Migrate to a self-hosted WordPress

I have migrated to a self-hosted blog at http://bodongchen.com/blog/ and will keep posting (hopefully more frequently) there. You are more than welcomed to take a look!

Bye, WordPress.com!

ICLS 2012: Envisioning the Future of Learning

I was at Sydney for the 10th International Conference of the Learning Sciences (ICLS2012) last week. It was my first ICLS journey, and I enjoyed it a lot. This conference provided me with opportunities to meet and chat with influential scholars in this field in person. The program listed an intriguing lineup of cutting-edge research in the field of learning sciences, and it was always a struggle for me to decide on which session to attend during each parallel session.

Like my friends Stian Haklev and Cresencia Fong, I also tried to take notes during the conference. You can find my list of notes on my shard Evernote notebook. I hope you find my notes complimentary to notes taken by other folks.

• Day 1: Keynote 1 Janet Kolodner
• Day 2: Keynote 2 and 3 paper sessions
• Day 3 – Keynote 3 Judy Kay
• Day 3 – Symposium 5.9 Conceptual change
• Day 3: Session 6.16

I presented a paper on my ground-breaking work on “promisingness judgments.” The title of my presentation is “Students’ Intuitive Understanding of Promisingness and Promisingness Judgments to Facilitate Knowledge Advancement.” My slides can be found here: http://goo.gl/EGqY9

I came back from the conference with a strong motivation to carry on my own research and a lot of ideas to keep me busy all through July. I look forward to CSCL2013 next year in Madison, WI.

Sifteo Cubes, and possibility of “playing” with ideas

Sifteo is a special gaming system that brings digital plays to physical objects. Inspired by classic games such as chess and mahjong, Sifteo embraces the power of manipulable physical blocks that people have been playing with for thousands of years. Compared with most video games that attracts people to delve into virtual worlds behind a computer screen, Sifteo brings the joy of touching and messing around with a few mahjong-size cubes back to people. However, it’s far richer than a mahjong brick. Sifteo cubes can sense a variety of gestures, such as clicking, shaking, and tilting, and they can also talk to each other through wireless communication. People can program on those cubes by incorporating specific contexts or story lines to give meaning to those gestures. Ever since its debut in 2009 at a TED event through a presentation by its inventor David Merrill (see below), a graduate student at MIT Media Lab at that time, Sifteo has attracted a lot of attention and interest from many people, especially educators.

However, although it has been there for quite a while, not many thorough review articles could be found on the Internet. Many articles rest on the surface of introducing basic functions of Sifeo Cubes and a handful of games that are currently available from its games store. But I did find a few blog posts that were pretty interesting. A few articles mentioned important limitations of this game system, including its constrain of having a computer around during game play, a very limited collection of games available right now, and its comparatively high price. Besides, a point made by Lori Emerson who tried Sifteo cubes in her humanity class is very interesting, criticizing that “these cubes seem to strongly encourage a passive acceptance of the interface and they discourage users from thinking about how the cubes work and from creating outside of the ready-made environment.” This point is reflected in one of those videos Merrill presented in his TED talk that showed a small kid who weren’t interested in what’s on those cubes at all but just wanted to put them in a stack. I found it ironic that Sifteo was trying to break the boundary of conventional user interaction with computer systems to allow people to “fast-prototype” ideas and safely make “productive mistakes”, as celebrated by Merrill in another TED talk, its confined design space is constricting people’s creation. As for now, I haven’t found any game from its games store that looks promising for education purpose. Many games are about sorting or calculation tasks; some other more interesting ones allow kids to do more, like create their own storyline with a few objects, but still it’s very restrictive. It might sound too picky before more powerful design emerges, but I strongly feel it needs to find a way to be more open to allow richer creation.

I recently got involved in a little research project involving Sifteo Cubes. It is a nice opportunity for me to put some thoughts on game design and my hands dirty on C# programming (for the first time). The main research question is pretty simple: By changing reward level or success rate from 25% to 75%, can we keep kids motivated in a game play? The rule of 25%-75% in gaming was discussed in Tom Charfield’s TED talk (also see below) and is worth more research. The game is also simple, but might be interesting enough for young kids. In each round, kids will first turn up a cube; the game will assign it a number and kids will keep flipping other Sifteo Cubes to find one that has the same number as the first one. So, the game is about pure luck, not really about educational values. But the experience of playing and programming with Sifteo makes me anxious to really design something on Sifteo, especially within a knowledge building context, to do more than ranking numbers or making words from characters.

