This morning, I received a newsletter from Sir Ken Robinson, a popular motivational speaker who focuses on education. There was a return email address, so I wrote to him. Here's what I wrote:
Dear Sir Ken,
"I love your message. I'm one of the worker bees who's trying to leverage the kind of changes you envision.
After 20+ years of hard work, my colleagues and I have reinvented educational assessment. No multiple choice. No high stakes. Our focus is on assessment for learning—supporting students in learning joyfully and deeply in a way that facilitates skills for learning, thinking, inquiring, relating and otherwise navigating a complex world. Our assessments are scalable and standardized, but they do not homogenize. They are grounded in a deep study of the many pathways through which students learn key skills and concepts. We're documenting, in exquisite (some would say insane) detail, how concepts and skills develop over time so we can gain insight into learners' knowledge networks. We don't ask about correctness. We ask about understanding and competence and how they develop over time. And we help teachers meet students "where they're at."
We've accumulated a strong base of evidence to support these claims. But now that we're ready to scale, we're running up against hostility toward all standardized assessment. It's difficult to get to the point where we can even have a conversation with our pedagogical allies. Ouch!
Lectica is organized as a nonprofit so we can guarantee that the underprivileged are served first. We plan to offer subscriptions to our assessments (learning tools) without charge to individual teachers everywhere.
We've kept our heads down as we've developed our methods and technology. Now we're scaling and want to be seen. We know we're part of the solution to today's educational crisis—perhaps a very big part of the solution. I'm hoping you'd like to learn more."
My email was returned with this message: "The email account that you tried to reach does not exist." How frustrating.
So, I thought I'd pen this post and ask my friends and colleagues to help me get access to Sir Ken's ear. If you know him, please forward this message. I'm certain he'll be interested in what we're doing for learning and development. Where are you Sir Ken Robinson? Can you hear me? Are you out there?
During the 70s and 80s I practiced midwifery. It was a great honor to be present at the births of over 500 babies, and in many cases, follow them into childhood. Every single one of those babies was a joyful, driven, and effective "every moment" learner. Regardless of difficulty and pain they all learned to walk, talk, interact with others, and manipulate many aspects of their environment. They needed few external rewards to build these skills—the excitement and suspense of striving seemed to be reward enough. I felt like I was observing the "life force" in action.
Unfortunately as many of these children approached the third grade (age 8), I noticed something else—something deeply troubling. Many of the same children seemed to have lost much of this intrinsic drive to learn. For them, learning had become a chore motivated primarily by extrinsic rewards and punishments. Because this was happening primarily to children attending conventional schools (Children receiving alternative instruction seemed to be exempt.) it appeared that something about schooling was depriving many children of the fundamental human drive required to support a lifetime of learning and development—a drive that looked to me like a key source of happiness and fulfillment.
Understanding the problem
Following upon my midwifery career, I flirted briefly with a career in advertising, but by the early 90's I was back in school—in a Ph.D. program in U. C. Berkeley's Graduate School of Education—where I found myself observing the same pattern I'd observed as a midwife. Both the research and my own lab experience exposed the early loss of students' natural love of learning. My concern was only increased by the newly emerging trend toward high stakes multiple choice testing, which my colleagues and I saw as a further threat to children's natural drive to learn.
Most of the people I've spoken to about this problem have agreed that it's a shame, but few have seen it as a problem that can be solved, and many have seen it as an inevitable consequence of either mass schooling or simple maturation. But I knew it was not inevitable. Children and those educated in a range of alternative environments did not appear to lose their drive to learn. Additionally, above average students in conventional schools appeared to be more likely to retain their love of learning.
I set out to find out why—and ended up on a long journey toward a solution.
How learning works
First, I needed to understand how learning works. At Berkeley, I studied a wide variety of learning theories in several disciplines, including developmental theories, behavioral theories, and brain-based theories. I collected a large database of longitudinal interviews and submitted them to in-depth analysis, looked closely at the relation between testing and learning, and studied psychological measurement, all in the interest of finding a way to support childrens' growth while reinforcing their love of learning.
My dissertation—which won awards from both U.C. Berkeley and the American Psychological Association—focused on the development of people's conceptions of learning from age 5 through 85, and how this kind of knowledge could be used to measure and support learning. In 1998, I received $500,000 from the Spencer Foundation to further develop the methods designed for this research. Some of my areas of expertise are human learning and development, psychometrics, metacognition, moral education, and research methods.
