{"id":89,"date":"2022-07-30T12:11:00","date_gmt":"2022-07-30T03:11:00","guid":{"rendered":"https:\/\/web.learning-engineering.com\/?page_id=89"},"modified":"2022-07-30T12:11:00","modified_gmt":"2022-07-30T03:11:00","slug":"2015%e5%b9%b4%e5%ba%a6%e7%a0%94%e7%a9%b6%e6%a5%ad%e7%b8%be","status":"publish","type":"page","link":"https:\/\/web.learning-engineering.com\/?page_id=89","title":{"rendered":"2015\u5e74\u5ea6\u7814\u7a76\u696d\u7e3e"},"content":{"rendered":"\n

\u25cb\u53d7\u8cde<\/h3>\n\n\n\n
  1. Nur HASANAH, Yusuke HAYASHI, Tsukasa HIRASHIMA, Investigation of Students’ Performance in Monsakun Problem Posing Activity based on the Triplet Structure Model of Arithmetical Word Problmes, ICCE2015 Best Technical Design Paper Award, 2015.12.04 \u2192 <\/a>\u8cde\u72b6\u306e\u30b9\u30ad\u30e3\u30f3<\/a><\/li>
  2. Tsukasa HIRASHIMA, Yusuke HAYASHI, Sho YAMAMOTO, Bridging Model between Problem and Solution Representations in Arithmetic\/Mathematics Word Problem, ICCE2015 Best Overall Paper Award Nomination, 2015.12.04 \u2192 \u8cde\u72b6\u30b9\u30ad\u30e3\u30f3\uff0cWeb<\/a>\u30da\u30fc\u30b8<\/a><\/li>
  3. \u53e4\u4e45\u4fdd\u548c\u4ec1, \u5c0f\u7530\u62f3\u592a, \u5ba4\u6d25\u5149\u8cb4, \u5c71\u5143\u7fd4, \u524d\u7530\u4e00\u8aa0, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u7b97\u6570\u6587\u7ae0\u984c\u3092\u5bfe\u8c61\u3068\u3057\u305f\u554f\u984c\u69cb\u9020\u306e\u7d44\u307f\u7acb\u3066\u6f14\u7fd2\u306e\u5b9f\u73fe, 2015\u5e74\u5ea6\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u7b2c29\u56de\u5168\u56fd\u5927\u4f1a, 2015\u5e74\u5ea6\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u5168\u56fd\u5927\u4f1a\u5b66\u751f\u5968\u52b1\u8cde, 2015.0530\u2192\u8cde\u72b6\u306e\u30b9\u30ad\u30e3\u30f3<\/a><\/li>
  4. \u5409\u7530\u5b8c, \u524d\u7530\u5553\u8f14, \u5c71\u4e2d\u5f70, \u5fd7\u7530\u6b63\u8a13, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u30ad\u30c3\u30c8\u30d3\u30eb\u30c9\u6982\u5ff5\u30de\u30c3\u30d7\u3092\u7528\u3044\u305f\u985e\u6bd4\u7684\u5b66\u7fd2\u306e\u652f\u63f4, 2015\u5e74\u5ea6\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u7b2c29\u56de\u5168\u56fd\u5927\u4f1a, 2015\u5e74\u5ea6\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u5168\u56fd\u5927\u4f1a\u5b66\u751f\u5968\u52b1\u8cde, 2015.06.01\u2192\u8cde\u72b6\u306e\u30b9\u30ad\u30e3\u30f3<\/a><\/li>
  5. \u91ce\u6751\u654f\u5f18, \u6797\u96c4\u4ecb, \u9234\u6728\u62d3\u78e8, \u5e73\u5d8b\u5b97, \u6559\u5e2b\u306b\u3088\u308b\u30af\u30e9\u30b9\u5358\u4f4d\u3067\u306e\u5b66\u7fd2\u72b6\u6cc1\u5b9f\u6642\u9593\u628a\u63e1\u306e\u305f\u3081\u306e\u30ad\u30c3\u30c8\u30d3\u30eb\u30c9\u6982\u5ff5\u30de\u30c3\u30d7\u306e\u5229\u7528, 2015\u5e74\u5ea6\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u7b2c29\u56de\u5168\u56fd\u5927\u4f1a, 2015\u5e74\u5ea6\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u5168\u56fd\u5927\u4f1a\u5b66\u751f\u5968\u52b1\u8cde, 2015.05.03\u2192\u8cde\u72b6\u306e\u30b9\u30ad\u30e3\u30f3<\/a><\/li>