The first question that came to my mind was: Can we manipulate ideas with cubes? In a traditional knowledge-building environment, ideas are put at the center of the space, usually embodied in student notes. In discourse, students put forward their ideas into the community space, build on each other’s ideas, making connection to other ideas by referencing, and creating higher level ideas to rise above current state of understanding. While a Sifteo cube can sense each other, how would ideas react to each other based on their innate characteristic? Similarly, while a cube can be clicked, tilted, and shaked by a human, what are the ways of manipulating an idea by a human? Then, is it possible and meaningful to build a bridge between these two? Since Knowledge Building theory is very much based on Karl Popper’s “three-world” cosmology that regards conceptual artifacts as objects existing independent of human mind, can we embody ideas into cubes to make the manipulation of ideas more natural (maybe natural only for some people), and to make knowledge building a game?! More and deeper thoughts are clearly needed here.

I wish to end this post with a quote from Diane Ackerman (also quoted by Merrill in his talk):

Play is our brain’s favorite way of learning.

Students’ misconceptions about geometric optics

A variety of conceptual change literature discusses people’s classic misconceptions on  scientific topics such as “seasons,” “gravity,” “circuit,” and “evolution.” Optics is usually not among the list. When preparing for a study, I got a chance to review some literature about students’ misconceptions about geometric optics. It is interesting that although those studies focused on different age groups, all the way from primary pupils to college students, misconceptions discussed in these studies have an amazing overlap. Apparently, many of our misconceptions about optics are hard to be fixed and might stay there forever regardless of science lessons.

From reviewed articles, I am compiling a list of misconceptions about geometric optics, clustering them according to their sub-topics, including vision, how light travel, shadow, reflection, refraction, and color of light. This list contains 52 distinctive misconceptions; it’s never intended to be exhaustive but complementary other lists such as this one. Please report back if you find yourself having any of the following misconceptions.

Vision

• Light is only present if observable effects are witnessed, e.g., a spot of light on a wall.
• Light is a necessary condition for humans to see, but cats are able to see in total darkness.
• Light is not necessary to see since we can see a little in a dark room.
• Objects are seen because they are bathed in light.
• To see an object it must be opaque.
• We see because there is a “visual ray” from out eyes to objects.
• We see because light comes to the eye and then goes to the object.
• We see because the eye is sending out ‘rays’ which return to the head with a message or picture.
• An individual’s eyes see the shape of an object.

How Light Travels

• Light is connected with its source or the effect produced. Light was not seen to exist between the two but “in the bulb” or “on the spot on the wall”.
• Light is an omnipresent medium and is not dentified with a particular source. The sky is filled with light.
• Only if a room is dark will light travel until it strikes an object.
• The distance travelled by light is dependent upon the perceived brightness of the light.
• The distance light travels is dependent upon its energy.
• Light travels a different distance depending upon whether it is day or night.
• Light does not travel during the day.
• Light does not travel at all during the night.
• Light from a car’s headlamps would only travel 400m or as far as the person looking at the headlamps, while light from the sun can travel millions of kilometers.
• Light velocity depends on the velocity of the light source.
• Light always travels in a straight line.
• Light needs an impetus to maintain its motion throughout space.
• Each point on a luminous object emits light in one direction.
• Light travels further at night than during the day.

Shadow

• Shadows can be conceived as an image, or as something belonging to an object.
• A shadow is a ‘reflection’ (reproduction) of an object.
• The stronger the source of light, give the bigger the shadow.
• The bigger the source of light, give the smaller the shadow.

Reflection

• After light strikes a blank sheet of paper or a mirror, the light remained on those surfaces.
• Mirrors reflect all light that shines on their surfaces.
• Shiny objects reflect more light than dull objects.
• Light only reflects off mirrors and other smooth surfaces.
• A flat mirror can only be made of glass or metallic.
• Objects are black because they do not reflect any light.
• A flat mirror forms real images.
• The image is resident on the surface of the mirror, or possibly just behind it.
• Images in a mirror can be in two places.
• If you are five feet tall, you need a five-foot tall mirror to see your entire body at once.
• You can see more of yourself if you move farther away from a mirror.