In the simplest possible terms, what I learned in 5 years of graduate school is that the human brain is designed to drive learning, and that preserving that natural drive requires 5 ingredients:
a safe environment that is rich in learning opportunities and healthy human interaction,
a teacher who understands each child's interests and level of tolerance for failure,
a mechanism for determining "what comes next"—what is just challenging enough to allow for success most of the time (but not all of the time),
instant actionable feedback, and
the opportunity to integrate new knowledge or skills into each learner's existing knowledge network well enough to make it useable before pushing instruction to the next level. (We call this building a "robust knowledge network"—the essential foundation for future learning.)*
Identifying the solution
Once we understood what learning should look like, we needed to decide where to intervene. The answer, when it came, was a complete surprise. Understanding what comes next—something that can only be learned by measuring what a student understands now—was an integral part of the recipe for learning. This meant that testing—which we originally saw as an obstacle to robust learning—was actually the solution—but only if we could build tests that would free students to learn the way their brains are designed to learn. These tests would have to help teachers determine "what comes next" (ingredient 3) and provide instant actionable feedback (ingredient 4), while rewarding them for helping students build robust knowledge networks (ingredient 5).
Unfortunately, conventional standardized tests were focused on "correctness" rather than robust learning, and none of them were based on the study of how targeted concepts and skills develop over time. Moreover, they were designed not to support learning, but rather to make decisions about advancement or placement, based on how many correct answers students were able to provide relative to other students. Because this form of testing did not meet the requirements of our learning recipe, we'd have to start from scratch.
Developing the solution
We knew that our solution—reinventing educational testing to serve robust learning—would require many years of research. In fact, we would be committing to possible decades of effort without a guaranteed result. It was the vision of a future educational system in which all children retained their inborn drive for learning that ultimately compelled us to move forward.
To reinvent educational testing, we needed to:
make a deep study of precisely how children build particular knowledge and skills over time in a wide range of subject areas (so these tests could accurately identify "what comes next");
make tests that determine how deeply students understand what they have learned—how well they can use it to address real-world issues or problems (requires that students show how they are thinking, not just what they know—which means written responses with explanations); and
produce formative feedback and resources designed to foster "robust learning" (build robust knowledge networks).
Here's what we had to invent:
A learning ruler (building on Commons  and Fischer );
A method for studying how students learn tested concepts and skills (refining the methods developed for my dissertation);
A human scoring system for determining the level of understanding exhibited in students' written explanations (building upon Commons' and Fischer's methods, refining them until measurements were precise enough for use in educational contexts); and
An electronic scoring system, so feedback and resources could be delivered in real time.
It took over 20 years (1996–2016), but we did it! And while we were doing it, we conducted research. In fact, our assessments have been used in dozens of research projects, including a 25 million dollar study of literacy conducted at Harvard, and numerous Ph.D. dissertations—with more on the way.
What we've learned
We've learned many things from this research. Here are some that took us by surprise:
Students in schools that focus on building deep understanding graduate seniors that are up to 5 years ahead (on our learning ruler) of students in schools that focus on correctness (2.5 to 3 years after taking socioeconomic status into account).
Students in schools that foster robust learning develop faster and continue to develop longer (into adulthood) than students in schools that focus on correctness.
On average, students in schools that foster robust learning produce more coherent and persuasive arguments than students in schools that focus on correctness.
On average, students in our inner-city schools, which are the schools most focused on correctness, stop developing (on our learning ruler) in grade 10.
The average student who graduates from a school that strongly focuses on correctness is likely, in adulthood, to (1) be unable to grasp the complexity and ambiguity of many common situations and problems, (2) lack the mental agility to adapt to changes in society and the workplace, and (3) dislike learning.
From our perspective, these results point to an educational crisis that can best be addressed by allowing students to learn as their brains were designed to learn. Practically speaking, this means providing learners, parents, teachers, and schools with metrics that reward and support teaching that fosters robust learning.
Where we are today
Lectica has created the only metrics that meet all of these requirements. Our mission is to foster greater individual happiness and fulfillment while preparing students to meet 21st century challenges. We do this by creating and delivering learning tools that encourage students to learn the way their brains were designed to learn. And we ensure that students who need our learning tools the most get them first by providing free subscriptions to individual teachers everywhere.
To realize our mission, we organized as a nonprofit. We knew this choice would slow our progress (relative to organizing as a for-profit and welcoming investors), but it was the only way to guarantee that our true mission would not be derailed by other interests.