  6. \u91ce\u6751 \u654f\u5f18,\u300c \u5e83\u5cf6\u5927\u5b66\u30a8\u30af\u30bb\u30ec\u30f3\u30c8\u30fb\u30b9\u30c1\u30e5\u30fc\u30c7\u30f3\u30c8\u30fb\u30b9\u30ab\u30e9\u30b7\u30c3\u30d7\u300d\u6210\u7e3e\u512a\u79c0\u5b66\u751f<\/strong>, 2015.12\u2192\u8cde\u72b6\u306e\u30b9\u30ad\u30e3\u30f3<\/a><\/li>
  7. \u9ad8\u91ce\u306f\u3044\u306d, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u4f5c\u7528\u30fb\u53cd\u4f5c\u7528\u306e\u56e0\u679c\u7684\u7406\u89e3\u306e\u305f\u3081\u306e\u6f14\u7fd2\u652f\u63f4\u30b7\u30b9\u30c6\u30e0\u306e\u8a2d\u8a08\u30fb\u958b\u767a\u3068\u5b9f\u9a13\u7684\u8a55\u4fa1, 2015<\/strong>\u5e74\u5ea6<\/strong>JSiSE<\/strong>\u5b66\u751f\u7814\u7a76\u767a\u8868\u4f1a<\/strong> <\/strong>\u512a\u79c0\u767a\u8868\u8cde<\/strong>, 2016.2\u2192\u8cde\u72b6\u306e\u30b9\u30ad\u30e3\u30f3<\/a><\/li>
  8. \u6e21\u90e8\u5b5d\u6587, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u5168\u4f53\u90e8\u5206\u95a2\u4fc2\u3092\u7528\u3044\u305f\u548c\u5dee\u7b97\u6570\u6587\u7ae0\u984c\u306b\u304a\u3051\u308b\u5faa\u74b0\u7684\uff13\u9805\u95a2\u4fc2\u306e\u7406\u89e3\u652f\u63f4, 2015\u5e74\u5ea6JSiSE\u5b66\u751f\u7814\u7a76\u767a\u8868\u4f1a \u512a\u79c0\u767a\u8868\u8cde, 2016.2\u2192\u8cde\u72b6\u306e\u30b9\u30ad\u30e3\u30f3<\/a><\/li>
  9. \u672c\u591a\u4fca\u96c4, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u6620\u50cf\u6559\u6750\u3092\u5bfe\u8c61\u3068\u3057\u305f\u30ad\u30c3\u30c8\u30d3\u30eb\u30c9\u6982\u5ff5\u30de\u30c3\u30d7\u3092\u7528\u3044\u305f\u30ce\u30fc\u30c8\u30c6\u30a4\u30ad\u30f3\u30b0\u30b7\u30b9\u30c6\u30e0\u306e\u8a2d\u8a08\u30fb\u958b\u767a, 2015\u5e74\u5ea6JSiSE\u5b66\u751f\u7814\u7a76\u767a\u8868\u4f1a \u512a\u79c0\u767a\u8868\u8cde, 2016.2\u2192\u8cde\u72b6\u306e\u30b9\u30ad\u30e3\u30f3<\/a><\/li>
  10. \u5317\u6751\u62d3\u4e5f, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u30c8\u30a5\u30fc\u30eb\u30df\u30f3\u306e\u4e09\u89d2\u30ed\u30b8\u30c3\u30af\u3092\u7528\u3044\u305f\u8ad6\u7406\u7684\u601d\u8003\u529b\u80b2\u6210\u652f\u63f4\u30b7\u30b9\u30c6\u30e0\u306e\u8a2d\u8a08\u30fb\u958b\u767a, 2015\u5e74\u5ea6JSiSE\u5b66\u751f\u7814\u7a76\u767a\u8868\u4f1a \u512a\u79c0\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u8cde, 2016.2\u2192\u8cde\u72b6\u306e\u30b9\u30ad\u30e3\u30f3<\/a><\/li><\/ol>\n\n\n\n

    <\/a><\/a>\u25cb\u539f\u8457\u8ad6\u6587<\/h3>\n\n\n\n
