How the Pioneers of the MOOC Got It Wrong (from IEEE), As Predicted

There is a sense of vindication that the predictions that many of us made about MOOCs have been proven right, e.g., see this blog post where I explicitly argue (as the article below states) that MOOCs misunderstand the importance of active learning. It’s disappointing that so much effort went wasted.  MOOCs do have value, but it’s much more modest than the sales pitch.

What accounts for MOOCs’ modest performance? While the technological solution they devised was novel, most MOOC innovators were unfamiliar with key trends in education. That is, they knew a lot about computers and networks, but they hadn’t really thought through how people learn.

It’s unsurprising then that the first MOOCs merely replicated the standard lecture, an uninspiring teaching style but one with which the computer scientists were most familiar. As the education technology consultant Phil Hill recently observed in the Chronicle of Higher Education, “The big MOOCs mostly employed smooth-functioning but basic video recording of lectures, multiple-choice quizzes, and unruly discussion forums. They were big, but they did not break new ground in pedagogy.”

Indeed, most MOOC founders were unaware that a pedagogical revolution was already under way at the nation’s universities: The traditional lecture was being rejected by many scholars, practitioners, and, most tellingly, tech-savvy students. MOOC advocates also failed to appreciate the existing body of knowledge about learning online, built over the last couple of decades by adventurous faculty who were attracted to online teaching for its innovative potential, such as peer-to-peer learning, virtual teamwork, and interactive exercises. These modes of instruction, known collectively as “active” learning, encourage student engagement, in stark contrast to passive listening in lectures. Indeed, even as the first MOOCs were being unveiled, traditional lectures were on their way out.

Source: How the Pioneers of the MOOC Got It Wrong – IEEE Spectrum

Research+Practice Partnerships and Finding the Sweet Spots: Notes from the ECEP and White House Summit

I wrote back in October about the summit on state implementation of the CS for All initiative which we at Expanding Computing Education Pathways (ECEP) alliance organized with the White House Office of Science and Technology Policy (OSTP). You can see the agenda here and a press release on the two days of meetings here.

I have been meaning to write about some of the lessons I learned in those two days, but have been simply slammed this month. I did finally write about some of the incremental steps that states are taking towards CS for All in my Blog@CACM post for November. That post is about the models of teacher certification that are developing, the CSNYC school-based mandate, and New Hampshire’s micro-certifications.

In this post, I want to tell you about a couple of the RPC ideas that I found most compelling. The first part of the day at the Eisenhower Executive Office Building (EEOB) on the White House grounds was organized by the Research+Practice Collaboratory (RPC). I was the moderator for the first panel of the day, where Phil Bell, Nichole Pinkard, and Dan Gallagher talked about the benefits of combining research plus practice.

I was excited to hear about the amazing work that Nichole Pinkard (pictured above) is doing in Chicago, working with Brenda Wilkerson in Chicago Public Schools. Nichole is a learning scientist who has been developing innovative approaches to engaging urban youth (see her Digital Youth Network website). She has all these cool things she’s doing to make the CS for All efforts in Chicago work. She’s partnering with Chicago parks and libraries — other than schools, they’re the ones who cover the city and connect with all kids. She’s partnering with Comcast to create vans that can go to parks to create hotspots for connectivity. Because she’s a researcher working directly with schools, they can do things that researchers alone would find hard to do — like when a student shows up to a CS activity, she can email the student’s parents to tell them the next steps to make sure that they continue the activity at home.

There was a second panel on “Finding the Sweet Spot: What Problems of Practice are Ripe for Knowledge Generation?” I didn’t know Shelley Pasnik from the Center for Children and Technology, and she had an idea I really liked that connected to one of Nichole’s points. Shelley emphasizes “2Gen learning,” having students bring with them parents or even grandparents so that there are two generations of learners involved. The older generation can learn alongside the student, and keep the student focused on the activity.

I know that the RPC folks are producing a report on their activity at the summit, so I’m sure we’ll be hearing more about their work.

Making learning effective, efficient, and engaging: An Interview With an Educational Realist and Grumpy Old Man, Paul Kirschner

I am a fan of Paul Kirschner‘s work.  This interview is great with useful insights about education — deep and pragmatic thinking.