Thus far, we've funded ourselves with work in the for-profit sector and income from grants. Our background research is rich, our methods are well-established, and our technology works even better than we thought it would. Last fall, we completed a demonstration of our electronic scoring system, CLAS, a novel technology that learns from every single assessment taken in our system.
The groundwork has been laid, and we're ready to scale. All we need is the platform that will deliver the assessments (called DiscoTests), several of which are already in production.
After 20 years of high stakes testing, students and teachers need our solution more than ever. We feel compelled to scale a quickly as possible, so we can begin the process of reinvigorating today's students' natural love of learning, and ensure that the next generation of students never loses theirs. Lectica's story isn't finished. Instead, we find ourselves on the cusp of a new beginning!
A final note: There are many benefits associated with our approach to assessment that were not mentioned here. For example, because the assessment scores are all calibrated to the same learning ruler, students, teachers, and parents can easily track student growth. Even better, our assessments are designed to be taken frequently and to be embedded in low-stakes contexts. For grading purposes, teachers are encouraged to focus on growth over time rather than specific test scores. This way of using assessments pretty much eliminates concerns about cheating. And finally, the electronic scoring system we developed is backed by the world's first "taxonomy of learning," which also serves many other educational and research functions. It's already spawned a developmentally sensitive spell-checker! One day, this taxonomy of learning will be robust enough to empower teachers to create their own formative assessments on the fly.
The world's best recruitment assessments—unlimited, auto-scored, affordable, relevant, and easy
Lectical Assessments have been used to support senior and executive recruitment for over 10 years, but the expense of human scoring has prohibited their use at scale. I'm DELIGHTED to report that this is no longer the case. Because of CLAS—our electronic developmental scoring system—this fall we plan to deliver customized assessments of workplace reasoning with real time scoring. We're calling this service LecticaFirst.
LecticaFirst is a subscription service.* It allows you to administer as many LecticaFirst assessments as you'd like, any time you'd like. It's priced to make it possible for your organization to pre-screen every candidate (up through mid-level management) before you look at a single resume or call a single reference. And we've built in several upgrade options, so you can easily obtain additional information about the candidates that capture your interest.
"Use of hiring methods with increased predictive validity leads to substantial increases in employee performance as measured in percentage increases in output, increased monetary value of output, and increased learning of job-related skills" (Hunter, Schmidt, & Judiesch, 1990).
Most conventional workplace assessments focus on one of two broad constructs—aptitude or personality. These assessments examine factors like literacy, numeracy, role-specific competencies, leadership traits, and cultural fit, and are generally delivered through interviews, multiple choice tests, or likert-style surveys. Emotional intelligence is also sometimes measured, but thus far, is not producing results that can complete with aptitude tests (Zeidner, Matthews, & Roberts, 2004).
Like Lectical Assessments, aptitude tests are tests of mental ability (or mental skill). High-quality tests of mental ability have the highest predictive validity for recruitment purposes, hands down. Hunter and Hunter (1984), in their systematic review of the literature, found an effective range of predictive validity for aptitude tests of .45 to .54. Translated, this means that about 20% to 29% of success on the job was predicted by mental ability. These numbers do not appear to have changed appreciably since Hunter and Hunter's 1984 review.
Personality tests come in a distant second. In their meta-analysis of the literature, Teft, Jackson, and Rothstein (1991) reported an overall relation between personality and job performance of .24 (with conscientiousness as the best predictor by a wide margin). Translated, this means that only about 6% of job performance is predicted by personality traits. These numbers do not appear to have been challenged in more recent research (Johnson, 2001).
Predictive validity of various types of assessments used in recruitment
The following table shows average predictive validities for various forms of assessment used in recruitment contexts. The column "variance explained" is an indicator of how much of a role a particular form of assessment plays in predicting performance—it's predictive power. When deciding which assessments to use in recruitment, the goal is to achieve the greatest possible predictive power with the fewest assessments. That's why I've included the last column, "variance explained with GMA." It shows what happens to the variance explained when an assessment of General Mental Ability is combined with the form of assessment in a given row. The best combinations shown here are GMA and work sample tests, GMA and Integrity, and GMA and conscientiousness.
