a patient’s blood, and jams the needle into the arm, making his patient scream in pain. Well, if you don’t learn how to apply the knowledge you’ve obtained, what good will it do you? While this analogy is somewhat whimsical, it illustrates the importance of both ‘to know and to do’ which is germane to CBL. I like to say that CBL is about being able to “show you know and proving that you can do it.” Specifically, the way we look at CBL is this:

At the micro level, CBL works to hone in on tiny, fractional, research-tested competencies which, once accumulated, will lead to a holistic and deep understanding of a given subject. Designing courses around these competencies is complex, and involves a detailed analysis of how knowledge should be measured, and what really needs to be assessed. This is where the science of learning design plays a critical role, and this is where we believe the magic happens.

Rather than viewing the educational paradigm shift behind CBL as just another trend, I urge educators to focus on the inherent benefits of what some might say is disruptive innovation and focus more on the value it provides to our students. I believe that within a few years, CBL, and student-centric models of education in general, will be widespread. Those educators and institutions that are early adopters of this innovation can be proud that they play a role in a groundbreaking new movement that seeks to promote the real end-goal of higher education: the long-term success of the student.

The post Changing the Rules: Competency Based Learning in Higher Education appeared first on FWD.

Increasingly educators are realizing that the development of academic language skills among all learners is important for success in school, in college, and in a career. Academic language involves decoding meaning—determining what a text says, a question asks, or a task requires, and encoding meaning—expressing one’s thoughts so they may be shared with others. The skills needed for students and workers in the 21^st century include analytical reading and writing, clear communication, critical thinking, and creativity. These skills are conveyed in the new Common Core State Standards for English language arts and mathematics and in the Next Generation Science Standards as well.

Analytical reading and writing skills build on foundation skills of early literacy, basic reading comprehension, and simple sentence formation. Making inferences about what is read and crafting an argument in writing to express an opinion pull together a number of complex cognitive processes. We must sort through multiple ideas, tap background knowledge, provide details, elaborate, and justify.

Clear communication involves the use of precise words, planning for a specific audience, and the ability to be responsive to the feedback in a conversation. If someone does not understand an utterance, then the speaker must rephrase or provide an example or find another way to make the message clear. If the listener has a follow-up questions, the speaker must think about a response and then provide it.

Critical thinking and creative activities may seem less language-dependent at first glance. In many ways they require us to apply our knowledge. Although they can be accomplished non-verbally, the results must be shared in writing or orally. Students generally need to listen and participate in academic talk in order to discuss their critical reasoning. Open-ended, higher-order questions and tasks by their very nature require more language knowledge to understand the intent of the question and to produce a response that communicates complex reasoning and describes abstract concepts.

Sheltered Instruction Observation Protocol (Echevarría, Vogt & Short, 2013) offers teachers an approach to teach for academic language development at the same time they make the content accessible to English learners and others who benefit from such integrated instruction. The SIOP Model has been tested empirically in several studies over 15 years (Short, Echevarria, & Richards-Tutor, 2011) and by others (Batt, 2010; McIntyre, et al., 2010). Standards such as the Common Core tell teachers what students need to learn and do as a result of instruction while the SIOP Model shows teachers how to plan and deliver effective lessons.

For instance, well-implemented SIOP instruction incorporates all four language skills—reading, writing, listening, and speaking—in lessons across the content areas. Several features of the SIOP Model promote oral interaction; teachers design lessons that build oral discourse skills and use oral language to support and strengthen reading and writing. Teachers include techniques that require student-student communication, such as cooperative group activities, pair work, literature circles, author’s chair, and role-play. They may assign projects that ask students to create something new, such as a cover for a National Geographic magazine and a summary for the accompanying article related to topic being studied in science.

Teachers using this method also purposefully plan scaffolding, building background, and vocabulary activities to make lessons accessible to English learners. Scaffolding provides support for students as they learn to do a task independently. For example, scaffolds may help students understand procedures (e.g., watch the teacher model a task step by step) or the new material being taught (e.g., add notes to a partially completed outline while reading a section of the textbook) or how to describe a diagram (e.g., use sentence starters: The illustration on the left shows…, The arrow indicates…). These scaffolds may be reduced and eventually removed as students gain academic language proficiency.