I want to fundamentally understand how people can learn in effective, efficient, and enjoyable ways, and how you can teach and design learning materials to achieve this objective. If a learner doesn’t enjoy the learning experience, even if it’s effective and/or efficient, they won’t do it. The same is true for teaching: that is it must also be effective, efficient, and enjoyable for the teacher because if a teacher doesn’t enjoy the teaching process, even if it’s effective and/or efficient, they won’t do it.

Source: GUEST POST: An Interview With an Educational Realist and Grumpy Old Man — The Learning Scientists

Are there some students who can’t learn how to code? Teachers must always answer “No!”

The below-linked article does a good job of considering the argument about whether everyone can learn to program, and comes to the same conclusion that I do — a CS teacher must always believe that everyone can learn to program.

Indeed, one can find a good number of opinionators weighing in on the subject. In “Separating Programming Sheep from Non-Programming Goats,” Stack Exchange co-founder Jeff Atwood cites Bornat’s initial paper and concludes, “the act of programming seems literally unteachable to a sizable subset of incoming computer science students.” Linux creator Linus Torvalds has been quoted as saying, “I actually don’t believe that everybody should necessarily try to learn to code” — although, he does propose that people be exposed to it to see if they have “the aptitude.” Clayton Lewis of the University of Colorado at Boulder conducted a survey in which 77% of responding computer science faculty strongly disagreed with the statement “Nearly everyone is capable of succeeding in the computer science curriculum if they work at it.” As a “bright-eyed beginner” (with a scant 15 years of introductory programming teaching under my belt), it’s hard for me to accept the assertion that there are “some who can’t.” Such reasoning smacks of elitism and prejudice, even if such attitudes aren’t expressed consciously.

Of course, I’ll be the first to admit that my own opinion rests heavily on my own preconceptions: I’ve always had that “Montessori feeling” — every interested student should be given a chance to try, and sometimes fail, in a supportive environment.So, rather than give up on some, shouldn’t educators themselves keep trying? The inverse to the question “are there some students who can’t learn?” is this question, “are there some students whom our (current) teaching methods can’t reach?” The first question by itself implies a “yes,” and thus closes a door on some students. The second question opens up a world of inquiry: if basic coding concepts are truly so simple (as they truly are once the abstraction is understood), what do we need to do to bring the hard cases home?

Source: Are there some students who can’t learn how to code? – O’Reilly Radar

New OECD Report Slams Computers And Says Why They Can Hurt Learning: It’s all about the pedagogy

My PhD advisor, Elliot Soloway, considers a new report on the value of computers in education, and gets to the bottomline.  To swipe a line from Bill Clinton, “It’s the pedagogy, stupid!”  Of course, I agree with Elliot, and it’s why Lecia Barker’s findings are so disturbing.  We have to be willing to change pedagogy to improve learning.

The findings are the findings, but what is really interesting is a statement that Andreas Schleicher, the director of OECD, made as to why the impact of technology is negative. In the foreword to the OECD report, he writes, “…adding 21st century technologies to 20th century teaching practices will just dilute the effectiveness of teaching.”WOW! In this one sentence, Schleicher names clearly what he sees as the root cause of the lack of technology’s impact on student achievement. While the NYT’s articles danced around the issues, Schleicher doesn’t pull any punches: The reason computers are not having a positive impact lies in the use of outmoded teaching practices that do not truly exploit the opportunities that a 1-to-1 classroom affords.

Source: New OECD Report Slams Computers — and Actually Says Why They Can Hurt Learning — THE Journal

Little Evidence That Executive Function Interventions Boost Student Achievement: So why should computing?

Here’s how I interpret the results described below.  Yes, having higher executive function (e.g., being able to postpone the gratification of eating a marshmallow) is correlated with greater achievement.  Yes, we have had some success teaching some of these executive functions.  But teaching these executive functions has not had any causal impact on achievement.  The original correlations between executive function and achievement might have been because of other factors, like the kids who had higher executive function also had higher IQ or came from richer families.

This is relevant for us because the myth that “Computer science teaches you how to think” or “Computer science teaches problem-solving skills” is pervasive in our community.  (See a screenshot of my Google search below, and consider this blog post of a few weeks ago.)  But there is no support for that belief.  If this study finds no evidence that explicitly teaching thinking skills leads to improved transferable achievement, then why should teaching computer science indirectly lead to improved thinking skills and transferable achievement to other fields?

