Sharp Brains: Brain Fitness and Cognitive Health News

Neuroplasticity, Brain Fitness and Cognitive Health News


Serious Games: Developing a Research Agenda for Educational Games and Simulations

(Edi­tor’s Note: the recent trade book Com­put­er Games and Instruc­tion brings togeth­er the lead­ing edge per­spec­tives of over a dozen sci­en­tists in the area of videogames and learn­ing, includ­ing a very insight­ful analy­sis ‑excerpt­ed below- by Har­vard’s Chris Dede. Please pay atten­tion to his thoughts on scal­a­bil­i­ty below, and enjoy!)

The research overview pro­vid­ed by Tobias, Fletch­er, and Dai (this vol­ume) is very help­ful in sum­ma­riz­ing stud­ies to date on var­i­ous dimen­sions of edu­ca­tion­al games and sim­u­la­tions. The next chal­lenge for the field is to move beyond iso­lat­ed research in which each group of inves­ti­ga­tors uses an idio­syn­crat­ic set of def­i­n­i­tions, con­cep­tu­al frame­works, and meth­ods. Instead, to make fur­ther progress, we as schol­ars should adopt com­mon research strate­gies and models—not only to ensure a high­er stan­dard of rig­or, but also to enable stud­ies that com­ple­ment each oth­er in what they explore.  As this book doc­u­ments, we now know enough as a research com­mu­ni­ty to under­take col­lec­tive schol­ar­ship that sub­di­vides the over­all task of under­stand­ing the strengths and lim­its of games and sim­u­la­tions for teach­ing and learn­ing. Fur­ther, through a con­tin­u­ous­ly evolv­ing research agen­da we can iden­ti­fy for fun­ders and oth­er stake­hold­ers an ongo­ing assess­ment of which types of stud­ies are most like­ly to yield valu­able insights, giv­en the cur­rent state of knowl­edge.

Research agen­das include both con­cep­tu­al frame­works for clas­si­fy­ing research and pre­scrip­tive state­ments about method­olog­i­cal rig­or. (For an exam­ple of a research agen­da out­side of gam­ing and sim­u­la­tion – in online pro­fes­sion­al devel­op­ment – see Dede, Ketel­hut, White­house, Bre­it, & McCloskey, 2009.) In addi­tion, research agen­das rest on tac­it assump­tions often unstat­ed, but in fact bet­ter made explic­it, as dis­cussed below. In this chap­ter, to inform a research agen­da for edu­ca­tion­al games and sim­u­la­tions, I offer thoughts about fun­da­men­tal assump­tions and a con­cep­tu­al frame­work that includes pre­scrip­tive heuris­tics about qual­i­ty. In doing so, my pur­pose is not to pro­pose what the research agen­da should be – that is a com­plex task best done by a group of peo­ple with com­ple­men­tary knowl­edge and per­spec­tives – but to start a dia­logue about what such an agen­da might include and how it might best be for­mu­lat­ed.

Fun­da­men­tal Assump­tions

My thoughts about a research agen­da for edu­ca­tion­al games and sim­u­la­tions are based on five fun­da­men­tal assump­tions. I take the trou­ble to artic­u­late these assump­tions because the beliefs and val­ues that under­lie a research agen­da often are the most impor­tant deci­sions made in its for­mu­la­tion. Mov­ing beyond “stealth” assump­tions about qual­i­ty to explic­it agree­ments and under­stand­ings is cen­tral to devel­op­ing schol­ar­ship that does not incor­po­rate many of the prob­lems that beset typ­i­cal edu­ca­tion­al research, such as irrel­e­vance and faulty meth­ods (Shavel­son & Towne, 2002). My five assump­tions posit that any research agen­da should focus on usable knowl­edge; col­lec­tive research; what works, when, for whom; more than a straight­for­ward com­par­i­son of the inno­va­tion to stan­dard prac­tice; and a focus on inno­va­tions that can be imple­ment­ed at scale. By “at scale,” I mean that that inno­va­tors can adapt the prod­ucts of research for effec­tive usage across a wide range of con­texts, many of which do not have an ide­al, full set of con­di­tions for suc­cess­ful imple­men­ta­tion.