    1. \u5ddd\u672c\u4f73\u4ee3, \u51fa\u53e3\u76f4\u8f1d, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u7802\u5c71\u6e21, \u8ad6\u7406\u7684\u601d\u8003\u529b\u80b2\u6210\u3092\u6307\u5411\u3057\u305f\u30d5\u30ed\u30fc\u30c1\u30e3\u30fc\u30c8\u6d3b\u7528\u5b66\u7fd2\u30b7\u30b9\u30c6\u30e0\u3068\u5c0f\u5b66\u6821\u5150\u7ae5\u306b\u3088\u308b\u5b9f\u9a13\u7684\u8a55\u4fa1, \u6559\u80b2\u30b7\u30b9\u30c6\u30e0\u60c5\u5831\u5b66\u4f1a, 32\u5dfb3\u53f7, pp.214-219, 2015.7, <\/a>PDF<\/a><\/li>
    2. Tomoya HORIGUCHI, Takahito TOMOTO, Tsukasa HIRASHIMA, <\/a>A Framework of Generating Explanation for Conceptual Understanding Based<\/a> on Semantics of Constraints,<\/a> <\/a>Research and Practice in Technology Enhanced Learning<\/a>, 10:2, Springer<\/a>, 2015,Jun 23<\/li>
    3. Noriyuki MATSUDA, Hisashi OGAWA, Tsukasa HIRASHIMA, Hirokazu TAKI, <\/a>A Generating Technique and Knowledge Representation of Multiple-Answer<\/a> Problems for Learning with Solving Knowledge<\/a>, Research and Practice in Technology Enhanced Learning<\/a>, 10:6, Springer<\/a>, 2015, Jun 23<\/li>
    4. \u6c34\u7530 \u66dc\u5e73, \u5e73\u5d8b \u5b97, \u30ad\u30c3\u30c8\u30d3\u30eb\u30c9\u6982\u5ff5\u30de\u30c3\u30d7\u4f5c\u6210\u904e\u7a0b\u306b\u304a\u3051\u308b\u8aa4\u63a5\u7d9a\u30ea\u30f3\u30af\u4fee\u6b63\u652f\u63f4\u6a5f\u80fd\u3068\u5b9f\u9a13\u7684\u691c\u8a3c, \u6559\u80b2\u30b7\u30b9\u30c6\u30e0\u60c5\u5831\u5b66\u4f1a, 32\u5dfb3\u53f7, pp.202-213, 2015.7 <\/a>PDF<\/a><\/li>
    5. Tsukasa HIRASHIMA, Kazuya YAMASAKI, Hiroyuki FUKUDA, Hideo FUNAOI, Framework of Kit-Build Concept Map for Automatic Diagnosis and Its Preliminary Use, Research and Practice in Technology Enhanced Learning<\/em> 2015, 10:17.PDF<\/a><\/li>
    6. \u6ff1\u7530 \u4f91\u592a\u90ce, \u5c71\u5143 \u7fd4, \u6797 \u96c4\u4ecb, \u5e73\u5d8b \u5b97, \u5358\u7d14\u5316\u65b9\u7565\u306b\u3088\u308b\u8aa4\u308a\u81ea\u5df1\u514b\u670d\u652f\u63f4\u306e\u5b66\u7fd2\u52b9\u679c\u306e\u5b9f\u9a13\u7684\u691c\u8a3c\u2015\u97f3\u697d\u30ea\u30ba\u30e0\u8ab2\u984c\u3092\u5bfe\u8c61\u3068\u3057\u3066\u2015, \u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u8ad6\u6587\u8a8c, Vol.J99-D,No.1,pp.134-137,Jan. 2016,PDF<\/a><\/li>
    7. \u5c71\u5143\u7fd4, \u6a4b\u672c\u62d3\u4e5f, \u795e\u6238\u5065\u5bdb, \u5409\u7530\u7950\u592a, \u524d\u7530\u4e00\u8aa0, \u5e73\u5d8b\u5b97, \u4f5c\u554f\u5b66\u7fd2\u652f\u63f4\u30b7\u30b9\u30c6\u30e0\u300c\u30e2\u30f3\u30b5\u30af\u30f3\u300d\u3078\u306e\u4e57\u6cd5\u306e\u5b9f\u88c5\u3068\u305d\u306e\u5b9f\u8df5\u5229\u7528, \u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u8ad6\u6587\u8a8c, Vol.J99-D,No.2,pp.232-235,Feb. 2016<\/li><\/ol>\n\n\n\n

      <\/a><\/a>\u25cb\u56fd\u969b\u4f1a\u8b70<\/h3>\n\n\n\n
      1. Yusuke HAYASHI, Tsukasa HIRASHIMA, Analysis of the Relationship Between Metacognitive Ability and Learning Activity with Kit-Build Concept Map. <\/a>HCII2016<\/a>(5) 2015: 304-312<\/a><\/li>