Why do CS teachers insist that we teach for a given outcome (“thinking skills” or “problem-solving skills”) when we have no evidence that we’re achieving that outcome?

The meta-analysis, by researchers Robin Jacob of the University of Michigan and Julia Parkinson of the American Institutes for Research, analyzed 67 studies published over the past 25 years on the link between executive function and achievement. The authors critically assessed whether improvements in executive function skills—the skills related to thoughtful planning, use of memory and attention, and ability to control impulses and resist distractions—lead to increases in reading and math achievement , as measured by standardized test scores, among school-age children from preschool through high school. More than half of the studies identified by the authors were published after 2010, reflecting the rapid increase in interest in the topic in recent years.

While the authors found that previous research indicated a strong correlation between executive function and achievement, they found “surprisingly little evidence” that the two are causally related.

“There’s a lot of evidence that executive function and achievement are highly correlated with one another, but there is not yet a resounding body of evidence that indicates that if you changed executive functioning skills by intervening in schools, that it would then lead to an improvement in achievement in children,” said Jacob. “Although investing in executive function interventions has strong intuitive appeal, we should be wary of investing in these often expensive programs before we have a strong research base behind them.”

via Study: Little Evidence That Executive Function Interventions Boost Student Achievement.

Important paper at SIGCSE 2015: Transferring Skills at Solving Word Problems from Computing to Algebra Through Bootstrap

I was surprised that this paper didn’t get more attention at SIGCSE 2015.  The Bootstrap folks are seeing evidence of transfer from the computing and programming activities into mathematics performance.  There are caveats on the result, so these are only suggestive results at this time.

What I’d like to see in follow-up studies is more analysis of the students.  The paper cited below describes the design of Bootstrap and why they predict impact on mathematics learning, and describes the pre-test/post-test evidence of impact on mathematics.  When Sharon Carver showed impact of programming on problem-solving performance (mentioned here), she looked at what the students did — she showed that her predictions were met.  Lauren Margulieux did think-aloud protocols to show that students were really saying subgoal labels to themselves when transferring knowledge (see subgoal labeling post).  When Pea & Kurland looked for transfer, they found that students didn’t really learn CS well enough to expect anything to transfer — so we need to demonstrate that they learned the CS, too.

Most significant bit: Really cool that we have new work showing potential transfer from CS learning into other disciplines.

Many educators have tried to leverage computing or programming to help improve students’ achievement in mathematics. However, several hopes of performance gains—particularly in algebra—have come up short. In part, these efforts fail to align the computing and mathematical concepts at the level of detail typically required to achieve transfer of learning. This paper describes Bootstrap, an early-programming curriculum that is designed to teach key algebra topics as students build their own videogames. We discuss the curriculum, explain how it aligns with algebra, and present initial data showing student performance gains on standard algebra problems after completing Bootstrap.

via Transferring Skills at Solving Word Problems from Computing to Algebra Through Bootstrap.

Going beyond the cognitivist in computing education research questions

In Josh Tenenberg’s lead article in the September 2014 ACM Transactions on Computing Education (linked below), he uses this blog, and in particular, this blog post on research questions, as a foil for exploring what questions we ask in computing education research.  I was both delighted (“How wonderful! I have readers who are thinking about what I’m writing!”) and aghast (“But wait!  It’s just a blog post!  I didn’t carefully craft the language the way I might a serious paper!”) — but much more the former.  Josh is kind in his consideration, and raises interesting issues about our perspectives in our research questions.

I disagree with one part of his analysis, though.  He argues that my conception of computing education (“the study of how people come to understand computing”) is inherently cognitivist (centered in the brain, ignoring the social context) because of the word “understand.”  Maybe.  If understanding is centered in cognition, yes, I agree.  If understanding is demonstrated through purposeful action in the world (i.e., you understand computing if you can do with computing what you want), then it’s a more situated definition.  If understanding is a dialogue with others (i.e., you understand computing if you can communicate about computing with others), then it’s more of a sociocognitive definition.

The questions he calls out are clearly cognitivist.  I’m guilty as charged — my first PhD advisor was a cognitive scientist, and I “grew up” as the learning science community was being born.  That is my default position when it comes to thinking about learning.  But I think that my definition of the field is more encompassing, and in my own work, I tend toward thinking more about motivation and about communities of practice.