Usable Knowl­edge

My first assump­tion that any research agen­da should focus on “usable knowl­edge”: insights gleaned from research that can be applied to inform prac­tice and pol­i­cy. I believe in defin­ing research agen­das in such a way that schol­ars not only build sophis­ti­cat­ed the­o­ries and applied under­stand­ings, but also dis­sem­i­nate this knowl­edge in a man­ner that helps stake­hold­ers access, inter­pret, and apply these insights. It is impor­tant to note that the process of cre­at­ing and shar­ing usable knowl­edge is best accom­plished by a com­mu­ni­ty of researchers, prac­ti­tion­ers, and pol­i­cy­mak­ers, as opposed to schol­ars devel­op­ing inde­pen­dent find­ings for oth­er stake­hold­ers to con­sume. As the chap­ters in this vol­ume doc­u­ment, in the case of gam­ing and sim­u­la­tion for learn­ing these stake­hold­ers include K‑12 teach­ers and admin­is­tra­tors, high­er edu­ca­tion, busi­ness and indus­try, med­i­cine and the health sci­ences, the mil­i­tary, and a vast unor­ga­nized group of peo­ple who desire bet­ter ways of infor­mal learn­ing. A com­mu­ni­ty of researchers, prac­ti­tion­ers, and pol­i­cy­mak­ers may also bet­ter accom­plish col­lec­tive the­o­ry build­ing than now occurs with frag­ment­ed cre­ation and dis­tri­b­u­tion of schol­ar­ly find­ings.

As Stokes describes in his book, Pasteur’s Quad­rant (1997), usable knowl­edge begins with per­sis­tent prob­lems in prac­tice and pol­i­cy, rather than with intel­lec­tu­al curios­i­ty. (This is not to dis­par­age pure­ly basic research, but to indi­cate its lim­its in imme­di­ate­ly pro­duc­ing usable knowl­edge.) In my expe­ri­ence, too often edu­ca­tion­al games and sim­u­la­tions are devel­oped because they are “cool” or “fun” — they are solu­tions look­ing for prob­lems (“build it and they will come.” If we are to gain the respect and col­lab­o­ra­tion of prac­ti­tion­ers and pol­i­cy­mak­ers, the major­i­ty of our research agen­da must focus on how games and sim­u­la­tions can aid in resolv­ing peren­ni­al edu­ca­tion­al prob­lems and issues, giv­ing pol­i­cy­mak­ers and prac­ti­tion­ers vital lever­age in address­ing trou­bling, wide­spread issues (Carl­son & Wilmot, 2006). Stokes makes a com­pelling case that usable knowl­edge is a pre­em­i­nent­ly valu­able form of research invest­ment, and Lage­mann (2002) makes the case that this strat­e­gy is very impor­tant for edu­ca­tion­al improve­ment.

Col­lec­tive Research

My sec­ond assump­tion is that, even though indi­vid­ual stud­ies of cre­ative “out­lier” approach­es is impor­tant, col­lec­tive research is vital for the fur­ther evo­lu­tion of our field. Ful­ly under­stand­ing a com­plex edu­ca­tion­al inter­ven­tion involv­ing gam­ing and sim­u­la­tion and effec­tive across a wide range of con­texts may require mul­ti­ple stud­ies along its var­i­ous dimen­sions, each schol­ar­ly endeav­or led by a group that spe­cial­izes in the meth­ods best suit­ed to answer­ing research ques­tions along that dimen­sion. Using such a dis­trib­uted research strat­e­gy among col­lab­o­rat­ing inves­ti­ga­tors, fun­ders could cre­ate port­fo­lios in which var­i­ous stud­ies cov­er dif­fer­ent por­tions of this sophis­ti­cat­ed schol­ar­ly ter­ri­to­ry, with com­ple­men­tary research out­comes enabling full cov­er­age and col­lec­tive the­o­ry-build­ing. Fur­ther, once the effi­ca­cy of an inter­ven­tion is deter­mined via explorato­ry research, a sin­gle large study with a com­plex treat­ment is of greater val­ue for research than mul­ti­ple small stud­ies of indi­vid­ual sim­ple inter­ven­tions, none of which has the sta­tis­ti­cal pow­er to deter­mine the nuanced inter­ac­tion effects described next. [For exam­ple, a researcher who wish­es to detect a small dif­fer­ence between two inde­pen­dent sam­ple means (e.g., treat­ment and con­trol) at a sig­nif­i­cance lev­el of 0.05, requires a sam­ple of size of 393 or more stu­dents in each group (Cohen, 1992).]