Refraction

• To be seen in a mirror, the object must be directly in front of the mirror or in the line-of-sight from the observer to the mirror.
• The image of an object placed to the side of a plane mirror does not exist for any observer.
• Reflection and refraction exclude each other, i.e., the presence of one prevents the presence of the other
• Half the image would disappear when half of a lens was used to form the image.
• Lenses are not necessary to form images.
• The size of an image depends on the size (diameter) of the lens used to form the image.
• The convergent lens increases the speed of the light.
• An optic prism disperses or refracts light that reaches it depending on its incidence.
• An optic prism must be a triangular prism.
• An optic prism can disperse light of any pure spectral colour.
• Light always passes straight through transparent material (without changing direction).
• An image can be seen on the screen regardless of where the screen is placed relative to a lens.
• To see a larger image on the screen, the screen should be moved further back.

Color of Light

• Colour is a property of objects, e.g., the book is red.

 

References

Blizak, D., Chafiqi, F., & Kendil, D. (2009). Students Misconceptions about Light in Algeria. Education and Training in Optics and Photonics (p. EMA5). Optical Society of America. Retrieved from http://www.opticsinfobase.org/abstract.cfm?URI=ETOP-2009-EMA5

Fetherstonhaugh, A. R. (1990). Misconceptions and light: A curriculum approach. Research in Science Education, 20(1), 105-113. doi:10.1007/BF02620485

Fetherstonhaugh, A., Happs, J., & Treagust, D. (1987). Student misconceptions about light: A comparative study of prevalent views found in Western Australia, France New Zealand, Sweden and the United States. Research in Science Education, 17(1), 156-164. doi:10.1007/BF02357183

Galili, I. (1996). Students’ conceptual change in geometrical optics. International Journal of Science Education, 18(7), 847-868. doi:10.1080/0950069960180709

Galili, I., & Hazan, A. (2000). Learners’ knowledge in optics: interpretation, structure and analysis. International Journal of Science Education, 22(1), 57-88. doi:10.1080/095006900290000

Galili, I., & Lavrik, V. (1998). Flux concept in learning about light: A critique of the present situation. Science Education, 82(5), 591-613. doi:10.1002/(SICI)1098-237X(199809)82:5<591::AID-SCE4>3.0.CO;2-4

Goldberg, F. M., & McDermott, L. C. (1986). Student difficulties in understanding image formation by a plane mirror. The Physics Teacher, 24(8), 472. doi:10.1119/1.2342096

Harrison, A. G., & Treagust, D. F. (1993). Teaching with analogies: A case study in grade-10 optics. Journal of Research in Science Teaching, 30(10), 1291-1307. doi:10.1002/tea.3660301010

Hecht, J. (2012). Recycled Fiber Optics: How Old Ideas Drove New Technology. Optics and Photonics News, 23(2), 22. doi:10.1364/OPN.23.2.000022

Kaewkhong, K., Mazzolini, A., Emarat, N., & Arayathanitkul, K. (2010). Thai high-school students’ misconceptions about and models of light refraction through a planar surface. Physics Education, 45(1), 97-107. doi:10.1088/0031-9120/45/1/012

Osborne, J. F., Black, P., Meadows, J., & Smith, M. (1993). Young children’s (7‐11) ideas about light and their development. International Journal of Science Education, 15(1), 83-93. doi:10.1080/0950069930150107

Palacios, F. J. P., Cazorla, F. N., & Madrid, A. C. (1989). Misconceptions on geometric optics and their association with relevant educational variables. International Journal of Science Education, 11(3), 273-286. doi:10.1080/0950069890110304

Pompea, S. M., Dokter, E. F., Walker, C. E., & Sparks, R. T. (2007). Using Misconceptions Research in the Design of Optics Instructional Materials and Teacher Professional Development Programs – OSA Technical Digest Series. Education and Training in Optics and Photonics (p. EMC2). Optical Society of America. Retrieved from http://www.opticsinfobase.org/abstract.cfm?URI=ETOP-2007-EMC2

Saxena, A. B. (1991). The understanding of the properties of light by students in India. International Journal of Science Education, 13(3), 283-289. doi:10.1080/0950069910130306

“Overview” of #AERA2012 Tweets

The AERA Annual Meeting is probably the largest getting-together of educational researchers and practitioners around the world. This year it has around 13,000 participants. Several hundreds of them are taking advantage of Twitter as a “backchannel” for communication at this conference. On Twitter, hashtags are usually used to easily aggregate tweets of a same topic together. This year, three hashtags, i.e. #AERA2012, #AERA, and #AERA12, are used, and #AERA2012 is the most popular one. For me as a person who cannot make it to the annual meeting this year, following tweets gathered around these hashtags provides me an opportunity to “participate” virtually.

