![[BACK]](/cvsweb/icons/back.gif){kind=icon}
Return to ngradesys CVS log ![[TXT]](/cvsweb/icons/text.gif){kind=icon}
![[DIR]](/cvsweb/icons/dir.gif){kind=icon}
Up to [CSRG BSD Unix] / 43BSD / contrib / apl / lib|
Return to ngradesys CVS log | Up to [CSRG BSD Unix] / 43BSD / contrib / apl / lib |
1.1 ! root 1: n� �*.KB�& H id n { id nam; sname; sum; n; v; index ! 2: nam { ,nam ! 3: sname { ((1YRname),Rnam)Yname ! 4: sum { +/v{^/sname=(Rsname)Rnam ! 5: n { 0 ! 6: } (0=sum)/nfind ! 7: } (1#sum)/mfind ! 8: n { vI1 ! 9: } (^/((1URname)Ynam)=name[n;])/0 ! 10: L { ' ',' ','full name: ',name[n;] ! 11: }0 ! 12: nfind: L { 'name not found' ! 13: } 0 ! 14: mfind: L { 'ambiguous names' ! 15: sname { v/[1]name ! 16: } (0=^/^/((Rsname)Rsname[1;])=sname)/dfind ! 17: L { ' ',(NJ(((1YRsname),1)R index { v/I1YRname)),':',' ',sname ! 18: getidx: n { 'Enter index of desired name: ' getnum '-' ! 19: } (0=V/n=index,`1)/error ! 20: } 0 ! 21: dfind: L { ' ',' ',sname ! 22: } 0 ! 23: error: L { '*** illegal index, try again ***' ! 24: } getidx ! 25: dstat dstat x; z ! 26: z { stat x ! 27: ' ' ! 28: ' ' ! 29: 'sample size ';z[1] ! 30: 'maximum ';z[2] ! 31: 'minimum ';z[3] ! 32: 'range ';z[4] ! 33: 'mean ';z[5] ! 34: 'variance ';z[6] ! 35: 'standard deviation ';z[7] ! 36: 'mean deviation ';z[8] ! 37: 'median ';z[9] ! 38: summary summary; i ! 39: L{' ' ! 40: L{' score summary' ! 41: L{' ' ! 42: L { ((4+1URname),(`1+1YRlist)R4)fhead list ! 43: L { name,(5 0 NJ ((1YRname),1)RI1YRname),4 0NJ score ! 44: L { 3 1 R ' ' ! 45: drop drop ;nam;idx;mask ! 46: L { 'Enter names (one per line) end with an empty line.' ! 47: getname: L'{'name: ' ! 48: } (0=Rnam{L')/0 ! 49: idx { id nam ! 50: } (0=idx)/getname ! 51: L { ((4 + 1URname),(`1+1YRlist)R4)fhead list ! 52: L { name[idx;],(5 0NJidx),4 0NJscore[idx;] ! 53: L'{'ok? (y/n): ' ! 54: }('y'#'L)/nogo ! 55: mask { (1YRname)R1 ! 56: mask[idx]{0 ! 57: name {mask/[1]name ! 58: } ((`1YRssn)=0)/nossn ! 59: ssn { mask/[1]ssn ! 60: } ssnd ! 61: nossn: ssn { ((1YRname),0)R'' ! 62: ssnd: } ((`1YRscore)=0)/noscore ! 63: score { mask/[1]score ! 64: } done ! 65: noscore: score { ((1YRname),0)R0 ! 66: done: L{'drop completed' ! 67: } getname ! 68: nogo: L { 'nobody dropped' ! 69: } getname ! 70: stat z{stat x;r;max;min;mean;var;sd;md;med;mode;v;m;n ! 71: n{Rx ! 72: r{(max{x[n])-min{(x{x[|Hx])[1] ! 73: sd{(var{(+/(|x-nRmean{(+/x)%n)*2)%n-1)*0.5 ! 74: md{(+/|x-nRmean)%n ! 75: med{0.5X+/x[(Sn%2),1+Dn%2] ! 76: z{n,max,min,r,mean,var,sd,md,med ! 77: & statname ` H number maximum minimum range mean variancestd dev mean devmedian newstat statscore { newstat data ;i ! 78: statscore { (9,1URdata)R0 ! 79: i {1 ! 80: next: statscore[;i]{stat data[;i] ! 81: } ((1URstatscore) & i{i+1)/next ! 82: statscore[1;]{+/[1]0#data ! 83: statsummary statsummary ! 84: L{' ' ! 85: L{' statistics summary' ! 86: L{' ' ! 87: L { (11,(`2+1YRlist)R4) fhead 1 0Ulist ! 88: L { statname,4 0 NJ D newstat score ! 89: L { 3 1 R ' ' ! 