As an exam­ple of steps towards col­lec­tive research on a sin­gle inter­ven­tion in edu­ca­tion­al gam­ing and sim­u­la­tion, in their lit­er­a­ture review Tobias et al (this vol­ume) doc­u­ment sev­er­al stud­ies by dif­fer­ent inves­ti­ga­tors on the Space Fortress videogame. While a com­mon con­cep­tu­al frame­work did not nec­es­sar­i­ly guide these stud­ies, pre­sum­ably each set of inves­ti­ga­tors built to some extent on pri­or research. Beyond a com­mon con­cep­tu­al frame­work, devel­op­ing shared mean­ings for terms is cen­tral to tru­ly col­lec­tive schol­ar­ship. For exam­ple, “trans­fer” is a term that has a vari­ety of mean­ings. In their research review, Tobias et al (this vol­ume) group stud­ies of trans­fer, but which of those stud­ies used old­er def­i­n­i­tions of this term and which used emerg­ing for­mu­la­tions (Mestre, 2002; Schwartz, Sears, & Brans­ford, 2005)? The use of wikis is help­ful in teams of inves­ti­ga­tors evolv­ing a com­mon ter­mi­nol­o­gy and con­cep­tu­al frame­work, as evi­denced by the Pitts­burgh Sci­ence of Learn­ing Center’s wiki on the­o­ry devel­op­ment (

What Works

My third assump­tion is that a research agen­da should cen­ter on what works, when, for whom, going beyond whether or not some edu­ca­tion­al game or sim­u­la­tion “is effec­tive” in some uni­ver­sal man­ner (Koz­ma, (1994); Means, 2006). Learn­ing is a human activ­i­ty quite diverse in its man­i­fes­ta­tions from per­son to per­son (Dede, 2008). Con­sid­er three activ­i­ties in which all humans engage: sleep­ing, eat­ing, and bond­ing. One can arrange these on a con­tin­u­um from sim­ple to com­plex, with sleep­ing towards the sim­ple end of the con­tin­u­um, eat­ing in the mid­dle, and bond­ing on the com­plex side of this scale. Peo­ple sleep in rough­ly sim­i­lar ways;, but indi­vid­u­als like to eat dif­fer­ent foods and often seek out a range of quite dis­parate cuisines. Bond­ing as a human activ­i­ty is more com­plex still: Peo­ple bond to pets, to sports teams, to indi­vid­u­als of the same gen­der and of the oth­er gen­der; fos­ter­ing bond­ing and under­stand­ing its nature are incred­i­bly com­pli­cat­ed activ­i­ties. Edu­ca­tion­al research strong­ly sug­gests that indi­vid­ual learn­ing is as diverse and as com­plex as bond­ing, or cer­tain­ly as eat­ing. Yet the­o­ries of learn­ing and philoso­phies about how to use inter­ac­tive media for edu­ca­tion tend to treat learn­ing like sleep­ing, as an activ­i­ty rel­a­tive­ly invari­ant across peo­ple, sub­ject areas, and edu­ca­tion­al objec­tives. That is, behav­ior­ists, cog­ni­tivists, con­struc­tivists, and those who espouse “sit­u­at­ed learn­ing” all argue that, well imple­ment­ed, their approach to instruc­tion works for all learn­ers (Dede, 2008).