Form of assessment
Variance explained (with GMA)
Complexity of workplace reasoning
(Dawson & Stein, 2004; Stein, Dawson, Van Rossum, Hill, & Rothaizer, 2003)
Aptitude (General Mental Ability, GMA)
(Hunter, 1980; Schmidt & Hunter, 1998)
Work sample tests
(Hunter & Hunter, 1984; Schmidt & Hunter, 1998)
(Ones, Viswesvaran, and Schmidt, 1993; Schmidt & Hunter, 1998)
(Barrick & Mount, 1995; Schmidt & Hunter, 1998).
Employment interviews (structured)
(McDaniel, Whetzel, Schmidt, and Mauer, 1994; Schmidt & Hunter, 1998)
Employment interviews (unstructured)
(McDaniel, Whetzel, Schmidt, and Mauer, 1994 Schmidt & Hunter, 1998)
Job knowledge tests
(Hunter and Hunter, 1984; Schmidt & Hunter, 1998)
Job tryout procedure
(Hunter and Hunter, 1984; Schmidt & Hunter, 1998)
(Hunter and Hunter, 1984; Schmidt & Hunter, 1998)
Training & experience: behavioral consistency method
(McDaniel, Schmidt, and Hunter, 1988a, 1988b; Schmidt & Hunter, 1998; Schmidt, Ones, and Hunter, 1992)
(McDaniel, Schmidt, and Hunter, 1988a; Schmidt & Hunter, 1998)
Years of education
(Hunter and Hunter, 1984; Schmidt & Hunter, 1998)
(Schmidt & Hunter, 1998)
The figure below shows the predictive power information from this table in graphical form. Assessments are color coded to indicate which are focused on mental (cognitive) skills, behavior (past or present), or personality traits. It is clear that tests of mental skills stand out as the best predictors.
Why use Lectical Assessments for recruitment?
Lectical Assessments are "next generation" assessments, made possible through a novel synthesis of developmental theory, primary research, and technology. Until now multiple choice style aptitude tests have been the most affordable option for employers. But despite being more predictive than personality tests, aptitude tests still suffer from important limitations. Lectical Assessments address these limitations. For details, take a look at the side-by-side comparison of LecticaFirst tests with conventional tests, below.
Lectica employs sophisticated developmental tools and technologies to efficiently determine the relation between role requirements and the level of reasoning skill required to meet those requirements.
Lectica's approach is not directly comparable to other available approaches.
Fit to role: relevance
Lectical Assessments are readily customized to fit particular jobs, and are direct measures of what's most important—whether or not candidates' actual workplace reasoning skills are a good fit for a particular job.
Aptitude tests measure people's ability to select correct answers to abstract problems. It is hoped that these answers will predict how good a candidate's workplace reasoning skills are likely to be.
In research so far: Predict advancement (R = .53**, R2 = .28), National Leadership Study.
The aptitude (IQ) tests used in published research predict performance (R = .45 to .54, R2 = .20 to .29)
The written response format makes cheating virtually impossible when assessments are taken under observation, and very difficult when taken without observation.
Cheating is relatively easy and rates can be quite high.
High. LecticaFirst assessments can be upgraded after hiring, then used to inform employee development plans.
None. Aptitude is a fixed attribute, so there is no room for growth.
Our assessments are developed with a 21st century learning technology that allows us to continuously improve the predictive validity of Lecticafirst assessments.
Conventional aptitude tests are built with a 20th century technology that does not easily lend itself to continuous improvement.
* CLAS is not yet fully calibrated for scores above 11.5 on our scale. Scores at this level are more often seen in upper- and senior-level managers and executives. For this reason, we do not recommend using lecticafirst for recruitment above mid-level management.
**The US Department of Labor’s highest category of validity, labeled “Very Beneficial” requires regression coefficients .35 or higher (R > .34).
Arthur, W., Day, E. A., McNelly, T. A., & Edens, P. S. (2003). A meta‐analysis of the criterion‐related validity of assessment center dimensions. Personnel Psychology, 56(1), 125-153.
Becker, N., Höft, S., Holzenkamp, M., & Spinath, F. M. (2011). The Predictive Validity of Assessment Centers in German-Speaking Regions. Journal of Personnel Psychology, 10(2), 61-69.
Beehr, T. A., Ivanitskaya, L., Hansen, C. P., Erofeev, D., & Gudanowski, D. M. (2001). Evaluation of 360 degree feedback ratings: relationships with each other and with performance and selection predictors. Journal of Organizational Behavior, 22(7), 775-788.
Dawson, T. L., & Stein, Z. (2004). National Leadership Study results. Prepared for the U.S. Intelligence Community.