90: newscore newscore ;pos;i;mask;listn;p;tp;it ! 91: L { 'current types are:' ! 92: L { type ! 93: L' { 'type ? ' ! 94: p { type find it { L' ! 95: }(V/p)/oktype ! 96: L { 'type not found' ! 97: }0 ! 98: oktype: tp { p/IRp ! 99: pos { (tp<scoretype)I1 ! 100: mask { 1,(Rscoretype)R1 ! 101: mask[pos] { 0 ! 102: score { mask\score ! 103: scoremax { mask\scoremax ! 104: list { (1,mask)\[1]list ! 105: scoretype { mask\scoretype ! 106: scoretype[pos] { tp ! 107: scoremax[pos] { 'scoremax ? ' getnum '.' ! 108: listn { 2 0NJ+/,scoretype=scoretype[pos] ! 109: list[pos+1;] { (1URlist)Yit,listn ! 110: CJ now input new scores ! 111: i { 1 ! 112: next: score[i;pos] { (name[i;],': ') getnum '-.' ! 113: }((1YRname)&i { i+1)/next ! 114: L { 'new score complete' ! 115: newname newname; nname; nssn ! 116: L { 'enter names (one per line) end with an empty line.' ! 117: getname: L' { 'name: ' ! 118: }(0=Rnname{L')/done ! 119: name { name addrow nname ! 120: nssn { '' ! 121: } (~askedssn)/addssn ! 122: L' { 'ssn : ' ! 123: nssn { L' ! 124: addssn: ssn { ssn addrow nssn ! 125: } getname ! 126: done: score { ((1YRname),`1YRscore)Yscore ! 127: L { 'newname complete' ! 128: analysis analysis; i; mk; n ! 129: n { 1YRname ! 130: means{ (n,1+1YRtype)R0 ! 131: i{1 ! 132: next: mk{ scoretype=i ! 133: }done XI 0=V/,mk ! 134: means[;i]{ 100X(+/mk/score) % +/mk/scoremax ! 135: done: }((1YRtype) >= i{ i+1 )/next ! 136: means[;i]{ (+/(means[;Ii-1]X(n,Rweight)Rweight)) %+/weight ! 137: ! 138: histgm title histgm data ; i;h;m50 ! 139: m50 { 51 > S/data ! 140: data { S data%2-m50 ! 141: h {51R0 ! 142: i{0 ! 143: loop: h[i+1] { +/data =i ! 144: }loop XI 50 & i {i+1 ! 145: '' ! 146: ' histogram of ',title ! 147: '' ! 148: i { S/h ! 149: loop2: ' *'[1+ h&i] ! 150: }loop2 XI 0< i{i-1 ! 151: '|----|----|----|----|----|----|----|----|----|----|' ! 152: }m50/low ! 153: '0 10 20 30 40 50 60 70 80 90 100' ! 154: }0 ! 155: low:'0 5 10 15 20 25 30 35 40 45 50' ! 156: ! 157: find p { list find item ! 158: p { ^/list=(Rlist)R(1URlist)Y item,' ' ! 159: ! 160: printstat printstat colnam;data ! 161: }(0=Rdata{ getcol colnam)/0 ! 162: dstat data ! 163: printstat0 printstat0 colnam;data ! 164: }(0=Rdata{ getcol colnam)/0 ! 165: data { (0#data)/data ! 166: dstat data ! 167: getcol data { getcol colnam;p ! 168: p { 1U list find colnam ! 169: }(~V/p)/snf ! 170: data { ,p/score ! 171: }0 ! 172: snf:p { tlist find colnam ! 173: }(~V/p)/nnf ! 174: data {,p/means ! 175: }0 ! 176: nnf:'column name ',colnam,' not found' ! 177: data { 0R0 ! 178: ! 179: printhist printhist colnam;data ! 180: }(0=Rdata{ getcol colnam)/0 ! 181: colnam histgm data ! 182: printhist0 printhist0 colnam;data ! 183: }(0=Rdata{ getcol colnam)/0 ! 184: data { (0#data)/data ! 185: colnam histgm data ! 186: setup setup ;i;tt;nname;wt ! 187: } askedsetup/exit ! 188: L{ 'enter the score types (one per line) end with an empty line.' ! 189: type { 0 0 R' ' ! 190: gettype: L' { 'type: ' ! 191: }next XI 0=Rtt { 'L ! 