Asking significant research questions is a crucial aspect of building a research foundation in computer science CS education. In this article, I argue that the questions that we ask are shaped by internalized theoretical presuppositions about how the social and behavioral worlds operate. And although such presuppositions are essential in making the world sensible, at the same time they preclude carrying out many research studies that may further our collective research enterprise. I build this argument by first considering a few proposed research questions typical of much of the existing research in CS education, making visible the cognitivist assumptions that these questions presuppose. I then provide a different set of assumptions based on sociocultural theories of cognition and enumerate some of the different research questions to which these presuppositions give rise. My point is not to debate the merits of the contrasting theories but to demonstrate how theories about how minds and sociality operate are imminent in the very questions that researchers ask. Finally, I argue that by appropriating existing theory from the social, behavioral, and learning sciences, and making such theories explicit in carrying out and reporting their research, CS education researchers will advance the field.

via Asking Research Questions.

ICER 2014 Preview: Briana Morrison and an instrument for measuring cognitive load

The International Computing Education Research (ICER) conference 2014 is August 11-13 in Glasgow (see program here).  My involvement starts Saturday August 9 when we have the welcome dinner for the doctoral consortium, which will be run all day on Sunday August 10 (Sally Fincher and I are chairing).  The main conference presentations continue through noon on Wednesday August 13. The rest of August 13 and into Thursday August 14 will be a new kind  of ICER session: Critical Research Review for work-in-progress.  I’m presenting on some new work that I’m getting feedback on related to constructionism for adults. I’ll blog about that later.

Briana Morrison is presenting her paper on developing an instrument to measure cognitive load (early version of paper available here), with co-authors Brian Dorn (my former student, now a chaired assistant professor at U. Nebraska-Omaha) and me.  Briana’s research is looking at the impacts of modality on program understanding for students.  Does audio vs. video vs. both have an impact on student understanding?  She’s controlling for time in all her presentations, and plans to measure performance…and cognitive load.  Is it harder for students to understand audio descriptions of program code, or to try to read text descriptions while trying to read text programs?

There wasn’t a validated instrument for her to use to measure the components of cognitive load — so she created one.  She took an existing instrument, and adapted it to computer science.  She and Brian did the hard work of crunching all the correlations and load factors to make sure that the instrument is still valid after her adaptation.  It’s an important contribution in terms of giving computing education researchers another validated tool for measuring something important about learning.

Online education is dead; long live Mentored Simulated Experiences

Roger Schank (one of the founders of both cognitive science and learning science) declares MOOCs dead (including Georgia Tech’s OMS degree, explicitly), while recommending a shift to Mentored Simulation Experiences.  I find his description of MSE’s interesting — I think our ebook work is close to what he’s describing, since we focus on worked examples (as a kind of “mentoring”) and low cognitive-load practice (with lots of feedback).

So, while I am declaring online education dead, because every university is doing it and the market will soon be flooded with crap, I am not declaring the idea of a learning by doing mentored experience dead.

So, I  propose a new name, Mentored Simulated Experiences.

via Education Outrage: Online education and Online degrees are dead; now let’s move on to something real.

A flawed case against teaching: Scaffolding, direct instruction, and learner-centered classrooms

Premise 1: Teaching is a human endeavor that does not and cannot improve over time.

Premise 2: Human beings are fantastic learners.

Premise 3: Humans don’t learn well in the teaching-focused classroom.

Conclusion: We won’t meet the needs for more and better higher education until professors become designers of learning experiences and not teachers.

at Change | The Case Against Teaching

——

Interesting argument linked above, but wrong.

• Premise 1: Teaching does improve with time.  Gerhard Fischer published a wonderful piece many years ago that showed how skiing instruction has improved over time, and that the approaches used can be understood in terms of cognitive science.
• Premise 2: Humans are fantastic learners, but as Kirschner, Sweller, and Clark showed, humans learn much better with direct instruction.
• Premise 3: No, no one learns well in a teaching-focused classroom.  However, many teachers help their students learn better in a student-centered classrooms.
• The Conclusion doesn’t follow from the premises at all.

 

Addressing Computer Science Student Misconceptions with Contrasts

I have wanted to figure out how to use in my class the interesting findings about the use of video to address science misconceptions.  The idea is that you want to use real student misunderstandings and contrast them with better, more powerful ways of understanding something.  The challenge for me has been how to get those misunderstandings in class.  I don’t want to call on someone that I know has a misconception and have him lay out his explanation — just to pounce on it to say, “And that’s wrong!”