Gaugler, B. B., Rosenthal, D. B., Thornton, G. C., & Bentson, C. (1987). Meta-analysis of assessment center validity. Journal of Applied Psychology, 72(3), 493-511.
Hunter, J. E., & Hunter, R. F. (1984). The validity and utility of alternative predictors of job performance. Psychological Bulletin, 96, 72-98.
Hunter, J. E., Schmidt, F. L., & Judiesch, M. K. (1990). Individual differences in output variability as a function of job complexity. Journal of Applied Psychology, 75, 28-42.
Johnson, J. (2001). Toward a better understanding of the relationship between personality and individual job performance. In M. R. R. Barrick, Murray R. (Ed.), Personality and work: Reconsidering the role of personality in organizations (pp. 83-120).
Mcdaniel, M. A., Schmidt, F. L., & Hunter, J., E. (1988a). A Meta-analysis of the validity of training and experience ratings in personnel selection. Personnel Psychology, 41(2), 283-309.
Mcdaniel, M. A., Schmidt, F. L., & Hunter, J., E. (1988b). Job experience correlates of job performance. Journal of Applied Psychology, 73, 327-330.
McDaniel, M. A., Whetzel, D. L., Schmidt, F. L., & Maurer, S. D. (1994). Validity of employment interviews. Journal of Applied Psychology, 79, 599-616.
Rothstein, H. R., Schmidt, F. L., Erwin, F. W., Owens, W. A., & Sparks, C. P. (1990). Biographical data in employment selection: Can validities be made generalizable? Journal of Applied Psychology, 75, 175-184.
Stein, Z., Dawson, T., Van Rossum, Z., Hill, S., & Rothaizer, S. (2013, July). Virtuous cycles of learning: using formative, embedded, and diagnostic developmental assessments in a large-scale leadership program. Proceedings from ITC, Berkeley, CA.
Tett, R. P., Jackson, D. N., & Rothstein, M. (1991). Personality measures as predictors of job performance: A meta-analytic review. Personnel Psychology, 44, 703-742.
Zeidner, M., Matthews, G., & Roberts, R. D. (2004). Emotional intelligence in the workplace: A critical review. Applied psychology: An International Review, 53(3), 371-399.
We've been hearing quite a bit about the "proficiency vs. growth" debate since Betsy DeVos (Trump's candidate for Education Secretary) was asked to weigh in last week. This debate involves a disagreement about how high stakes tests should be used to evaluate educational programs. Advocates for proficiency want to reward schools when their students score higher on state tests. Advocates for growth want to reward schools when their students grow more on state tests. Readers who know about Lectica's work can guess where we'd land in this debate—we're outspokenly growth-minded.
For us, however, the proficiency vs. growth debate is only a tiny piece of a broader issue about what counts as learning. Here's a sketch of the situation as we see it:
Getting a higher score on a state test means that you can get more correct answers on increasingly difficult questions, or that you can more accurately apply writing conventions or decode texts. But these aren't the things we really want to measure. They're "proxies"—approximations of our real learning objectives. Test developers measure proxies because they don't know how to measure what we really want to know.
What we really want to know is how well we're preparing students with the skills and knowledge they'll need to successfully navigate life and work.
Scores on conventional tests predict how well students are likely to perform, in the future, on conventional tests. But scores on these tests have not been shown to be good predictors of success in life.*
In light of this glaring problem with conventional tests, the debate between proficiency and growth is a bit of a red herring. What we really need to be asking ourselves is a far more fundamental question:
What knowledge and skills will our children need to navigate the world of tomorrow, and how can we best nurture their development?
That's the question that frames our work here at Lectica.
*For information about the many problems with conventional tests, see FairTest.
Ten years ago, Kirschner, Sweller, & Clark published an article entitled, Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching.
In this article, Kirschner and his colleagues contrast outcomes for what they call "guidance instruction" (lecture and demonstration) with those from constructivism-based instruction. They conclude that constructivist approaches produce inferior outcomes.
The article suffers from at least three serious flaws.
First, the authors, in making their distinction between guided instruction and constructivist approaches, have created a caricature of constructivist approaches. Very few experienced practitioners of constructivist, discovery, problem-based, experiential, or inquiry-based teaching would characterize their approach as minimally guided. "Differently guided" would be a more appropriate term. Moreover, most educators who use constructivist approaches include lecture and demonstration where these are appropriate.