However, to follow such an amount of tweets produced by hundreds of people seems to be a challenge. To make sure I have a chance to go back and review those tweets, I created a public archive of tweets containing the #AERA2012 hashtag by using “Twitter Archive Google Spreadsheet – TAGS v3.0” developed by Martin Hawksey. During the first few days of this conference, participants have produced more than 4,000 tweets, around 2500 of them are unique. Even if you’re ambitious enough to read all of them, it’s always nice if you can put the whole bunch of tweets in a magic box and let it tell what the main things people are talking about.

The Overview program is designed to do this trick. Overview is an open-source tool to help journalists find stories in large amounts of data, by cleaning, visualizing and interactively exploring large document and data sets. Although it was designed for journalists, it has potential uses in many other contexts. I exported the Twitter archive as a CSV file and put it into the Overview program. It did some preprocessing tasks as the first step, and then I can really start to explore this dataset. (For details about how to do this, read this blog post).

This is what I got at the first place. The left side panel presents a topic tree that helps me navigate the whole dataset by topics this programs identified (through some natural language processing techniques). The right side panel is a visualization of tweets, with each dot representing an individual tweet. One design principle of the Overview project is to combine the power of both machine and human to make sense of data, so as a user I am encouraged to go through this dataset, and assign tags to each topic; that’s what the central panel do.

So I went through the dataset by clicking on those topics (and sub-topics) in the topic tree one by one, and assign a tag to tweets that are about a same topic. This process was like coding when analyzing qualitative data. And what I did was just the first round of coding. After spending two hours doing this, here is what I got.

Both the topic tree and the visualization become colorful, because a list of tags with colors were created for clusters of tweets. The first round of tagging/coding identified 45 tags. They are rough and need further refinement. But by looking at the top 20 tags you can get a sense what colleagues were mainly talking about on Twitter during the conference. Results indicate that many people were using Twitter to share session information, invite people to their exhibitions, welcome people in various ways, share personal status (like I’m at a session about bla and it rocks), organize tweetup, etc. A number of tweets were about the case that US scholars were denied entry to Canada for the AERA meeting. Some tweets were about complains and discussions on wifi issues and confusion of multiple hashtags. Only a few among these top 20 tags were about content of presentations, such as race issues in education, bullying and cyber-bullying, higher education, and indigenous education. (Note: the tag “no-content” covers those tweets that have a list of question marks, maybe because they’re in a different language (I did notice colleagues from Japan were also tweeting). Apparently Overview needs to make improvement on internationalization.) Overall, this preliminary analysis of this dataset may sound discouraging to me who was eager to participate virtually by following the Twitter stream of this conference, simply because only a little portion of those tweets were about real content of presentations and few of them were related to my interests.

The Overview program can further cluster the visualization by running a force-directed layout (see below). Now you can see where those different clusters/topics are distributing and navigate manually to make sense of the dataset. You may realize there are a number of dark dots in the background. My sense is they are tweets this program is having a hard time assigning to any topic. You can zoom in to read what they’re really about and get a better understanding of the dataset, if you have time.

To summarize, this blog post presents a simple experiment using the Overview program to help me understand the “large” corpus of tweets produced by AERA Annual Meeting participants so far. Although the Overview program is still prototypic and needs further improvements, it did provide an interesting and fruitful way to achieve my goal. Meanwhile, this little experiment may also provide some insight for understanding usage of Twitter as a backchannel for communication at conferences.

Writing Comprehensive Exam

Comprehensive examination (comps) is a milestone for most PhD students. It’s not necessarily the biggest one but still quite important. As the guidelines from my program say:

The general purposes of the doctoral comprehensive examination are to assist doctoral students to integrate and deepen their understanding of major current theories and alternative views in scholarly literature in the field of curriculum studies, and in a particular area of study within curriculum.  Equally important, this examination requires students to apply some of  these ideas from scholarly literature to outlining and justifying a research method for an intended thesis study in a chosen area of specialization.