192: type { type addrow tt ! 193: }gettype ! 194: next: L{ 'enter the weights for each type' ! 195: list{(1,Rlist)Rlist { (6S3+1URtype)Y'number' ! 196: i { 1 ! 197: weight { (1YRtype)R0 ! 198: nextweight: weight[i] { (type[i;],' = ') getnum '.' ! 199: }nextweight XI (1YRtype)&i { i+1 ! 200: name { 0 0 R' ' ! 201: L { 'enter names (one per line) end with an empty line.' ! 202: getname: L' { 'name: ' ! 203: } next2 XI 0=Rnname { L' ! 204: name { name addrow nname ! 205: } getname ! 206: next2: score { ((1YRname),0)R0 ! 207: scoretype { 0R0 ! 208: scoremax { 0R0 ! 209: ssn { 0 0R0 ! 210: askedssn { 0 ! 211: subs { 0 ! 212: L { 'setup complete' ! 213: askedsetup { 1 ! 214: } 0 ! 215: exit: L { 'setup previously completed' ! 216: change change;nam;stype;idx;ip;ic ! 217: L' { 'Enter the item to be changed: ' ! 218: stype { L',' ' ! 219: ic { list find stype ! 220: newtype { nwscore ! 221: }(0#V/ic)/ok ! 222: newtype { nwname ! 223: }(^/stype=(Rstype)Y'name ')/ok ! 224: newtype { nwssn ! 225: }(^/stype=(Rstype)Y'ssn ')/ok ! 226: L{'type not found' ! 227: }0 ! 228: ok:col { `1 + ic I 1 ! 229: L { 'Enter names (one per line) end with an empty line.' ! 230: getname: L' { 'name: ' ! 231: nam { L' ! 232: } (0=Rnam)/0 ! 233: idx { id nam ! 234: }(0=idx)/getname ! 235: } newtype ! 236: nwscore: L{name[idx;],' - ',list[1+col;],'old score ',Bscore[idx;col] ! 237: }(1#R ip{'new score: ' getnum '-.')/nc ! 238: }(0>ip)/nc ! 239: score[idx;col]{ip ! 240: L { 'score change complete' ! 241: }getname ! 242: nwname: L' { 'new name: ' ! 243: } (0=Rnam{L')/nc ! 244: name { name addrow nam ! 245: name[idx;] { name[1YRname;] ! 246: name { name[I`1+1YRname;] ! 247: L { 'name change complete' ! 248: }getname ! 249: nwssn: L { name[idx;],' - old ssn = ',ssn[idx;] ! 250: L' { 'new ssn: ' ! 251: } (0=Rsin{L')/nc ! 252: ssn[idx;] { 9Y (~sinE' -')/sin ! 253: L { 'ssn change complete' ! 254: }getname ! 255: nc: L{'no change' ! 256: }getname ! 257: ! 258: analstats analstats ! 259: L {'' ! 260: L {' analysis statistics' ! 261: L { '' ! 262: tlist { type,[1](1URtype)Y'total' ! 263: L{(11,(`1+1YRtlist)R4) fhead tlist ! 264: L { statname,4 0 NJD newstat means ! 265: L { 3 1 R ' ' ! 266: ! 267: analprint analprint; rank ;tlist ! 268: tlist { type,[1](1URtype)Y'total' ! 269: rank { |V ,means[;1URmeans] ! 270: L { ' ' ! 271: L { ' ranking of score totals' ! 272: L { ' ' ! 273: L { ((3+1URname),(1YRtlist)R5) fhead ((1,1URtlist)R'rank '),[1]tlist ! 274: L { name[rank;],(4 0NJ(((Rrank),1)RIRrank)),5 1NJmeans[rank;] ! 275: L { 3 1 R' ' ! 276: anal466 anal466; i; mk; ds; dt; dm; scoret; low ! 277: CJ assume types hw, test and final ! 278: means{ ((Rnumber),1+1YRtype)R0 ! 279: scoret { tscore score ! 280: i{1 ! 281: CJ for each student ! 282: loop: ds{ scoret[i;] ! 283: CJ homework ! 284: mk { scoretype=1 ! 285: dt { mk/ds ! 286: dm { mk/scoremax ! 287: CJ drop lowest homework ! 288: low { (dtID/dt)#IRdt ! 289: means[i;1]{(+/low/dtXdm)%+/low/dm ! 