Teachers will help students develop their background knowledge (e.g., through audio-visual aids and hands-on experiences) in order to facilitate their reading comprehension skills and set them up for adding content information to their knowledge base. SIOP teachers not only pre-teach key vocabulary and give students multiple opportunities to use the new words (e.g., with vocabulary routines, games, and graphics), but they also teach students strategies to help determine word meanings.

Effective teachers are often described as ones who know their subject matter deeply and have the strong content-related pedagogy. In today’s classrooms, we also need teachers who know how to teach and promote the academic language of their subject area so students can develop and use 21^st century skills successfully in school and beyond.

To hear more about the instructions and assessments discussed, join Cristina Hernandez and me today, February 12 for a webinar: Using the SIOP Model to Reach WIDA and College and Career Readiness Standards. Register here now.

References

Batt, E. (2010). Cognitive coaching: A critical phase in professional development to implement sheltered instruction. Teaching and Teacher Education 26, 997-1005.

Echevarría, J., Vogt, M.E. & Short, D. (2013). Making Content Comprehensible for English Learners: The SIOP^® Model, Fourth Edition. Boston: Allyn & Bacon.

McIntyre, E., Kyle, D., Chen, C., Muñoz, M. & Beldon, S. (2010). Teacher learning and ELL reading achievement in sheltered instruction classrooms: Linking professional development to student development, Literacy Research and Instruction, 49(4), 334-351.

Short, D., Echevarría, J., & Richards-Tutor, C. (2011). Research on academic literacy development in sheltered instruction classrooms. Language Teaching Research, 15(3), 363–380

The post 21st Century Academic Skills in School and Beyond appeared first on FWD.

Since not everyone is at the same level coming into a game, this implies we need to make games adaptive. Charles et al (2005) list many ways that adaptivity can help improve game play, including: “to moderate the challenge levels for a player, help players avoid getting stuck, adapt gameplay more to a player’s preference/taste, or perhaps even detect players using or abusing an oversight in the game design to their advantage.” Lopes and Bidarra (2011) suggest that without adaptivity games: are predictable, which encourages “gaming the system,” encourage practice when it is not needed, and have little replay value.

So, how can games adapt to players’ levels? There are some basic things that can be done to get closer to players’ levels without player modeling, such as:

1. Static leveling – This would be the lowest level of adaptivity, in which everyone goes through the same levels and has the same requirements to move to the next level. For example, you must successfully complete four quests to move to level 2. In this case, the game is not trying to create an estimate of player skill. Presumably more skilled players will complete those four quests quicker than less-skilled players and will move on to the next level. Very skilled players will have to move through many levels that are too basic in order to get to the more advanced levels and there isn’t much replayability.

2. Branching based on behavioral measures – Players can go down a limited number of pre-defined paths based on simple measures of performance or choice. In this case, there are a limited number of predefined paths. Players might all play level A, and then depending on whether they get more or fewer than 60% of problems correct, go from A -> B -> D or A -> C ->D, for example.  The key here is that the decision used to choose the path is based on a simple behavioral measure of what they did in the game. Again, this does not attempt to make an estimate of any underlying knowledge, skill, or player attribute; it is just a report of player behavior. This means that it is less likely that simple, unweighted measures are used, and we aren’t getting at why we would want these players to have different paths. What we’re really probably saying here is that players who get more than 60% correct are no longer novices at our skill. However, if we want to make that judgment, we should try to model the skill with more advanced methods that take probability and error into account.

To really get to the point where we are matching situations to skills, we need to create more sophisticated models of the players.

3. Player Modeling – Estimates of characteristics
What we need to do to get to really good matching between players and activities is to infer player characteristics from what happens in the game. For example, we might want to infer traits like creativity, puzzle solving ability, ability to add fractions, and/or conversational English ability from their game play and then adapt from that. We could create a profile of skills for each player where we estimate whether they are at novice, intermediate, advanced, or expert levels.

In order to create such profiles, we need to start with defining the domains we’re interested in. What characteristics will we want to adapt to? We’ll then need to determine how to get evidence of players’ levels of those characteristics. Two ways to start out are: 1. have players rate themselves and/or 2. design the game so the initial levels are very good at gathering this evidence.