Then I realized my chance this last week.  I was grading the second midterm, and saw all these surprising misconceptions made evident in the students answers.  Normally, the class time after a midterm is about going over the midterm answers.  I decided instead to make it about the misconceptions.

I built a Powerpoint slide deck filled with these contrasting bits of code (like the contrasting explanations in the science videos) and with alternative code for answering the same problem.  I tried to disguise the code so as not to embarrass any particular student.  For example, I changed variable names — and since students expect that changing variable names should make plagiarized code impossible to detect, that should be enough, right?

I formed students into pairs, and then put up the slides and asked for them to respond or to answer a question in their pair.  For example, I noticed that several students seemed to confuse IF and WHEN.  So I put up this slide.

I asked students to punch into their clickers what they thought “A” would print out.  And yes, about 20% of the students guessed something other than “1.” I executed “A” as a way of checking the answer. I then had students answer for “B.”  I could hear lots of discussion suggesting that students were seeing the difference between IF and WHILE.

I put code up like this:

I had each group discuss what would be the output of this code, then took suggestions of the output from around the room.  I wrote them on the board, and then had pairs vote on which answer they most agreed with.  By the time we voted, everyone got it right — just generating the options, and hearing the discussion as each option went up, they figured out what the best answer was.  I really liked hearing students “discovering” invariants as they talked, e.g., “The loop can never end, because you never change node1a in the loop!”

I have no real evidence of learning here — we’ll see how things go in the class.  I do have a sense that this was a more fruitful activity for a most-midterm discussion than just me giving the answers and telling them why the wrong answers were wrong.  That recitation of sins usually just results in students coming up to me with, “You only give me 5 points for this, but  based on the discussion, I think I deserved 7.”  This way, the discussion was punctuated more often with “Ohhhh — now I get it!”

 

 

 

Visiting Indiana University this week

I’m visiting Indiana University this week, and giving two talks.  If any readers are in the Bloomington area, I hope you can stop by!

9:30 am Jan 29
Colloquium
Education 2140

Title: Improving Success in Learning Computer Science Using Lessons from Learning Sciences

Abstract: Learning computer science is difficult, with multiple international studies demonstrating little progress. We still understand too little about the cognitive difficulties of learning programming, but we do know that we can improve success by drawing on lessons from across learning sciences. In this talk, I will describe three examples, where we improve success in learning computer science through application of lessons and models from the learning sciences. We increased the retention of non-CS majors in a required CS course by increasing the relevance of the course (informed by Eccles’ model of achievement-related choices), though we are limited in how far we can go because legitimate peripheral participation is less relevant. We improved opportunities to learn in a collaborative forum by drawing on lessons from anchored instruction, but were eventually defeated by student perceptions of culture. We have improved learning and transfer of knowledge about programming by using subgoal labeling to promote self-explanations.

9 am Thursday Jan 31
SoIC Colloquium Series
IMU State Room East

Title: Three Lessons in Teaching Computing to Everyone

Abstract:  My colleagues and I have been studying how to teach computer science, to CS majors, to non-CS undergraduates, and to adult professionals.  In this talk, I’ll talk about some of what we’ve learned, organized around three lessons.  Lesson #1: We typically teach computer science too abstractly, and by teaching it in a context (e.g., media, robots, Nintendo GameBoys, Photoshop), we can dramatically improve success (retention and learning) for both traditional and non-traditional CS learners. Lesson #2: Collaboration can create opportunities for learning, but classroom culture (e.g., competition) trumps technology (Wikis).  Lesson #3: Our greatest challenge in computer science education is improving teaching, and that will require changes in high schools, in public policy, and in universities.

What are the cognitive skills needed for model-building?

Mylène is describing in the below blog post about how she’s helping her students develop a set of cognitive skills (including a growth mindset) to help them build models.  What I found fascinating in her post were the implicit points, obvious to her, about what the students didn’t know.  One student said, “I wish someone had told me this a long time ago.”  What are the cognitive skills necessary to enable people to build models, or program?  Causal thinking is absolutely critical, of course. What else is necessary that we haven’t identified?  We need to check if students have those skills, or if we need to teach them explicitly.