Second, the research reviewed by the authors was fundamentally flawed. For the most part, the metrics employed to evaluate different styles of instruction were not reasonable measures of the kind of learning constructivist instruction aims to support—deep understanding (the ability to apply knowledge effectively in real-world contexts). They were measures of memory or attitude. Back in 2010, Stein, Fisher, and I argued that metrics can't produce valid results if they don't actually measure what we care about (Redesigning testing: Operationalizing the new science of learning.) Why isn't this a no-brainer?
And finally, the longitudinal studies Kirschner and his colleagues reviewed had short time-spans. None of them examined the long-term impacts of different forms of instruction on deep understanding or long-term development. This is a big problem for learning research—one that is often acknowledged, but rarely addressed.
Since Kirschner's article was published in 2006, we've had an opportunity to examine the difference between schools that provide different kids of instruction, using assessments that measure the depth and coherence of students' understanding. We've documented a 3 to 5 year advantage, by grade 12, for students who attend schools that emphasize constructivist methods vs. those that use more "guidance instruction".
In this post, I'll be describing and comparing three basic forms of assessment—surveys, tests of factual and procedural knowledge, and performative tests.
Surveys—measures of perception, preference, or opinion
What is a survey? A survey (a.k.a. inventory) is any assessment that asks the test-taker to choose from a set of options, such as "strongly agree" or "strongly disagree", based on opinion, preference, or perception. Surveys can be used by organizations in several ways. For example, opinion surveys can help maintain employee satisfaction by providing a "safe" way to express dissatisfaction before workplace problems have a chance to escalate.
Surveys have been used by organizations in a variety of ways. Just about everyone who's worked for a large organization has completed a personality inventory as part of a team-building exercise. The results stimulate lots of water cooler discussions about which "type" or "color" employees are, but their impact on employee performance is unclear. (Fair warning: I'm notorious for my discomfort with typologies!) Some personality inventories are even used in high stakes hiring and promotion decisions, a practice that continues despite evidence that they are very poor predictors of employee success .
Although most survey developers don't pretend their assessments measure competence, many do. The item on the left was used in a survey with the words "management skills" in it's title.
Claims that surveys measure competence are most common when "malleable traits"—traits that are subject to change, learning or growth—are targeted. One example of a malleable trait is "EQ" or "emotional intelligence". EQ is viewed as a skill that can be developed, and there are several surveys that purport to measure its development. What they actually measure is attitude.
Another example of surveys masquerading as assessments of skill is in the measurement of "transformational learning". Transformational learning is defined as a learning experience that fundamentally changes the way a person understands something, yet the only way it appears to be measured is with surveys. Transformational learning surveys measure people's perceptions of their learning experience, not how much they are actually changed by it.
The only survey-type assessments that can be said to measure something like skill are assessments—such as 360s—that ask people about their perceptions. Although 360s inadvertently measure other things, like how much a person is liked or whether or not a respondent agrees with that person, they may also document evidence of behavior change. If what you are interested in is behavior change, a 360 may be appropriate in some cases, but it's important to keep in mind that while a 360 may measure change in a target's behavior, it's also likely to measure change in a respondent's attitude that's unrelated to the target's behavior.
360-type assessments may, to some extent, serve as tests of competence, because behavior change may be an indication that someone has learned new skills. When an assessment measures something that might be an indicator of something else, it is said to measure a proxy. A good 360 may measure a proxy (perceptions of behavior) for a skill (competence).
There are literally hundreds of research articles that document the limitations of surveys, but I'll mention only one more of them here: All of the survey types I've discussed are vulnerable to "gaming"—smart people can easily figure out what the most desirable answers are.
Surveys are extremely popular today because, relative to assessments of skill, they are inexpensive to develop and cost almost nothing to administer. Lectica gives away several high quality surveys for free because they are so inexpensive, yet organizations spend millions of dollars every year on surveys, many of which are falsely marketed as assessments of skill or competence.
Tests of factual and procedural knowledge
A test of competence is any test that asks the test taker to demonstrate a skill. Tests of factual and procedural knowledge can legitimately be thought of as tests of competence.
The classic multiple choice test examines factual knowledge, procedural knowledge, and basic comprehension. If you want to know if someone knows the rules, which formulas to apply, the steps in a process, or the vocabulary of a field, a multiple choice test may meet your needs. Often, the developers of multiple choice tests claim that their assessments measure understanding, reasoning, or critical thinking. This is because some multiple choice tests measure skills that are assumed to be proxies for skills like understanding, reasoning, and critical thinking. They are not direct tests of these skills.