      2. Tomoya HORIGUCHI, Takahito TOMOTO, Tsukasa HIRASHIMA, The Effect of Problem Sequence on Students’ Conceptual Understanding in Physics. HCII2016 (5) 2015: 313-322<\/li>
      3. Noriyuki MATSUDA, Hisashi OGAWA, Tsukasa HIRASHIMA, Hirokazu TAKI, Analysis of Multiple-Choice Tests Through Erroneous Choices Using a Technique of Automatic Problem Generation. HCII2016 (5) 2015: 362-369<\/li>
      4. Tsukasa HIRASHIMA, Yusuke HAYASHI, Sho YAMAMOTO, Kazuhisa MAEDA, Bridging Model between Problem and Solution Representations in Arithmetic\/Mathematics Word Problem, Proc. of ICCE2015, pp. 9-18, 2015.11<\/li>
      5. Nur HASANAH, Yusuke HAYASHI, Tsukasa HIRASHIMA, Investigation of Students’ Performance in Monsakun Problem Posing Activity based on the Triplet Structure Model of Arithmetical Word Problems, Proc. of ICCE2015, pp. 27-36, 2015.11<\/li>
      6. Mohammad ALKHATEEB, Yusuke HAYASHI, Taha RAJAB, Tsukasa HIRASHIMA, The Effects of KB\u2010mapping Method to Avoid Sentence\u2010by\u2010 Sentence Comprehension Style in EFL Reading, Proc. of ICCE2015, pp. 46-55, 2015.11<\/li>
      7. Ahmad Afif SUPIANTO, Yusuke HAYASHI, Tsukasa HIRASHIMA, <\/a>Tracing Problem\u2010Posing Activity Sequences toward Detection of Trap\u2010States in Thinking<\/a>, Proc. of ICCE2015, pp. 85-90, 2015.11<\/li>
      8. Tomoya SHINOHARA, Isao IMAI, Takahito TOMOTO, Tomoya HORIGUCHI, Atsushi YAMADA, Sho YAMAMOTO, Yusuke HAYASHI, Tsukasa HIRASHIMA, Experimental Evaluation of Error-Based Simulation for Dynamics Problems in Science Class at Junior High School, Workshop Proc. of ICCE2015, pp. 265-270, 2015.11<\/a><\/li>
      9. Ahmad Afif SUPIANTO, Yusuke HAYASHI, Tsukasa HIRASHIMA, <\/a>Analysis of Problem-Posing Activity Sequences toward Modeling Thinking Process and Detection of Trap States<\/a>, Workshop Proc. of ICCE2015, pp. 558-567, 2015.11<\/li>
      10. Tomoya SHINOHARA, Takahito TOMOTO, Tomoya HORIGUCHI, Atsushi YAMADA, Sho YAMAMOTO, Yusuke HAYASHI, Tsukasa HIRASHIMA, Experimental Use of Error-Based Simulation for Dynamics Problems in National Institutes of Technology, WIPP Proc. of <\/a>ICCE2015<\/a>, pp. 13-15, 2015.11<\/a><\/li><\/ol>\n\n\n\n

        <\/a><\/a>\u25cb\u7814\u7a76\u4f1a\u30fb\u5168\u56fd\u5927\u4f1a\u7b49\u53e3\u982d\u767a\u8868<\/h3>\n\n\n\n
        1. \u53e4\u4e45\u4fdd\u548c\u4ec1, \u5c0f\u7530\u62f3\u592a, \u5ba4\u6d25\u5149\u8cb4, \u5c71\u5143\u7fd4, \u524d\u7530\u4e00\u8aa0, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u7b97\u6570\u6587\u7ae0\u984c\u3092\u5bfe\u8c61\u3068\u3057\u305f\u554f\u984c\u69cb\u9020\u306e\u7d44\u307f\u7acb\u3066\u6f14\u7fd2\u306e\u5b9f\u73fe, <\/a>2015<\/a>\u5e74\u5ea6\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u7b2c<\/a>29<\/a>\u56de\u5168\u56fd\u5927\u4f1a<\/a>(JSAI2015), 2015.0530<\/a><\/li>