How do we make estimates of these characteristics? This is where some statistical lifting is required. This Iseli et al article from 2010, from a group designing Navy simulations, is where things are headed statistically (if you want to look at the nuts and bolts). They use Bayesian Networks, a statistical method that, in this application, estimates the probabilities of different proficiency levels based on evidence from the simulation. So, based on a whole bunch of player actions that get statistically combined, the Bayesian Network might estimate that there is a 92% chance that they are at an expert level of Navy damage control operations (as assessed by Iseli et al). The researchers do a nice job showing how to make these estimates based on what is happening in the simulation, but they do all the calculation after the fact (outside the environment). The next move is to make this happen within the game so the game can adapt based on the results of the computation.

So, if it isn’t obvious… this all requires data! We have to be thinking very carefully about the data we collect from player actions, and how we use it both in game and after the fact.

Once we know what a player’s characteristics are, we can choose the next activity to maximize different things. For example, if we are noting flagging engagement, we might choose what we think will be most motivating. If we want to maximize learning, we might choose the activity most within the player’s zone of proximal development. If we want a better estimate of their characteristics, we might choose an activity that will best help us differentiate between novice and intermediate players.

Finally, at the end of the day, all of these player estimates can also serve as assessment data, providing feedback for teachers and students about their levels of knowledge, skills, and abilities. So, making the game better also serves to give us better information about our students… win, win!

For good reading:
Charles, D., Kerr, A., McNeill, M., McAlister, M., Black, M., Kucklich, J., … Stringer, K. (2005). Player-centred game design: Player modelling and adaptive digital games. In Proceedings of the Digital Games Research Conference (Vol. 285). Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.97.735&rep=rep1&type=pdf
Csikszentmihalyi, M and Csikszentmihalyi I.S, “Optimal experience: psychological studies of flow in consciousness.” 1988, Cambridge; New York: Cambridge University Press.
Iseli, M. R., Koenig, A. D., Lee, J., & Wainess, R. (2010). Automatic assessment of complex task performance in games and simulations. In The Interservice/Industry Training, Simulation & Education Conference (I/ITSEC) (Vol. 2010). Retrieved from https://www.cse.ucla.edu/products/reports/R775.pdf
Lopes, R., & Bidarra, R. (2011). Adaptivity challenges in games and simulations: a survey. Computational Intelligence and AI in Games, IEEE Transactions on, 3(2), 85–99.Available: http://graphics.tudelft.nl/~rafa/myPapers/bidarra.TCIAIG.2011a.pdf
Magerko, B., Heeter, C., Fitzgerald, J., & Medler, B. (2008). Intelligent adaptation of digital game-based learning. In Proceedings of the 2008 Conference on Future Play: Research, Play, Share (pp. 200–203). Available: http://lmc.gatech.edu/~bmedler3/papers/MagerkoHeeterMedlerFitzgerald-Intelligent AdaptationofDigitalGame-BasedLearning.pdf

The post Finding the Right Level: Adaptivity in Learning Games appeared first on FWD.

Conley’s also right that, in order to be successful in college and in their careers, students need more diverse skills than the traditional cognitive skills. A survey of over 400 employers found that more than 90 percent rated oral and written communication, teamwork and collaboration, professionalism and work ethic, and critical thinking and problem solving as very important. These skills were rated higher in importance than were proficiency in English language arts, mathematics, or science. In a similar survey conducted by McKinsey & Co., employers reported that many applicants for jobs had unsuitable work habits, insufficient problem solving skills, and were poor communicators. These shortcomings were cited more often than poor reading or math skills. In addition, areas such as resilience, conscientiousness, ambition, and engagement have all been shown to be related to success.

Conley argues that we should refer to noncognitive skills as metacognitive learning skills, to better describe the “full range of behaviors, attitudes, and beliefs students demonstrate while engaging in the learning process.” I’m on board with this change in terminology. Beyond that, we need to also help teachers, schools, and policy makers understand the importance of these skills and how to infuse them into classrooms. These metacognitive learning skills should be taught alongside traditional content and then we need to measure and track student progress in acquiring them.

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Magical. That was the word that came to mind after a visit last week to Huntsville, Alabama to check in on the amazing transformation from print to digital learning that has occurred there just since the start of school. Today is Digital Learning Day, which highlights success stories in digital learning. Huntsville is one of them. Educators from all over the country have been flocking there since last fall, when word of the district’s 1:1 Learning Initiative began to get out. “We’re the largest school district in the world that is all digital,” Superintendent Casey Wardynski shared with reporters. “The whole world is looking at Huntsville right now.”