It took longer than I expected to start writing my comps, mostly because I wish what I write for the exam could turn out to be also useful for my thesis. After I eventually got started, it took me longer than I expected (again) to finish my writing. The topic I chose is in lack of literature (what?!), and writing a literature review requires more thoughts. Anyway, my comps got submitted two weeks ago, and I’d like to write a couple of words about it.

LaTeX

It might be wired to start with this word because it might not mean anything to many people, especially to folks in social sciences. If you don’t know it, LaTeX is a document markup language that you can use to mark up the structure and details of your paper, written in plain text, with a variety of commands, and convert it to nicely formatted PDF or other formats. It’s widely used by science and engineering folks because these guys need to deal with math formula and LaTeX is very powerful in this.

I have never used LaTeX before writing my comps, but I somehow decided to experiment with it this time. The results didn’t make me regret, for a couple of reasons:

1. LaTeX saved me from dealing with (unstable) formatting issues in MS Word and allow me to focus on content when writing. By changing a few words, you can the whole paper from an APA draft format to a double-column format looks like a journal.
2. LaTeX deal with bibliography and referencing gracefully. With help of some special reference packages, it allows you to finish tiring things with reference in a simple and easy way.
3. Playing with LaTeX brings me more joy than getting a new HDTV (but less than getting a new iPad maybe).

The down side is that it may take some time to setup a LaTeX environment on your computer and to learn some commands. But once you get through that process, you will not go back to use MS Word for a big writing project like thesis. So I am pretty determined to use LaTeX for my coming thesis writing.

I am attaching some resources that I found useful at the end of this blog.

Promisingness

Okay, this word is the biggest word in my comps, and it used to drive me crazy. The following is an excerpt from my comps:

The term of “promisingness” was originally introduced in the book Surpassing Ourselves: An Inquiry into the Nature and Implications of Expertise (Bereiter & Scardamalia, 1993), and was further elaborated in a later book Education and Mind in the Knowledge Age (Bereiter, 2002). However, in the past two decades this term has seldom appeared beyond the knowledge building community and there is little research to address this concept. This makes a literature review on promisingness difficult.

In this chapter, I am trying to broaden the scope from education to various domains, to present research or practice that can inspire our thinking of promisingness. In order to deepen our understanding of promisingness, I attempt to answer the following basic questions about promisingness: what does promisingness mean, why is it important, and how people make promisingness judgments. The first section will discuss the literal meaning of promisingness, by consulting respected dictionaries and thesauri. The second section will explore extensive presence of promisingness in various creative processes. The third and fourth sections will try to construct a descriptive model of promisingness, by mapping promisingness in the idea space with that in the nature and venture capital industry. Then, in order to get a deeper understanding of promisingness judgments for pedagogical and technological design, the fifth section will dive into psychology literature looking for psychological foundations of promisingness judgments. In the last section, I will try to situate the proposed research on promisingness into education and curriculum studies.

Bringing literature from several different fields (e.g. natural selection, venture capital, decision-making and judgments, and curriculum studies) was challenging, but the biggest breakthrough for my writing was to make those connections. Although my comps is far from making any breakthrough, the process of writing it echoes Dunbar’s (1995) finding on the important role analogies play in scientific inquiry.

The path ahead

From the official guide of doing PhD at OISE, the next step after completing comps is to write a thesis proposal and forming a committee. At the same time, I’m planning a study that will be carried out soon dealing with developmental issues around promisingness judgments.

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Appendix: Resources of LaTeX

1. Setup

• basic LaTeX setup – Windows: MiKTeX; also install some useful packages
• editors
  • TeXworks – default one comes with MiKTeX, with very simple UI and functions
  • Texmaker – I found this tool the most handy one!
  • TeXnicCenter – also powerful but with messier UI.
  • LyX – a WYGIWYM editor, closer the gap of user experience between LaTeX and Word

2. Citation & Bibliography

• Choosing citation package
• Read this page first
• biblatex – seems to be the best choice
• natbib – a package for customizable citation; I used this one for my comps.

Resources

• WikiBooks -Latex – Wonderful resources for learning LaTeX.
  Using LaTeX to Write a PhD Thesis – reading a book? a quick skim might lead you to useful tips.
  Using BibTeX: a short guide
  LaTeX course – this resource is fantastic!!
  Convert latex to rtf (to be used by MS Word): link from wikibook, latex2rtf.