290: CJ tests (skip if none) ! 291: }l1 XI ~V/mk{scoretype=2 ! 292: dt { mk/ds ! 293: dm { mk/scoremax ! 294: low { dtID/dt ! 295: dt[low] { means[i;3] { S/dt[low],(scoretype=3)/ds ! 296: means[i;2]{(+/dtXdm)%+/dm ! 297: }loop XI (1YRmeans)&i{i+1 ! 298: l1: means[;4]{ (+/(means[;1 2 3]X((Rnumber),Rweight)Rweight)) %+/weight ! 299: analprint ! 300: tscore res { tscore data; i; dt; mn; sd ! 301: CJ tscore computes the tscores for a vector ! 302: CJ or matrix <data>. ! 303: res { (Rdata)R0 ! 304: i{1 ! 305: }loop XI 2=RRdata ! 306: dt { data ! 307: }l1 ! 308: loop: dt { data[;i] ! 309: l1: mn { (+/dt)%Rdt ! 310: sd { ((+/(dt-mn)*2)%`1+Rdt)*.5 ! 311: dt { 50 + 10 X (dt-mn)%sd ! 312: }end XI 1=RRdata ! 313: res[;i]{dt ! 314: }loop X (1URres)&i{i+1 ! 315: end: res{dt ! 316: tsummary tsummary ! 317: L{' ' ! 318: L{' t-score summary' ! 319: L{' ' ! 320: L { ((4+1URname),(`1+1YRlist)R4)fhead list ! 321: L { name,(5 0 NJ numc),4 0NJ D tscore score ! 322: ! 323: fhead r { f fhead list ! 324: r{((I+/f)E+\f)\O\ list ! 325: ! 326: addrow r { mx addrow row; l ! 327: CJ add a new row to a matrix ! 328: l { (Rrow)S1URmx ! 329: r { (((1YRmx),l)Ymx),[1]lYrow ! 330: lgrade lgrade ! 331: CJ compute lettergrade ! 332: L{'enter cutoff values' ! 333: L{' a b c d' ! 334: cut { L ! 335: letter { ((1YRmeans),1)R'fdcba'[1++/means[;1URmeans]J.>cut] ! 336: alphabetize alphabetize;cols;order;alphabet ! 337: CJ alphabetize the name list and reorder the scores ! 338: alphabet { ' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz' ! 339: cols { I 10 D Rname[1;] ! 340: order { |H +/ (alphabetIname[;cols])X((Rname[;1]),10 D Rname[1;])RO|((Ralphabet)*cols) ! 341: name { name[order;] ! 342: score { score[order;] ! 343: ssn { ssn[order;] ! 344: askssn askssn ;i;sin ! 345: } askedssn/enter ! 346: CJ ask for a ssn for each person in the name list ! 347: i { 1 ! 348: ssn { ((Rname[;1]),9)R ' ' ! 349: loop: L' { name[i;],': ' ! 350: sin { L', ' ' ! 351: ssn[i;] { 9Y (~ sin E ' -')/sin ! 352: } ((1YRssn) & i { i+1)/loop ! 353: L { 'ssn entry complete' ! 354: askedssn { 1 ! 355: } 0 ! 356: enter: L { 'ssn previously completed.' ! 357: analgrin analgrin;i;means;mk;ds;dt;dm;low;n;w ! 358: n { 1YRname ! 359: w { Rweight ! 360: scoret { tscore score ! 361: rank { means { (n,1+1YRtype)R0 ! 362: i { 1 ! 363: } subs/loop2 ! 364: loop1: }enter1 XI ~V/mk{scoretype=i ! 365: dm { mk/scoremax ! 366: means[;i] { \O(+/(mk/scoret)X((n,Rdm)Rdm))%+/dm ! 367: rank[;i] { \O|H|Gmeans[;i] ! 368: enter1: }loop1 XI (w&i{i+1) ! 369: rank[;1+w] { \O|H|G(+/means[;1 2 3]X(n,w)Rweight)%+/weight ! 370: } 0 ! 371: loop2: ds { scoret[i;] ! 372: mk { scoretype=1 ! 373: dt { mk/ds ! 374: dm { mk/scoremax ! 375: low { (dtID/dt)#IRdt ! 376: means[i;1]{(+/low/dtXdm)%+/low/dm ! 377: }enter2 XI ~V/mk{scoretype=2 ! 378: dt { mk/ds ! 379: dm { mk/scoremax ! 380: low { dtID/dt ! 381: dt[low] { means[i;3] { S/dt[low],(scoretype=3)/ds ! 382: means[i;2] { (+/dtXdm)%+/dm ! 