        2. \u5409\u7530\u5b8c, \u524d\u7530\u5553\u8f14, \u5c71\u4e2d\u5f70, \u5fd7\u7530\u6b63\u8a13, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u30ad\u30c3\u30c8\u30d3\u30eb\u30c9\u6982\u5ff5\u30de\u30c3\u30d7\u3092\u7528\u3044\u305f\u985e\u6bd4\u7684\u5b66\u7fd2\u306e\u652f\u63f4, <\/a>2015<\/a>\u5e74\u5ea6\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u7b2c<\/a>29<\/a>\u56de\u5168\u56fd\u5927\u4f1a<\/a>(JSAI2015),2015.0601<\/a><\/li>
        3. \u91ce\u6751\u654f\u5f18, \u6797\u96c4\u4ecb, \u9234\u6728\u62d3\u78e8, \u5e73\u5d8b\u5b97, \u6559\u5e2b\u306b\u3088\u308b\u30af\u30e9\u30b9\u5358\u4f4d\u3067\u306e\u5b66\u7fd2\u72b6\u6cc1\u5b9f\u6642\u9593\u628a\u63e1\u306e\u305f\u3081\u306e\u30ad\u30c3\u30c8\u30d3\u30eb\u30c9\u6982\u5ff5\u30de\u30c3\u30d7\u306e\u5229\u7528, <\/a>2015<\/a>\u5e74\u5ea6\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u7b2c<\/a>29<\/a>\u56de\u5168\u56fd\u5927\u4f1a<\/a>(JSAI2015), 2015.05.30<\/a><\/li>
        4. \u91ce\u6751\u654f\u5f18, \u6797\u96c4\u4ecb, \u9234\u6728\u62d3\u78e8, \u5e73\u5d8b\u5b97, \u30a2\u30af\u30c6\u30a3\u30d6\u30e9\u30fc\u30cb\u30f3\u30b0\u306b\u304a\u3051\u308b\u6559\u5e2b\u306e\u30a2\u30af\u30c6\u30a3\u30d6\u5316\u306e\u305f\u3081\u306e\u30ad\u30c3\u30c8\u30d3\u30eb\u30c9\u6982\u5ff5\u30de\u30c3\u30d7\u306e\u5229\u7528, \u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a<\/a> <\/a>\u7b2c<\/a>74<\/a>\u56de<\/a> <\/a>\u5148\u9032\u7684\u5b66\u7fd2\u79d1\u5b66\u3068\u5de5\u5b66\u7814\u7a76\u4f1a\uff08<\/a>SIG-ALST-74<\/a>\uff09, <\/a>pp.22-27, 2015.07.18<\/a><\/li>
        5. \u5e83\u7530\u667a\u4e5f, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u5358\u6587\u7d71\u5408\u578b\u4f5c\u554f\u6f14\u7fd2\u306b\u304a\u3051\u308b\u8996\u7dda\u60c5\u5831\u304b\u3089\u306e\u4f5c\u554f\u30d7\u30ed\u30bb\u30b9\u306e\u5206\u6790, <\/a>2015<\/a>\u5e74\u5ea6\u6559\u80b2\u30b7\u30b9\u30c6\u30e0\u60c5\u5831\u5b66\u4f1a<\/a> <\/a>\u7b2c<\/a>40<\/a>\u56de\u5168\u56fd\u5927\u4f1a<\/a>(JSiSE2015), pp201~202, 2015.0902<\/a><\/li>
        6. \u57ce\u8c37\u662d\u5145, \u5e73\u5d8b\u5b97, \u5206\u6570\u6f14\u7b97\u306e\u95a2\u4fc2\u7684\u7406\u89e3\u3092\u53ef\u80fd\u3068\u3059\u308b\u5206\u6570\u30d6\u30ed\u30c3\u30af\u30e2\u30c7\u30eb\u3068\u305d\u306e\u64cd\u4f5c\u578b\u5b66\u7fd2\u74b0\u5883\u306e\u8a2d\u8a08\u30fb\u958b\u767a, <\/a>2015<\/a>\u5e74\u5ea6\u6559\u80b2\u30b7\u30b9\u30c6\u30e0\u60c5\u5831\u5b66\u4f1a\u7b2c<\/a>40<\/a>\u56de\u5168\u56fd\u5927\u4f1a<\/a>(JSiSE2015), pp.335-336, 2015.09.03<\/a><\/li>
        7. \u7be0\u539f\u667a\u54c9, \u6771\u672c\u5d07\u4ec1, \u5800\u53e3\u77e5\u4e5f, \u5c71\u7530\u6566\u58eb, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u904b\u52d5\u7cfb\u306b\u304a\u3051\u308b\u529b\u306e\u628a\u63e1\u306e\u4fc3\u9032\u3092\u76ee\u6307\u3057\u305f<\/a>Error-based Simulation<\/a>\u3068\u305d\u306e\u8a55\u4fa1\u5b9f\u9a13, <\/a>2015<\/a>\u5e74\u5ea6\u6559\u80b2\u30b7\u30b9\u30c6\u30e0\u60c5\u5831\u5b66\u4f1a\u7b2c<\/a>40<\/a>\u56de\u5168\u56fd\u5927\u4f1a<\/a>(JSiSE2015), pp.333-334, 2015.09.03<\/a><\/li>