Multiple choice tests are widely used, because there is a large industry devoted to making them, but they are increasingly unpopular because of their (mis)use as high stakes assessments. They are often perceived as threatening and unfair because they are often used to rank or select people, and are not helpful to the individual learner. Moreover, their relevance is often brought into question because they don't directly measure what we really care about—the ability to apply knowledge and skills in real-life contexts.
Tests that ask people to directly demonstrate their skills in (1) the real world, (2) real-world simulations, or (3) as they are applied to real-world scenarios are called performative tests. These tests usually do not have "right" answers. Instead, they employ objective criteria to evaluate performances for the level of skill demonstrated, and often play a formative role by providing feedback designed to improve performance or understanding. This is the kind of assessment you want if what you care about is deep understanding, reasoning skills, or performance in real-world contexts.
Performative tests are the most difficult tests to make, but they are the gold standard if what you want to know is the level of competence a person is likely to demonstrate in real-world conditions—and if you're interested in supporting development. Standardized performative tests are not yet widely used, because the methods and technology required to develop them are relatively new, and there is not yet a large industry devoted to making them. But they are increasingly popular because they support learning.
Unfortunately, performative tests may initially be perceived as threatening because people's attitudes toward tests of knowledge and skill have been shaped by their exposure to high stakes multiple choice tests. The idea of testing for learning is taking hold, but changing the way people think about something as ubiquitous as testing is an ongoing challenge.
Lectical Assessments are performative tests—tests for learning. They are designed to support robust learning—the kind of learning that optimizes the growth of essential real-world skills. We're the leader of the pack when it comes to the sophistication of our methods and technology, our evidence base, and the sheer number of assessments we've developed.
 Frederick P. Morgeson, et al. (2007) Are we getting fooled again? Coming to terms with limitations in the use of personality tests for personnel selection, Personnel Psychology, 60, 1029-1033.
When I was a kid, the main way school performance was measured was with letter grades. We got letter grades on almost all of our work. Getting an A meant you knew it all, a B meant you didn't quite know it all, C meant you knew enough to pass, D meant you knew so little you were on the verge of faiing, and F meant you failed. If you always got As you were one of the really smart kids, and if you always got Ds and Fs you were one of the dumb kids. Unfortunately, that's how we thought about it, plain and simple.
If I got a B, my teacher and parents told me I could do better and that I should work harder. If I got a C, I was in deep trouble, and was put on restriction until I brought my grade up. This meant more hours of homework. I suspect this was a common experience. It was certainly what happened on Father Knows Best and The Brady Bunch.
The best teachers also commented on our work, telling us where we could improve our arguments or where and how we had erred, and suggesting actions we could take to improve. In terms of feedback, this was the gold standard. It was the only way we got any real guidance about what we, as individuals, needed to work on next. Letter grades represented rank, punishment, and reward, but they weren't very useful indicators of where we were in our growth as learners. Report cards were for parents.
Usher in Lectica and DiscoTest
One of our goals here at Lectica has been to make possible a new kind of report card—one that:
delivers scores that have rich meaning for students, parents, and decision-makers,
provides the kind of personal feedback good teachers offer, and
gives students an opportunity to watch themselves grow.
This new report card—illustrated on the right—uses a single learning "ruler" for all subjects, so student growth in different subjects can be shown on the same scale. In the example shown here, each assessment is represented by a round button that links to an explanation of the student's learning edge at the time the assessment was taken.
This new report card also enables direct comparisons between growth trajectories in different subject areas.
An additional benefit of this new report card is that it delivers a rich portfolio-like account of student growth that can be employed to improve admissions and advancement decisions.
And finally, we're very curious about the potential psychological benefits of allowing students to watch how they grow. We think it's going to be a powerful motivator.
Recently, members of our team at Lectica have been discussing potential misuses of Lectical Assessments, and exploring the possibility that they could harm some students. There are serious concerns that require careful consideration and discussion, and I urge readers to pitch in.
One of the potential problems we've discussed is the possiblilty that students will compare their scores with one another, and that students with lower scores will suffer from these comparisons. Here's my current take on this issue.