383: }loop2 XI (1YRmeans)&i{i+1 ! 384: enter2: means[;4] { (+/(means[;1 2 3]X(n,w)Rweight)%+/weight ! 385: rank { |H[1]|G[1]means ! 386: grin grin;ss;n ! 387: ss { 'L,' ' ! 388: ss { 9Y(ss#'-')/ss ! 389: n { (^/ssn=(Rssn)Rss)I1 ! 390: } (n$1YRssn)/ok ! 391: L { 'invalid student id: ',ss ! 392: } 0 ! 393: ok: L { (13,(`2+1YRlist)R4) fhead 1 0 U list ! 394: 'max score', 4 0 NJ scoremax ! 395: 'raw score', 4 0 NJ score[n;] ! 396: ' t score', 4 0 NJ scoret[n;] ! 397: '' ! 398: } (subs)/enter ! 399: 'Based on T scores but without using the substitution' ! 400: ' algorithm (i.e. - no scores have been droped or replaced yet)' ! 401: enter: 'The total number of students in class is ',(3 0NJ `1URssn),'.' ! 402: '' ! 403: ' homework test final total' ! 404: 'rank ', 8 0NJ rank[n;] ! 405: & Llx K & 7 7 'type ''describe'' for a description of this workspace' ssnprint ssnprint function ;name ! 406: name { ssn ! 407: BJ function ! 408: & askedsetup & & ! 409: getnum num { prompt getnum let;str ! 410: again: L' { prompt ! 411: str { L' ! 412: } (1<+/strE'.')/error ! 413: } (1<+/strE'-')/error ! 414: }goodnum XI ^/strElet,'0123456789' ! 415: error: L { '*** illegal number, try again. ***' ! 416: }again ! 417: goodnum: num { BJ str ! 418: } (1#R,num)/error ! 419: analize analize ;data;mean;didanal;tlist ! 420: didanal { 0 ! 421: } ((RBN27)>1)/cont1 ! 422: L' { 'do you want names or ssn to be listed? (n/s): ' ! 423: } ('n'=1YL')/cont1 ! 424: ssnprint 'analize' ! 425: }0 ! 426: cont1: L' { 'do you want a score summary? (y/n): ' ! 427: } ('n'=1YL')/cont2 ! 428: summary ! 429: cont2: L' { 'do you want raw score statistics? (y/n): ' ! 430: } ('n'=1YL')/cont3 ! 431: statsummary ! 432: cont3: L' { 'do you want a mean score summary? (y/n): ' ! 433: } ('n'=1YL')/cont7 ! 434: analysis ! 435: analprint ! 436: didanal { 1 ! 437: cont7: L' { 'do you want individual score type rankings? (y/n): ' ! 438: } ('n'=1YL')/cont4 ! 439: } (didanal)/analcont2 ! 440: analysis ! 441: didanal { 1 ! 442: analcont2: rankprint ! 443: cont4: L' { 'do you want mean score statistics? (y/n): ' ! 444: } ('n'=1YL')/cont5 ! 445: } (didanal)/analcont1 ! 446: analysis ! 447: didanal { 1 ! 448: analcont1: analstats ! 449: cont5: L' { 'do you want histogram and statistics for any specific score? (y/n): ' ! 450: } ('n'=1YL')/0 ! 451: L { 'enter score types (one per line) end with an empty line.' ! 452: cont6: L' { 'type: ' ! 453: tp { L' ! 454: } (0=Rtp)/0 ! 455: tlist { type,[1](1URtype)Y'total' ! 456: data { (0#data)/data { getcol tp ! 457: }(0=Rdata)/cont6 ! 458: tp histgm data ! 459: L { 3 1 R' ' ! 460: L { (3R' '),'statistics of ',tp ! 461: dstat data ! 462: L { 3 1 R' ' ! 463: } cont6 ! 464: ! 465: rankprint rankprint; av1; tlist ! 466: tlist { type,[1](1URtype)Y'total' ! 467: av1 { |H [1]|V [1]means ! 468: L { ' ' ! 469: L { ' ranking for each score type' ! 470: L { ' ' ! 471: L { ((3+1URname),(`1+1URav1)R4) fhead tlist ! 472: L { name,4 0 NJav1 ! 473: L { 3 1 R' ' ! 