        8. \u5e73\u5d8b\u5b97, \u518d\u751f\u7684\u601d\u8003\u8ab2\u984c\u306e\u5275\u9020\u7684\u601d\u8003\u8ab2\u984c\u306b\u5909\u63db\u3059\u308b\u305f\u3081\u306e\u5b66\u7fd2\u8ab2\u984c\u306e\u60c5\u5831\u69cb\u9020\u30e2\u30c7\u30eb\u5316, \u6559\u80b2\u5de5\u5b66\u4f1a\u5168\u56fd\u5927\u4f1a, 3a-A303-04(2015)PDF<\/a><\/li>
        9. \u5bae\u8feb\u7fd4\u5e73, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u5de5\u592b\u8a08\u7b97\u3092\u5bfe\u8c61\u3068\u3057\u305f\u4f8b\u984c\u540c\u7b49\u554f\u984c\u4f5c\u6210\u6f14\u7fd2\u306e\u8a2d\u8a08\u30fb\u958b\u767a, \u4e2d\u56fd\u652f\u90e8<\/a>2015<\/a>\u5e74\u5ea6\u7814\u7a76\u767a\u8868\u4f1a<\/a>(JSiSE2015), pp.7-12, 2015.10.10<\/a><\/li>
        10. Ahmad Afif SUPIANTO,Yusuke HAYASHI,Tsukasa HIRASHIMA,Trap-States Found in Problem-Posing Activity Sequences Based on Triplet Structure Model,<\/a>\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u7b2c<\/a>75<\/a>\u56de\u5148\u9032\u7684\u5b66\u7fd2\u79d1\u5b66\u3068\u5de5\u5b66\u7814\u7a76\u4f1a<\/a>(SIG-ALST)pp,-, 2015.11.14<\/a><\/li>
        11. \u4e2d\u7530\u5927\u4ecb, \u6797\u96c4\u4ecb, \u7b20\u4e95\u4fca\u4fe1, \u76ca\u5ddd\u5f18\u5982, \u5e73\u5d8b\u5b97, \u6c38\u91ce\u548c\u7537, \u6e9d\u53e3\u7406\u4e00\u90ce, \u6388\u696d\u6848\u306e\u610f\u56f3\u69cb\u9020\u306e\u518d\u69cb\u6210\u6f14\u7fd2\u30b7\u30b9\u30c6\u30e0\u306e\u8a2d\u8a08\u3068\u958b\u767a, \u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u7b2c<\/a>75<\/a>\u56de\u5148\u9032\u7684\u5b66\u7fd2\u79d1\u5b66\u3068\u5de5\u5b66\u7814\u7a76\u4f1a<\/a>(SIG-ALST), pp.-, 2015.11.14<\/a><\/li>
        12. \u5317\u6751<\/a> <\/a>\u62d3\u4e5f, \u5c71\u4e2d<\/a> <\/a>\u5f70, \u524d\u7530<\/a> <\/a>\u5553\u8f14, \u6797<\/a> <\/a>\u96c4\u4ecb, \u5e73\u5d8b<\/a> <\/a>\u5b97, \u540c\u4e00\u547d\u984c\u69cb\u9020\u306b\u57fa\u3065\u304f\u7a74\u57cb\u3081\u554f\u984c\u3068\u30ad\u30c3\u30c8\u30d3\u30eb\u30c9\u6982\u5ff5\u30de\u30c3\u30d7\u306e\u751f\u6210\u3068\u5b9f\u9a13\u7684\u5229\u7528<\/a>, \u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u7b2c75\u56de\u5148\u9032\u7684\u5b66\u7fd2\u79d1\u5b66\u3068\u5de5\u5b66\u7814\u7a76\u4f1a(SIG-ALST)pp,-, 2015.11.14<\/li>
        13. \u524d\u7530\u5553\u8f14, \u5317\u6751\u62d3\u4e5f, \u672c\u591a\u4fca\u96c4, \u8305\u5cf6\u8def\u5b50, \u5b87\u4e95\u7f8e\u4ee3\u5b50, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u6620\u50cf\u8b1b\u7fa9\u3068\u30ad\u30c3\u30c8\u30d3\u30eb\u30c9\u6982\u5ff5\u30de\u30c3\u30d7\u306e\u5bfe\u5fdc\u4ed8\u3051\u306b\u3088\u308b\u6559\u6388\u8005\u3068\u5b66\u7fd2\u8005\u306e\u7406\u89e3\u306e\u305a\u308c\u306e\u691c\u51fa\u6a5f\u80fd\u306e\u958b\u767a\u3068\u5b9f\u8df5\u5229\u7528, \u6559\u80b2\u30b7\u30b9\u30c6\u30e0\u60c5\u5831\u5b66\u4f1a\uff08<\/a>JSiSE<\/a>\uff09<\/a>2015<\/a>\u5e74\u5ea6\u7b2c<\/a>4<\/a>\u56de\u7814\u7a76\u4f1a, <\/a>pp.25-32, 2015.11.21<\/a><\/li>