Students receive scores all the time. By third grade they already know their position in the class hierarchy, and live everyday with that reality. Moreover, despite the popular notion that all students can become above average if they work hard enough, average students don't often become above average students, which means that during their entire 12 years of schooling, they rarely receive top rewards (the best grades) for the hard work they do. In fact, they often feel like they're being punished even when they try their best. To make things worse, in our current system they're further punished by being forced to memorize content they haven't been prepared to understand, a problem that worsens year by year.
Lectica's approach to assessment can't prevent students from figuring out where their scores land in the class distribution, but we can give all students an opportunity to see themselves as successful learners, no matter where their scores are in that distribution. Average or below average students may still have to live with the reality that they grow at different rates than some of their peers, but they'll be rewarded for their efforts, just the same.
I've been told by some very good teachers that it is unacceptable to use the expression "average student." While I share the instinct to protect students from the harm that can come from labels, I don't share the belief that being an average student is a bad thing. Most of us were average students—or to be more precise, 68% of us were within one standard deviation of the mean. How did being a member of the majority become a bad thing? And what harm are we doing to students by creating the illusion that we are all capable of performing above the mean?
I don't think we hurt children by serving up reality. We hurt them when we mislead them by telling them they can all be above average, or when we make them feel hopeless by insisting that they all learn at the same pace, then punishing them when they can't keep up.
I'm not saying it's not possible to raise the average. We do it by meeting the specific learning needs of every student and making sure that learning time is spent learning robustly. But we can't change the fact that there's a distribution. And we shouldn't pretend this is the case.
Lectical Assessments are tests, and are subject to the same abuses as other tests. But they have three attributes that help mitigate these abuses. First, they allow all students without severe disabilities to see themselves as learners. Second, they help teachers customize instruction to meet the needs of each student, so more kids have a chance to achieve their full potential. And finally, they reward good pedagogy—even in cases in which the assessments are being misused. After all, testing drives instruction.
DiscoTests and conventional standardized tests can be thought of as complementary. They are designed to test different kinds of skills, and research confirms that they are successful in doing so. Correlations between scores on the kind of developmental assessments made by DTS and scores on conventional multiple choice assessments is in the .40-.60 range. That means that somewhere between 16% to 36% of the kind of learning that is captured by conventional assessments is likely to overlap with the kind of learning that is captured by DiscoTests.
The table below provides a comparison of DiscoTests with conventional standardized tests on a number of dimensions.
Cognitive developmental theory, Dynamic Skill Theory, Test theory
Fischer’s Dynamic Skill Scale, an exhaustively researched general developmental scale, which is a member of a family of similar scales that were developed during the 20th century.
Statistically generated scales, different for each test (though some tests are statistically linked)
Empirical, fine-grained & precise, calibrated to the dynamic skill scale
Empirical, coarse-grained and general
Primary item type
More or less sophisticated forms of multiple choice
Reasoning with knowledge, knowledge application, making connections between new and existing knowledge, writing
Content knowledge, procedural knowledge
Carefully selected “big ideas” and the concepts and skills associated with them.
The full range of content specified in state standards for a given subject
Yes, (1) each DiscoTest focuses on ideas and skills central K-12 curricula, (2) test questions require students to thoughtfully apply new knowledge and connect it with their existing knowledge, (3) students receive reports with targeted feedback and learning suggestions, (4) teachers learn how student knowledge develops in general and on each targeted concept or skill.
Not really, though increasingly claim to be
Embeddable in curricula
Yes, DiscoTests are designed to be part of the curriculum.
Yes, statistically, calibrated to the skill scale
Yes, statistically only
Low. Selection decisions are based on performance patterns over time on many individual assessments.
High. Selection decisions are often based on single assessments.
Direct tests that focus on deepening and connecting knowledge about key concepts and ideas, while developing broad skills that are required in adult life, such as those required for reasoning, communicating, and problem-solving.
Tests of proxies, focus on ability to detect correct answers.
.91+ for a single age cohort (distinguishes 5-6 distinct levels of performance).
For high stakes tests, usually .95+ for a single age cohort (distinguishes 6-7 distinct levels of performance).
At the end of October, the Century Foundation released a paper entitled, Eight reasons not to tie teacher pay to standardized test results. I agree with their conclusions, and would add that even if all standardized tests were extremely reliable and measured exactly what they intended to measure, this would be a bad idea. This is because success in the adult world requires a multiplicity of skills and forms of knowledge, and tests focus on only some of these, one at a time. Until we can construct multifaceted longitudinal stories about the progress of individual students that are tied to a non-arbitrary standardized metric, we should not even consider linking student evaluations to teacher pay.