A Review of Data-Driven Personalized Adaptive Learning
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摘要: 智能教育环境下的教学更加关注学习者的个性化诉求，而自适应学习能够为实现个性化教育提供技术和方法支持.文章从数据驱动的视角出发，通过开展国内外个性化自适应学习研究的综述分析，对其系统框架和相关组件进行阐述和解读.其中，重点从领域知识模型、学习者特征模型和教学模型三方面对其实现机制进行探析，提出当前研究存在的问题和不足，并在此基础上介绍了近年来可促进解释性提升的相关组件技术研究，奠定进一步深入个性化自适应学习研究的基础.
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关键词:
- 个性化自适应学习 /
- 教育知识图谱及其表示学习 /
- 知识追踪 /
- 个性化学习路径推荐
Abstract: Teaching in a smart education environment pays more attention to the individual demands of learners, and adaptive learning can provide technical and methodological support for achieving personalized education. A review of the domestic and foreign research on personalized adaptive learning research is conducted to interpret its system framework and related components. Its implementation mechanism is given at three aspects, i.e., the domain knowledge model, the learner characteristic model, and the teaching model. After a comprehensive analysis, the problems and deficiencies of current research are pointed out. On that basis, the research on related component technologies in recent years that can promote interpretability is introduced to provide references for the next step of personalized adaptive learning research. -
随着社会经济的持续发展以及人类日益增长的物质生活需要，化学能源出现紧缺，环境污染问题日益严峻，因此开发利用可替代的清洁能源和可再生能源就显得尤为必要[1]。氢能具有极高的能量密度和高燃烧值(约为汽油的3倍，仅次于核能)，燃烧产物清洁，完全零排放，极具应用前景。然而，氢气是二次能源[2]，主要通过其他途径[3]产生，譬如利用化石燃料(煤、石油和天然气)制氢[4]、利用生物质制氢[5]、用水制氢，而难以从自然环境中直接获取。作为可再生资源，生物质资源丰富，储备充足，相比化石燃料造成的污染小，但能量密度低、制备氢气的过程复杂，不适合推广[6-7]。若能利用自然可再生资源产氢，这将有助于缓解能源紧缺的现状，同时创造巨大的经济和环境效益。
太阳能作为世界上最清洁的可再生能源之一，辐射到地表的总功率巨大，相比其他能源更具持续性，可作为二次能源，且储量丰富，是制氢的理想能源[8]。因此，利用太阳能实现光催化制氢受到广泛关注。目前，光催化制氢技术已受广泛研究[9-11]，如王熙等[9]将Cu2O和TiO2引入石墨烯制备出新型的光催化薄膜，在光催化产氢实验中该薄膜表现出很强的光催化产氢性能；廖添等[10]将Fe、Cr与TiO2共掺杂制备出纳米球用于光催化制氢，证明其具有良好的光催化稳定性。
光催化产氢主要利用半导体材料在合适的光源能量激发下，产生光生电子与光生空穴，并利用光生电子的还原性还原H+生成氢气的过程。现阶段，半导体光催化剂主要是n型半导体，材料多用金属氧化物和硫化物(如TiO2、Cu2O、ZnO、CdS等)，其中TiO2因储量丰富、价格低廉、无毒无害、能带结构较为合理被广泛应用[12]。但是在一般情况下，TiO2只能被紫外光激发，且光生空穴和电子易复合，致使量子效率低下，导致TiO2的光催化活性较低[13]，因此对TiO2进行改性提高其对可见光的利用是十分有意义的。