        14. \u5c71\u4e2d\u5f70, \u524d\u7530\u5553\u8f14, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u5f62\u6210\u7684\u8a55\u4fa1\u3068\u30d5\u30a3\u30fc\u30c9\u30d0\u30c3\u30af\u306e\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u5316\u3092\u6307\u5411\u3057\u305f\u30ad\u30c3\u30c8\u30d3\u30eb\u30c9\u6982\u5ff5\u30de\u30c3\u30d7\u306e\u6559\u5e2b\u7528\u30a2\u30ca\u30e9\u30a4\u30b6\u306e\u8a2d\u8a08\u30fb\u958b\u767a, \u65e5\u672c\u6559\u80b2\u5de5\u5b66\u4f1a\u7814\u7a76\u5831\u544a\u96c6, <\/a>JSET15-5<\/a>\uff0c<\/a>pp.19-26, 2015.12.12<\/a><\/li>
        15. \u677e\u672c\u660e\u7d18, \u6797\u96c4\u4ecb, \u7b20\u4e95\u4fca\u4fe1, \u5e73\u5d8b\u5b97, \u6e9d\u53e3\u7406\u4e00\u90ce, \u6559\u5e2b\u30b3\u30df\u30e5\u30cb\u30c6\u30a3\u4f9d\u5b58\u306e\u6559\u6388\u77e5\u8b58\u306b\u57fa\u3065\u304f\u6388\u696d\u8a2d\u8a08\u652f\u63f4\u3092\u5fd7\u5411\u3057\u305f\u6388\u696d\u30d7\u30e9\u30f3\u304b\u3089\u306e\u6df1\u5c64\u69cb\u9020\u306e\u62bd\u51fa, \u4fe1\u5b66\u6280\u5831, <\/a>Vol.115, No.492, pp.291-296, 2016.3.5<\/a><\/li>
        16. \u5c71\u7530\u6566\u58eb, \u5b89\u7530\u5065\u6c70, \u7be0\u539f\u667a\u54c9, \u5c71\u5143\u7fd4, \u5800\u53e3\u77e5\u4e5f, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u7269\u4f53\u306b\u306f\u305f\u3089\u304f\u529b\u30fb\u52a0\u901f\u5ea6\u30fb\u901f\u5ea6\u306e\u95a2\u9023\u4ed8\u3051\u306e\u305f\u3081\u306e<\/a>Error-Based Simulation, <\/a>\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u7b2c<\/a>76<\/a>\u56de\u5148\u9032\u7684\u5b66\u7fd2\u79d1\u5b66\u3068\u5de5\u5b66\u7814\u7a76\u4f1a<\/a>(SIG-ALST), pp.-, 2016.03.06<\/a><\/li>
        17. \u91ce\u6751\u654f\u5f18, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u6388\u696d\u5185\u5b66\u7fd2\u6d3b\u52d5\u306e\u30de\u30cd\u30b8\u30e1\u30f3\u30c8\u3092\u6307\u5411\u3057\u305f\u500b\u4eba\u6d3b\u52d5\u304b\u3089\u306e\u30b0\u30eb\u30fc\u30d7\u6d3b\u52d5\u6210\u679c\u4e88\u6e2c\u6cd5, \u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a<\/a> <\/a>\u7b2c<\/a>76<\/a>\u56de<\/a> <\/a>\u5148\u9032\u7684\u5b66\u7fd2\u79d1\u5b66\u3068\u5de5\u5b66\u7814\u7a76\u4f1a\uff08<\/a>SIG-ALST-76<\/a>\uff09, <\/a>pp.24-29, 2015.03.06<\/a><\/li>
        18. Jaruwat PAILAI, Warunya WUNNASRI, Yusuke HAYASHI, Tsukasa HIRASHIMA, Automatic Concept Map Assessment in Formative Assessment Approach, <\/a>\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u7b2c<\/a>76<\/a>\u56de\u5148\u9032\u7684\u5b66\u7fd2\u79d1\u5b66\u3068\u5de5\u5b66\u7814\u7a76\u4f1a<\/a>(SIG-ALST), pp.-, 2016.03.06<\/a><\/li>