As a con­se­quence, many edu­ca­tion­al design­ers and schol­ars seek the sin­gle best medi­um for learn­ing, as if such a uni­ver­sal tool could exist. For exam­ple, a uni­ver­sal method for devel­op­ing instruc­tion is the goal of “instruc­tion­al sys­tems design” (Dick & Carey, 1996) Sim­i­lar to every oth­er form of edu­ca­tion­al tech­nol­o­gy, some see gam­ing and sim­u­la­tion as uni­ver­sal­ly opti­mal, a “sil­ver bul­let” for education’s woes (Salen, 2008). As Lar­ry Cuban doc­u­ments in his book, Over­sold and Under­used (2001), in suc­ces­sive gen­er­a­tions pun­dits have espoused as “mag­i­cal” media the radio, the tele­vi­sion, the com­put­er, the Inter­net, and now lap­tops, gam­ing, blog­ging, and pod­cast­ing (to name just a few). The weak­ness in this posi­tion is the tac­it assump­tion, per­va­sive in most dis­cus­sions of edu­ca­tion­al tech­nol­o­gy research, that instruc­tion­al media are “one size fits all” rather than enabling an ecol­o­gy of ped­a­go­gies to empow­er the many dif­fer­ent ways peo­ple learn.

No learn­ing medi­um is a tech­nol­o­gy like fire, where one only has to stand near it to get a ben­e­fit from it. Knowl­edge does not intrin­si­cal­ly radi­ate from edu­ca­tion­al games and sim­u­la­tions, infus­ing stu­dents with learn­ing as fires infuse their onlook­ers with heat. How­ev­er, var­i­ous the­o­ret­i­cal per­spec­tives (e.g., cog­ni­tive sci­ence, social con­struc­tivism, instruc­tion­al sys­tems design) can pro­vide insights on how to con­fig­ure these inter­ac­tive media to aid var­i­ous aspects of learn­ing, such as visu­al rep­re­sen­ta­tion, stu­dent engage­ment, and the col­lec­tion of assess­ment data. Deter­min­ing whether and how each instruc­tion­al medi­um can best enhance some aspect of a par­tic­u­lar ped­a­gogy is as sen­si­ble instru­men­tal­ly as devel­op­ing a range of tools (e.g., screw­driv­er, ham­mer, saw, wrench) that aid a carpenter’s abil­i­ty to con­struct arti­facts.

Fur­ther, numer­ous stud­ies doc­u­ment that no opti­mal ped­a­gogy – or instruc­tion­al medi­um – is effec­tive across every sub­ject mat­ter (Shul­man, 1986; Bech­er, 1987; Lam­pert, 2001). As one exam­ple of research on sub­ject-spe­cif­ic ped­a­gogy, David Garvin (2003) doc­u­ments that the Har­vard Law School, Busi­ness School, and Med­ical School have sep­a­rate­ly strong­ly influ­enced how their par­tic­u­lar pro­fes­sion is taught, each by espous­ing and mod­el­ing sophis­ti­cat­ed “case-method” instruc­tion. Garvin’s find­ings show that what each of these fields means by case-method ped­a­gogy is quite dif­fer­ent and that those dis­sim­i­lar­i­ties are shaped by the par­tic­u­lar con­tent and skills pro­fes­sion­als in that type of prac­tice must mas­ter. Thus, the nature of the con­tent and skills to be learned shape the type of instruc­tion to use, just as the devel­op­men­tal lev­el of the stu­dent influ­ences what teach­ing meth­ods will work well. No edu­ca­tion­al approach, includ­ing gam­ing and sim­u­la­tion, is uni­ver­sal­ly effec­tive; and the best way to invest in learn­ing tech­nolo­gies is a research agen­da that includes the effects of the cur­ricu­lum, the con­text, and stu­dents’ and teach­ers’ char­ac­ter­is­tics in deter­min­ing which aspects of edu­ca­tion­al games and sim­u­la­tions work when, for whom, under what con­di­tions nec­es­sary for suc­cess.