And well they should. A year after he was hired the Huntsville school board approved Wardynski’s plan to purchase digital curricula instead of textbooks and laptops, netbooks, and iPads for students and teachers. Computer networks were set up in schools and WiFi installed on school buses and in public areas of the city. Today, after extensive training, teachers can engage their students using interactive texts, videos, animations, and other tools. Digital assessments help identify strengths and weaknesses and personalize lessons for each student. Students can learn anytime, anywhere.

As we toured two campuses and then listened to students, teachers, parents and the president of the school board, I was struck by several themes. First, going from all-print to all-digital so quickly and successfully requires strong leadership. That is exemplified by Wardynski, his leadership team and HCS school principals. Wardynski’s unique background prepared him well for system-wide change. He has a Ph.D. in policy analysis, taught economics at West Point, earned the rank of colonel, and headed up the Army’s office of Economic and Manpower Analysis. He also led the creation of America’s Army, a digital game with 12 million users that revolutionized Army recruitment and human capital management.

When he came to Huntsville, Wardynski wanted students to get more instructional time and one-on-one attention. In going digital, the goal wasn’t to give students and teachers devices. The goal was to improve learning. This is one lesson I’m sure the visiting educators took home with them. Don’t spend millions on devices unless you have a plan for using them to aid teaching and learning.

Another lesson was the need to work with a specialized partner. The district chose Pearson as its 1:1 Learning partner to provide support for pretty much every aspect of its conversion, with the shared objectives of accelerating student achievement and shifting instructional practice. As the person leading Pearson’s efforts to help districts enter the digital age, I frequently have the opportunity to help with this important work.

Perhaps my favorite part of the visit was listening to an AP English teacher named Stephanie Hyatt talk about how the Pearson digital curriculum resources and other tools had “absolutely changed” her teaching and made it more powerful. As an example, she told the story of how she capitalized on President Barack Obama’s inauguration in January. She had students work in groups to identify examples of imagery, diction and other rhetorical elements of the inaugural speech President Kennedy gave in 1960, posting the assignment on the collaboration system called Edmodo. On another day, she had the groups compare that speech with President Abraham Lincoln’s first inaugural address. Then, after President Obama’s speech, she asked them to compare it with the other two. But, this time, she threw her students something of a curveball. She knew from monitoring their work online that some students weren’t contributing much to their groups. So, she put those who were slacking off together. They could no longer rely on more productive students to carry the burden.

The Huntsville newspaper quoted her saying that “the kids (who hadn’t done the work) had a deep hole to dig themselves out of.” They also learned that Hyatt was paying attention to their work—just as educators across the country are paying attention to what she and her colleagues are doing for kids in Huntsville.

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I recently visited Freedom Park School, a Pearson National Model School in Augusta, GA, to gather some information on this topic.  I have had the great privilege of working with the Freedom Park staff for the last five years and know that they are always ahead of the curve in addressing new challenges in education.  I was eager to see and hear specifically how this staff is addressing this issue of college and career readiness in the elementary grades.

What I, and my visiting colleagues, saw and heard was most helpful in providing concrete examples that address this issue.  We visited classrooms from kindergarten to 8^th grade and saw the progression of strategies teachers are using to address the college and career readiness. How does a kindergarten or first grade teacher set the foundations for independent learning?  Teachers told us they have learned that the most important factor for building student independence is setting routine and ritual in the classroom.  In the initial partnership between Freedom Park and Pearson, each teacher worked to establish strong routines and rituals in their classrooms.  As the years have progressed, teachers have continued to use and enhance those lessons, making the strategies part of their habitual practice.  Teachers at Freedom Park take the time to establish strong routines (i.e., what we always do in this class; how learning time is used) and rituals (i.e., how we do things on this class).  By taking significant time to focus on the procedures of the classroom at the beginning of the school year, Freedom Park teachers tell us they are able to create classrooms in which learning can go deeper and where students can take ownership of this learning.