474: & describe � &syn n new apl score system spring 1982 ! 475: ! 476: an apl workspace is available which is designed to maintain and perform ! 477: analysis on a set of class grades. the system is interactive in nature ! 478: allowing each user to select the set of functions suitable for his or her ! 479: needs. ! 480: ! 481: to obtain a frech copy of this system, type ')load ngradesys' after ! 482: entering apl. ! 483: ! 484: ! 485: setting up the system: ! 486: to set the system up for a new class, the function 'setup' is used. ! 487: 'setup' will respond by asking score types, score weights and student names. ! 488: names may be added or deleted at any time so an accurate name list is not ! 489: required to setup the system. ! 490: ! 491: the system is now setup. save a copy of this setup workspace by typing ! 492: ')save xxx'. the workspace will be saved in the file xxx and may be loaded ! 493: in the future with the load command. it is advisable to keep several backup ! 494: copies of an active grade workspace. ! 495: ! 496: ! 497: alphabetizing the name list: ! 498: to enter new scores into the system efficiently it is best to have the ! 499: names alphabetized both in the system and the stack of new scores. to ! 500: alphabetize the name list, type 'alphabetize'. this will also reorder the ! 501: previously entered scores and ssn. this function may be called any time after ! 502: the system is setup, ussually the function is called just after setup and ! 503: then each time a list of new names is added to the system. ! 504: ! 505: ! 506: entering the social security numbers (ssn): ! 507: social security numbers can be entered by typing 'askssn'. the system ! 508: will then prompt with a name and ask for the corresponding ssn. this ! 509: sequence will continue until all ssn are entered. ! 510: ! 511: entering a new set of grades: ! 512: to enter a new score, type 'newscore' and you will be promted for the ! 513: grade type and the maximum possible score. you will then be prompted for a ! 514: grade for each student. ! 515: ! 516: ! 517: adding and dropping students: ! 518: to add a list of students, type 'newname'. you will then be prompted ! 519: for a list of names. enter each on a new line and then terminate the list ! 520: with a null line. note, any old scores for these new students must be ! 521: inserted with the 'change' function. (described later) ! 522: ! 523: to drop a list of students, type 'drop'. you will then be prompted ! 524: for a list of names. enter each on a new line and then terminate the list ! 525: with a null line. for each name entered, the system will display all grades ! 526: recorded for that student and then request confirmation that you still want ! 527: to drop the student. ! 528: ! 529: changing names, scores, ssn: ! 530: to change the spelling of a name, change the grade of a student or ! 531: correct a students ssn, type 'change'. the function will then prompted ! 