Treat­ment Effects

My fourth assump­tion is that, even though sum­ma­tive eval­u­a­tions are impor­tant, the schol­ar­ly focus in the research agen­da should expand well beyond the “is there a sig­nif­i­cant dif­fer­ence in out­come between this inter­ven­tion and stan­dard prac­tice?” stud­ies that com­prise many of the pub­li­ca­tions in the Tobias et al review (this vol­ume). A vast lit­er­a­ture exists doc­u­ment­ing the “no sig­nif­i­cant dif­fer­ence” out­comes char­ac­ter­is­tic of many such stud­ies (Rus­sell, 1999). Beyond flaws in research design and ana­lyt­ic meth­ods, fre­quent rea­sons for lack of a sig­nif­i­cant treat­ment effect include an inter­ven­tion too short in dura­tion to expect a sub­stan­tial impact or a sam­ple so small that, for lack of sta­tis­ti­cal pow­er, even a large effect size could not be detect­ed. The use of mea­sures inad­e­quate to detect the sig­nif­i­cant dif­fer­ences that are occur­ring is anoth­er com­mon prob­lem; for exam­ple, paper-and-pen­cil item-based tests are flawed in their mea­sure­ment of sophis­ti­cat­ed think­ing skills, such as sci­en­tif­ic inquiry (Resnick & Resnick, 1992; Quell­malz & Haer­tel, 2004; Nation­al Research Coun­cil, 2006; Clarke & Dede, in press). Fur­ther, even when all these prob­lems are over­come, often the pop­u­la­tion in the study is nar­row, the teacher char­ac­ter­is­tics are opti­mal, or the con­text is unrep­re­sen­ta­tive; each of these gen­er­ates major threats to gen­er­al­iz­abil­i­ty.

In fact, many of these stud­ies are sum­ma­tive eval­u­a­tions mas­querad­ing as research. There is noth­ing wrong with devel­op­ing an inter­ven­tion and con­duct­ing a sum­ma­tive eval­u­a­tion of its over­all impact under typ­i­cal con­di­tions and with rep­re­sen­ta­tive pop­u­la­tions for its poten­tial use. Design heuris­tics from eval­u­a­tions of suc­cess­ful inno­va­tions are often use­ful (Con­nol­ly, Stans­field, & Hainey, 2009). Fur­ther, eval­u­at­ing the effi­ca­cy of a treat­ment before con­duct­ing elab­o­rate research stud­ies of its rel­a­tive effec­tive­ness across mul­ti­ple types of con­texts is impor­tant in mak­ing wise allo­ca­tions of resources. How­ev­er, eval­u­a­tion stud­ies are a poor place to stop in research on an inno­va­tion and should be only a small part of a research agen­da, not the pre­pon­der­ance of work, as they typ­i­cal­ly do not con­tribute much to the­o­ry and do not pro­vide nuanced under­stand­ings of what works, when, for whom, and under what con­di­tions.


My fifth assump­tion is that a research agen­da for edu­ca­tion­al gam­ing and sim­u­la­tion should priv­i­lege stud­ies of inter­ven­tions that can be imple­ment­ed at scale. Scale is not pure­ly a mat­ter of eco­nom­ic com­mon sense, such as not spend­ing large amounts of resources on stu­dents in each class­room hav­ing access to a game devel­op­ment com­pa­ny to build what they design, or sim­u­la­tions that involve high ratios of instruc­tors to learn­ers. Research has doc­u­ment­ed that in edu­ca­tion, unlike oth­er sec­tors of soci­ety, the scal­ing of suc­cess­ful instruc­tion­al pro­grams from a few set­tings to wide­spread use across a range of con­texts is very dif­fi­cult even for inno­va­tions that are eco­nom­i­cal­ly and logis­ti­cal­ly prac­ti­cal (Dede, Honan, & Peters, 2005).