Promisingness Judgments by Grade 3s

I started working on a tool called “Promising Ideas tool,” firstly named as “Big Ideas tool,” one and a half years ago. It is an add-on to Knowledge Forum (KF), a knowledge-building environment used in many kinds of settings but mostly in primary schools. The Promising Ideas tool is really simple in terms of functionality. It looks like a lite and KF-confined version of Diigo or SparTag.us, which are both used for people to tag important information on the Web and leave traces for later-comers for better browsing heuristics.

However, the Promising Ideas tool was designed on a distinctive philosophy and to meet very different needs. A phenomenon I learned from both talking with teachers and reading student notes in KF is that, young students may repeat each other’s ideas for weeks and weeks without moving their shared understanding forward. The reasons for this phenomenon are complicated. One possible reason might be that young students cannot intentionally process that much information produced by their peers at a time; as a result, although the community knowledge might grow fast at the beginning of their inquiry, it may enter a plateau phase when students are slowing picking up each other’s ideas and promising ideas become swamped in a number of repeating ones. It seems beneficial for a class to stop contributing new notes at some point and start reflecting on existing ideas in their community. At this point, two questions came to our minds: (1) Do/Can young students have a sense about the status or frontier of their community knowledge? (2) How can we make promising ideas in their discourse more visible and help them make more progress?

So we designed and developed the Promising Ideas tool, to help students make promisingness judgments on their peer discourse. As soon as we produced the first prototype in 2010, we started to test the tool in a primary school. A paper[in pdf] about how well can Grade 5/6 students do promisingness judgments without any intervention was presented at CSCL 2011 conference in Hong Kong. From April to June this year, we conducted the most recent pilot in a Grade 3 class. Before we started this pilot, teachers were very concerned whether Grade 3s can grasp the meaning of “promising;” they were also worried whether promisingness judgments would lead to a fear of sharing ideas among students, as I described in a previous post. Those concerns are all valid and valuable and we agreed we should pay special attention to the words we use in communicating the tool and the philosophy and intention behind this tool to kids.

The first session of this intervention was a 30 mins Knowledge Building (KB) talk about the meaning of “promising ideas,” led by a teacher who had years of experience in KB. This talk was fruitful. The class arrived at a shared understanding that promising ideas were ideas that “they wish to spend time on,” “are not necessarily correct at the beginning and may end up change a little bit in further inquiry,” and would “deepen their shared understanding.” This promisingness talk is crucial for successful use of this tool, in the sense that it helps students understand the reason and purpose of using this tool. The first part of the following video, 0:01 to 4:40, is a concise version of the promisingness talk.

The second session of this pilot was a tool tutorial session led by my colleague Monica Resendes. She walked through all important features of this tool in front of all students. Questions popped up from time to time among students and were quickly responded. This session lasted for around 15 mins. Students learned this tool pretty fast and moved to do actual promisingness judgments with this tool. (To get a better sense about how this tool works, watch the following video.)

In the third session, students worked in pairs and spent 20 mins on conducting promisingness judgments on their notes in a KF view they were working on. They identified 88 “promising” ideas (28 distinctive ones after collapsing overlapping ideas). All those ideas were piped into an idea space attached to the specific view they were working on. After idea tagging was done, we displayed the idea space to all students sitting in a circle (see the second part of the first video). The teacher and students went through the ideas and discussed on each idea at the top of the list. Finally, they collectively selected three “most promising” ideas and exported them to a new view for further inquiry in the following weeks.

We ran this intervention for two iterations in the same class when they were studying the same curriculum unit. Eventually, students produced three KF views that represent three different stages of their collective inquiry. Preliminary analysis shows the quality of their notes (measured with a “scientificness” scheme) and students’ conceptual understanding of that unit (measured by Latent Semantic Analysis between student notes and authoritative sources) has significantly improved across three views. I also analyzed data from a control group and did not find such significant improvements.

To summarize, the Promising Ideas tool is simple and can be used very differently. However, I think the most powerful way of using this tool is to facilitate promisingness judgments by students in their knowledge-building discourse. This use case or intention clearly distinguish the tool from some social bookmarking or highlighting tools, like Diigo and SparTag.us. While the meaning of “promisingness” and fundamental psychological mechanisms of doing promisingness judgments are still open to debate, it is important to engage students in a discussion about the meaning and importance of “promising ideas” before actually using this tool. Substantial work needs to be done in the future!