In addition, Freedom Park’s teachers shared that their professional learning communities, known as Learning Teams, is strengthening their collegial, collaborative powers to address many topics.  Beginning this year,  teachers meet regularly to reflect on their teaching practices, analyze student responses and reactions to instruction, and adjust their instruction based on their findings.  Teachers indicated that this well-structured process enhances their practices and is a significant contributor to their continued professional growth.

Freedom Park has truly embodied the college and career readiness competencies set forth in the Common Core State Standards:

• Demonstrate independence;
• Build strong content knowledge;
• Respond to varying demands of audience, task, purpose and discipline;
• Comprehend as well as critique;
• Value evidence;
• Use technology strategically and capably;
• Understand other perspectives and cultures.

I hope you’ll join me and Susan Powell on a webinar Wednesday to hear firsthand how Freedom Park can serve as an example for other elementary schools during this transition to the Common Core.

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For the last 10 years or so, there has been a tremendous push in educational research circles to measure efficacy. Efficacy can be defined, roughly, as the extent to which a product or service is having a desired effect. We write a new math curriculum — is it having the effect we expect? We put computers in classrooms — do they make a difference in learning? We’ve placed heavy emphasis on research methods such as randomized control trials and statistics such as effect sizes to determine if what we’re studying has an effect.

However, there is another concept, one that researchers have batted around for years, that is slowly creeping its way back into educational literature: fidelity. Also roughly defined, fidelity is the extent to which a product or service is being implemented as intended. This is a key distinction from efficacy. We can create spectacular mathematics curricula that might be extremely effective…but only if put into action in the ways the developers had in mind.

Let’s take a look at a couple of ways that fidelity plays a role in program implementation.

• Professional Development: I spent a number of years in the aughts driving from school to school evaluating the effectiveness of the Enhancing Education Through Technology grant program. A major goal, in addition to technology literacy, was to improve reading and math achievement scores in grantees’ schools. When measuring changes in such scores over the life of each school’s grant, I found wide variation from one school to another. One of the most important determining factors was teacher professional development (PD). Grantees were required to provide PD, but the amount and quality of PD varied greatly. Statistically controlling for a number of other factors, schools that lacked quality PD showed the fewest gains in achievement. The long and the short of it was that there was no implementation fidelity in those programs. No one had a solid plan to implement the hardware and software in classrooms in a way that would be effective.
• Unintended Consequences: Some folks might be tempted to say that at least the schools above each got some new computers, and isn’t that better than not having them at all? Not necessarily. Research in public health and violence prevention programs over the last twenty years has demonstrated that practitioners often make changes to programs that the developers did not intend, and that these changes can not only cause the program to have no effect, but can also possibly cause it to have negative effects. (See, e.g., Mihalic, 2004, Silvia & Thorne, 1997, Donaldson et al., 1994.) Researchers have found that some practitioners may implement some components of a program, but not all. They may ignore one aspect of a program while replacing it with things the practitioners bring themselves, perhaps because they think that what they bring is more relevant, effective, or important. In educational circles, this can lead to confusion, frustration, and missed learning opportunities. This is not to say that we should stifle instructors’ creativity and knowledge of their students — far from it. But if we don’t know the changes that practitioners are making, we can’t account for those changes when measuring the program’s efficacy. (Nor can we provide just-in-time information to help right a sinking ship.) The program itself might look to be a failure when in fact it was simply implemented poorly. The aforementioned PD can help with this, especially by demonstrating to practitioners not only how a program works but also why it works the way it does — and, therefore, why it’s important to maintain all facets of the program.

In short, it’s important for researchers to remember that while we are measuring efficacy, we must also measure fidelity. Ideally, that information can be used to give educational programs (and, ultimately, students) the best chances for success. So, grab your watering can, and don’t forget the roots. After all, what good is a dead plant?

References

Donaldson, S.I., Graham, J.W., & Hansen, W.B. (1994). Testing the generalizability of intervening mechanism theories: Understanding the effects of adolescent drug use prevention interventions. Journal of Behavioral Medicine, 17, 195–216.

Mihalic, S. (2004). The importance of implementation fidelity. Emotional & Behavioral Disorders in Youth, 4, 83 – 105.

Silvia, E.S., & Thorne, J. (1997). School-Based Drug Prevention Programs: A Longitudinal Study in Selected School Districts. Research Triangle, NC: Research Triangle Institute.

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