Last year I found out in February that my students couldn’t consistently distinguish between a cause and a definition, and trying to promote that distinction while they were overloaded with circuit theory was just too much.  So this year I created a unit called “Thinking Like a Technician,” in which I introduced the thinking skills we would use in the context of everyday examples.

via Growth-Mindset Resource Could Support Model-Building « Shifting Phases.

Teaching CS in Schools with Meaning: Contexts and problems come first

Richard Hake relates a story from Alan Schoenfeld:

One of the problems on the NAEP [National Assessment of Educational Progress] secondary mathematics exam, which was administered to a stratified sample of 45,000 students nationwide, was the following: An army bus holds 36 soldiers. If 1128 soldiers are being bused to their training site, how many buses are needed?

Seventy percent of the students who took the exam set up the correct long division and performed it correctly. However, the following are the answers those students gave to the question of ‘how many buses are needed?’: 29% said…31 remainder 12; 18% said…31; 23% said…32, which is correct. (30% did not do the computation correctly).

It’s frightening enough that fewer than one-fourth of the students got the right answer. More frightening is that almost one out of three students said that the number of buses needed is ‘31 remainder 12’.

The problem that Hake and Schoenfeld are both pointing out is that we teach mathematics (and much else in our curriculum) completely divorced from the contexts in which the mathematics make sense. The children taking the NAEP knew how to do the mathematics, but not why, and not nearly enough about how the mathematics helps to solve a problem.  They knew mathematics, but now what it was for.

Hake relates this story in an article about Louis Paul Benezet, an educator who ran a radical experiment in the 1930’s. Benezet saw how mindlessly young children were performing mathematics, so he made a dramatic change: Almost entirely remove mathematics from grades 1-5. Start teaching mathematics in grade 6, with a focus on problem-solving (e.g., start from estimation, so that you have a sense of when an answer is reasonable). Sixth graders can understand the problems for which one should use mathematics.  The point is not to introduce the solution, until students understood the problem.  Remarkably, the experimental 6th graders completely caught up in just four months to the 6th graders who had had mathematics all five previous years.

The experiment was radical then, and as far as I know, has not been replicated — even though evaluations suggest it worked well.  It runs against our intuition about curriculum.  Mathematics is important, right?  We should do more of it, and as early as possible.  How could you remove any of it?  Benezet argued that, instead, young children should do more reading and writing, saving the mathematics for when it made sense.

Hake uses Benezet (and the evaluation of Benezet’s approach by Berman) to argue for a similar radical approach to physics education — teaching some things to kids to build up intuition, but with a focus on using physics to solve problems, and introducing the problems only when the students can understand them. There are lessons here for computing education, too.

• First, problems and contexts always come first! Teaching a FOR loop and arrays before teaching a problem in which they are useful just leads to rote learning, brittle knowledge which can’t be applied anywhere, let alone transferred.
• Second, the answer to the question “What should be removed from our overly-packed curriculum to squeeze computer science in?” may be “Get rid of the overly-packed curriculum.”  There may be things that we’re teaching at the wrong time, in the wrong way, which really is just a waste of everyone’s time.
• Finally, just how young should we be teaching programming? Several people sent me the link to the report about Estonia teaching all first graders to program (quoted and linked below). Sure, you can teach first graders to program — but will they understand why they’re programming? What problems will first graders recognize as problems for which programming is a solution?

I do applaud the national will in Estonia to value computing education, but I do wonder if teaching programming so young leads to rote learning and the idea that “31 remainder 12” is a reasonable number of buses.

We’re reading today that Estonia is implementing a new education program that will have 100 percent of publicly educated students learning to write code.

Called ProgeTiiger, the new initiative aims to turn children from avid consumers of technology (which they naturally are; try giving a 5-year-old an iPad sometime) into developers of technology (which they are not; see downward-spiraling computer science university degree program enrollment stats).

ProgreTiiger education will start with students in the first grade, which starts around the age of 7 or 8 for Estonians. The compsci education will continue through a student’s final years of public school, around age 16. Teachers are being trained on the new skills, and private sector IT companies are also getting involved, which makes sense, given that these entities will likely end up being the long-term beneficiaries of a technologically literate populace.

via Guess who’s winning the brains race, with 100% of first graders learning to code? | VentureBeat.