In fact, research find­ings typ­i­cal­ly show sub­stan­tial influ­ence of con­tex­tu­al vari­ables (e.g., the teacher’s con­tent prepa­ra­tion, stu­dents’ self-effi­ca­cy, pri­or aca­d­e­m­ic achieve­ment) in shap­ing the desir­abil­i­ty, prac­ti­cal­i­ty, and effec­tive­ness of edu­ca­tion­al inter­ven­tions (Barab & Luehmann, 2003; Schnei­der & McDon­ald, 2007). There­fore, achiev­ing scale in edu­ca­tion requires designs that can flex­i­bly adapt to effec­tive use in a wide vari­ety of con­texts across a spec­trum of learn­ers and teach­ers. Clarke and Dede (2009) doc­u­ment the appli­ca­tion of a five-dimen­sion­al frame­work for scal­ing up to the imple­men­ta­tion of the Riv­er City mul­ti-user vir­tu­al envi­ron­ment for mid­dle school sci­ence:

  • Depth: eval­u­a­tion and design-based research to under­stand and enhance caus­es of effec­tive­ness
  • Sus­tain­abil­i­ty: “robust design” to enable adapt­ing to inhos­pitable con­texts
  • Spread: mod­i­fy­ing to retain effec­tive­ness while reduc­ing resources and exper­tise required
  • Shift: mov­ing beyond “brand” to sup­port users as co-eval­u­a­tors, co-design­ers, and co-scalers
  • Evo­lu­tion: learn­ing from users’ adap­ta­tions to rethink the innovation’s design mod­el

This is not to argue that research agen­das should not include stud­ies of unscal­able inter­ven­tions – such research can aid with design and help evolve the­o­ry – but I believe that the bulk of a research agen­da, to pro­duce usable knowl­edge, should focus on inno­va­tions that can scale. As the research review by Tobias et al (this vol­ume) doc­u­ments, edu­ca­tion­al games and sim­u­la­tions in gen­er­al offer desir­able affor­dances for imple­men­ta­tion at scale.

I offer these assump­tions not as “truths,” but as propo­si­tions to be debat­ed in the course of for­mu­lat­ing a research agen­da for edu­ca­tion­al gam­ing and sim­u­la­tion. Oth­ers may wish to mod­i­fy assump­tions, to add assump­tions to this list, or even to argue that a research agen­da should not make any assump­tions about what con­sti­tutes qual­i­ty. My point is that any attempt to devel­op a research agen­da should make its under­ly­ing beliefs and val­ues explic­it, because these are cen­tral to deter­min­ing its con­cep­tu­al frame­work.

Chris Dede is the Wirth Pro­fes­sor in Learn­ing Tech­nolo­gies at Har­vard Grad­u­ate School of Edu­ca­tion. The excerpt above is part of his chap­ter Devel­op­ing a Research Agen­da for Edu­ca­tion­al Games and Sim­u­la­tions in the book Com­put­er Games and Instruc­tion, pub­lished in 2011 by Infor­ma­tion Age Pub­lish­ing.

–> To Learn More and Order Book via pub­lish­er (offers dis­counts): click on Com­put­er Games and Instruc­tion

–> To Learn More and Order Book via click on Com­put­er Games and Instruc­tion

Book Descrip­tion: There is intense inter­est in com­put­er games. A total of 65 per­cent of all Amer­i­can house­holds play com­put­er games, and sales of such games increased 22.9 per­cent last year. The aver­age amount of game play­ing time was found to be 13.2 hours per week. The pop­u­lar­i­ty and mar­ket suc­cess of games is evi­dent from both the increased earn­ings from games, over $7 Bil­lion in 2005, and from the fact that over 200 aca­d­e­m­ic insti­tu­tions world­wide now offer game relat­ed pro­grams of study.