        19. Warunya WUNNASRI, Jaruwat PAILAI, Yusuke HAYASHI, Tsukasa HIRASHIMA, Comparison between Scratch-and Kit-Build concept map with several evaluation methods, <\/a>\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u7b2c<\/a>76<\/a>\u56de\u5148\u9032\u7684\u5b66\u7fd2\u79d1\u5b66\u3068\u5de5\u5b66\u7814\u7a76\u4f1a<\/a>(SIG-ALST), pp.-, 2016.03.06<\/a><\/li>
        20. \u5317\u6751\u62d3\u4e5f, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u30c8\u30a5\u30fc\u30eb\u30df\u30f3\u306e\u4e09\u89d2\u30ed\u30b8\u30c3\u30af\u3092\u7528\u3044\u305f\u8ad6\u7406\u7684\u601d\u8003\u529b\u80b2\u6210\u652f\u63f4\u30b7\u30b9\u30c6\u30e0\u306e\u8a2d\u8a08\u30fb\u958b\u767a,<\/a> 2015<\/a>\u5e74\u5ea6<\/a>JSiSE<\/a>\u5b66\u751f\u7814\u7a76\u767a\u8868\u4f1a, 2<\/a>016.2<\/a><\/li>
        21. \u672c\u591a\u4fca\u96c4, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u6620\u50cf\u6559\u6750\u3092\u5bfe\u8c61\u3068\u3057\u305f\u30ad\u30c3\u30c8\u30d3\u30eb\u30c9\u6982\u5ff5\u30de\u30c3\u30d7\u3092\u7528\u3044\u305f\u30ce\u30fc\u30c8\u30c6\u30a4\u30ad\u30f3\u30b0\u30b7\u30b9\u30c6\u30e0\u306e\u8a2d\u8a08\u30fb\u958b\u767a, <\/a>2015<\/a>\u5e74\u5ea6<\/a>JSiSE<\/a>\u5b66\u751f\u7814\u7a76\u767a\u8868\u4f1a, <\/a>2016.2<\/a><\/li>
        22. \u6d25\u5b88\u5eb8\u5e73, \u6797\u76f4\u4e5f, \u7be0\u539f\u667a\u54c9, \u5c71\u5143\u7fd4, \u5800\u53e3\u77e5\u4e5f, \u6797\u96c4\u4ecb, \u5e73\u5d8b\u5b97, \u5358\u7d14\u5316\u65b9\u7565\u3092\u7528\u3044\u305f\u884c\u304d\u8a70\u307e\u308a\u306e\u81ea\u5df1\u514b\u670d\u652f\u63f4\u30b7\u30b9\u30c6\u30e0\u306e\u305f\u3081\u306e\u30e2\u30cb\u30bf\u30ea\u30f3\u30b0\u30c4\u30fc\u30eb\u306e\u8a2d\u8a08\u30fb\u958b\u767a\u3068\u5b9f\u8df5\u7684\u5229\u7528, <\/a>2015<\/a>\u5e74\u5ea6<\/a>JSiSE<\/a>\u5b66\u751f\u7814\u7a76\u767a\u8868\u4f1a, <\/a>2016.2<\/a><\/li>
        23. \u5b89\u7530<\/a> <\/a>\u5065\u6c70, \u5c71\u7530<\/a> <\/a>\u6566\u58eb, <\/a><\/a>\u7be0\u539f<\/a> <\/a>\u667a\u54c9<\/a>, <\/a>\u5c71\u5143<\/a> <\/a>\u7fd4<\/a>, <\/a>\u5800\u53e3<\/a> <\/a>\u77e5\u4e5f<\/a>, <\/a>\u6797<\/a> <\/a>\u96c4\u4ecb<\/a>, <\/a>\u5e73\u5d8b<\/a> <\/a>\u5b97, <\/a>Error-Based Simulation<\/a>\u7528\u3044\u305f\u529b\u5b66\u6f14\u7fd2\u306b\u304a\u3051\u308b\u30e2\u30cb\u30bf\u30ea\u30f3\u30b0\u30c4\u30fc\u30eb\u306e\u8a2d\u8a08\u30fb\u958b\u767a, <\/a>2015<\/a>\u5e74\u5ea6<\/a>JSiSE<\/a>\u5b66\u751f\u7814\u7a76\u767a\u8868\u4f1a, <\/a>2016.2<\/a><\/li>
        24. 2015\u5e74\u5ea6\u6559\u80b2\u30b7\u30b9\u30c6\u30e0\u60c5\u5831\u5b66\u4f1a\u5b66\u751f\u767a\u8868\u4f1a6\u4ef6<\/li><\/ol>\n\n\n\n

          –\u5171\u540c\u7814\u7a76\u8005<\/p>\n\n\n\n

          no data<\/p>\n\n\n\n

          <\/a><\/a>\u25cb\u62db\u5f85\u8b1b\u6f14\uff0f\u4f9d\u983c\u8b1b\u6f14<\/h3>\n\n\n\n