532: for the type of change needed. the available types include 'name', 'ssn', ! 533: or one of the previously entered score types. example, if the score types ! 534: available are 'hw', 'test', and 'quiz' and you are wanting to change the ! 535: second homework score, then enter 'hw 2' when prompted. next, the function ! 536: will prompt for a name, the function verifies the name exists and then ! 537: prompts for the new data (the name, score or ssn). ! 538: ! 539: ! 540: analizing the scores: ! 541: to analize the scores, type 'analize'. this function will guide the ! 542: user through the analysis sequence. just answer 'y' or 'n' for each of ! 543: the questions asked. ! 544: the following analysis functions are available: ! 545: 1) score summary - a list of raw scores for all score types and ! 546: all students. this is a convienent method of obtaining ! 547: a hardcopy back up of all scores for the current class. ! 548: 2) statistics summary - this function computes simple statistics ! 549: each set of scores and prints out the results. ! 550: 3) mean score summary - this computes the average percent grade ! 551: for each grade type for each student and also the total ! 552: percent grade based on the weights entered for the score ! 553: types. ! 554: 4) individual score rankings - this functions lists the students ! 555: ordered by their rank of the total and their ranking for ! 556: each score type is also listed. ! 557: 5) mean score statistics - this function will compute simple ! 558: statistics on the data generated by the mean score summary ! 559: (function 3 above). ! 560: 6) histogram and statistics for any specific score - this will ! 561: generate simple statistics and a histogram for and ! 562: individual score or any score type. example: if 'hw' is ! 563: a score type then 'hw' would generate data for the ! 564: combined homeworks and 'hw 2' would generate data for ! 565: just homework #2. ! 566: ! 567: ! 568: ***** notes on using the system ! 569: 1) at no time should a function be interrupted midway through ! 570: completion for this may cause inconsistencies in the workspace. ! 571: 2) the function which looks up names is able to look up a partial ! 572: name and match it. this implies that any time a name is required ! 573: to be typed in, only the first few letters which unambiguously ! 574: identify it are needed. if a partial match is found, then the full ! 575: name will be displayed so you can verify that the correct name was ! 576: matched. if you enter an ambiguous set of letters for a match, ! 577: then the system will display all names which matched correctly. ! 578: 3) three digit scores and fractional scores upset the printing ! 579: format of the score summary in analysis. therefore it is best to ! 580: limit maximum scores to 100.
This archive runs on limited infrastructure. Preserving old code on modern bandwidth. Automated agents are requested to crawl responsibly.