In view of the intense inter­est in com­put­er games edu­ca­tors and train­ers, in busi­ness, indus­try, the gov­ern­ment, and the mil­i­tary would like to use com­put­er games to improve the deliv­ery of instruc­tion. Com­put­er Games and Instruc­tion is intend­ed for these edu­ca­tors and train­ers. It reviews the research evi­dence sup­port­ing use of com­put­er games, for instruc­tion, and also reviews the his­to­ry of games in gen­er­al, in edu­ca­tion, and by the mil­i­tary. In addi­tion chap­ters exam­ine gen­der dif­fer­ences in game use, and the impli­ca­tions of games for use by low­er socio-eco­nom­ic stu­dents, for stu­dents’ read­ing, and for con­tem­po­rary the­o­ries of instruc­tion. Final­ly, well known schol­ars of games will respond to the evi­dence reviewed.

Pref­ace. SECTION I: INTRODUCTION TO COMPUTER GAMES. Intro­duc­tion, Sig­mund Tobias and J. D. Fletch­er. Search­ing For the Fun in Learn­ing: A His­tor­i­cal Per­spec­tive on the Evo­lu­tion of Edu­ca­tion­al Video Games, Alex Games and Kurt D. Squire. Using Video Games as Edu­ca­tion­al Tools in Health­care, Janis A. Can­non-Bow­ersClint Bow­ers, and Kate­lyn Proc­ci. After the Rev­o­lu­tion: Game-Informed Train­ing in the U.S. Mil­i­tary, Ralph Ernest Chatham. Mul­ti-User Games and Learn­ing: A Review of the Research, Jonathon Richterand Daniel Liv­ing­stoneSECTION II: REVIEW OF THE LITERATURE AND REACTIONS. Review of Research on Com­put­er Games, Sig­mund Tobias, J. D. Fletch­er, David Yun Dai, and Alexan­der P. Wind. Reflec­tions on Empir­i­cal Evi­dence on Games and Learn­ing, James Paul Gee. Devel­op­ing a Research Agen­da for Edu­ca­tion­al Games and Sim­u­la­tions, Chris Dede. Com­ments on Research Com­par­ing Games to Oth­er Instruc­tion­al Meth­ods,Marc Pren­skySECTION III: COMPUTER GAME ISSUES. Mul­ti­me­dia Learn­ing and Games, Richard E. May­er. Action Game Play as a Tool to Enhance Per­cep­tion, Atten­tion and Cog­ni­tion, Ash­ley F. Ander­son and Daphne Bave­li­er. Devel­op­ing an Elec­tron­ic Game for Vocab­u­lary Learn­ing: A Case Study, Michael L. Kamil and Cheryl Taitague. Instruc­tion­al Sup­port in Games, Hen­ny Leemkuil and Ton de Jong. Impli­ca­tions of Con­struc­tivism for the Design and Use of Seri­ous Games, Jamie R. KirkleyThomas M. DuffySon­ny E. Kirkley, and Deb­o­rah L. H. Kre­mer. Impli­ca­tions of Game Use for Explic­it Instruc­tion, Putai Jin and Renae Low. Cost Analy­sis in Assess­ing Games for Learn­ing, J. D. Fletch­er. Using Com­put­er Games to Teach Adult Learn­ers Prob­lem Solv­ing, Joan (Yuan-Chung) Lang and Harold F. O’Neil. Gen­der and Gam­ing, Elis­a­beth R. Hayes. Com­put­er Games and Oppor­tu­ni­ty to Learn: Impli­ca­tions for Teach­ing Stu­dents from Low Socioe­co­nom­ic Back­grounds,David Yun Dai and Alexan­der P. WindSECTION IV: EVALUATION AND SUMMING UP. Stealth Assess­ment in Com­put­er-Based Games to Sup­port Learn­ing, Valerie J. Shute. Com­put­er Games, Present and Future, Sig­mund Tobias and J. D. Fletch­er.

Leave a Reply...

Loading Facebook Comments ...

Leave a Reply

Categories: Education & Lifelong Learning

Tags: , , , , , , , , , , , , ,

Search in our archives

About SharpBrains

As seen in The New York Times, The Wall Street Journal, BBC News, CNN, Reuters,  SharpBrains is an independent market research firm tracking how brain science can improve our health and our lives.

Follow us and Engage via…

RSS Feed

Watch All Recordings Now (40+ Speakers, 12+ Hours)