Cu2O通常为红色或黄色粉末，八面立方体，禁带宽度约2.0 eV[14]，因其具有低毒、便宜、容易制备、吸收可见光、理论利用效率(9%~11%)较高以及能带隙可调等优点而成为颇具应用潜力的半导体材料[15]，将其与TiO2进行复合可有效提高对可见光的利用率。在本研究中，利用Cu2O和TiO2进行复合制备了Cu2O-TiO2光催化剂薄膜，促使Cu2O导带上的部分光生电子迁移至TiO2的导带上，TiO2价带上的光生空穴迁移至Cu2O的价带上，增强了对可见光的吸收，抑制光生电子(e-)和空穴(h+)的分离，从而提高半导体光催化效果[16]。本研究还以甲醇溶液为模拟废水，研究Cu2O-TiO2催化甲醇的产氢性能以及影响产氢性能的相关因素。
1. 实验部分
1.1 主要试剂与仪器
主要试剂：乙酸钠、乙酸铜、氢氧化钠、乳酸、甲醇、无水乙醇，均为分析纯，均由广州化学试剂厂生产。浓盐酸从广州光华化学厂有限公司购入，二氧化钛由广州和仟贸易有限公司提供。实验用水为去离子水(或自制超纯水)。
主要仪器：气相色谱仪(GC9560型，上海华爱)、300 W的氙灯(PLS-SXE300，北京畅拓)、UV-Vis漫反射光谱(U-3010，HITACHI)、X射线粉末衍射仪(D8 ADVANCE°，德国Bruker)、荧光分光光度计(RF-540，日本岛津)、扫描电子显微镜(Ultra 55，德国)、气氛保护箱式炉(QSXL-1008，杭州卓驰)。
1.2 Cu2O-TiO2复合光催化剂的制备
制备Cu2O薄膜光催化剂：称取4.10 g乙酸钠和1.99 g乙酸铜，用去离子水溶解，定容至500 mL；取120 mL溶液至反应器中，用3%(质量分数)盐酸调节pH至5.86；将已处理好的2.5 cm×3 cm铜片安装在光催化反应器中，在电压为1.85 eV、电流为0.30 A条件下电沉积90 min；最后将样品(Cu2O薄膜)置于马弗炉氮气保护和200 ℃下煅烧1 h。
制备Cu2O-TiO2复合薄膜：采用涂覆法将TiO2均匀的涂到Cu2O薄膜上。具体步骤：称量30 mg TiO2，溶于2 400 μL无水乙醇，置于超声中30~60 min使TiO2分散均匀；然后用移液枪吸取一定量的溶液均匀涂覆在已制备好的Cu2O薄膜上；将Cu2O-TiO2样品置于马弗炉氮气保护200 ℃下煅烧1 h，使TiO2和Cu2O紧密复合。
1.3 材料表征和性能测试
采用扫描电子显微镜(SEM)观察Cu2O的形态，并用X射线粉末衍射(XRD)分析Cu2O的晶型结构。采用紫外-可见漫反射光谱(UV-Vis DRS)和荧光光谱(PL)分析Cu2O-TiO2的光学性质。
1.4 Cu2O-TiO2产氢性能测试
1.4.1 探究光源对Cu2O-TiO2产氢性能的影响
光催化产氢实验的装置如图 1所示，具体操作过程如下：先将光催化剂复合薄膜正确安装在光催化电解池中，以体积分数为20%甲醇水溶液或者葡萄糖溶液为牺牲剂，进行光催化反应实验。以功率为300 W的氙灯作为模拟太阳光的光源，实验过程中所使用的可见光由截止波长为420 nm的滤光片滤除紫外光得到，反应容器石英窗口距离光源约为10 cm。光催化反应前，整个体系用N2吹扫30 min，以便除去水中的溶解氧及光催化电解池中的氧气(O2)。采用循环冷却水系统恒定在室温(25 ℃)，实验的光照总时间设定为1.5 h，每隔30 min从光催化反应器的气体取样口中采集气体样品，采用气相色谱(GC-TCD，TDX-01柱，载气Ar)进行H2产量的定量分析。
采用单位面积的产氢速率RA衡量材料的光催化产氢活性：
(1) 其中，RA为产氢速率(mmol/(h·m2))，nH2为氢气的产量(物质的量，mmol)，A为复合薄膜的面积(m2)，t为光催化反应时间(h)。
采用表观量子产率(Apparent Quantum Yield，AQY)评估空穴复合效率，采用草酸铁钾光量计[17]法测量总激发光子数。表观量子产率
(2) 其中，AQY为表观量子产率(%)，nH2为氢气产量(物质的量，mmol)，np为总激发光子数。
1.4.2 探究反应条件对产氢性能的影响
以功率为300 W的氙灯模拟太阳光为光催化产氢实验的光源，以甲醇水溶液作为模拟废水进行产氢反应实验。光催化反应器总体积150 mL，反应溶液100 mL，反应时间为120 min，采用TiO2质量分数分别为0%、10.4%、13.9%、34.7%、41.7%、55.6%、100%的Cu2O-TiO2为催化剂，最后用GC9560型气相色谱仪对H2进行定性、定量分析，计算氢气的反应速率。以功率为300 W的氙灯模拟太阳光为光源，配制不同体积分数(5%、10%、15%、20%、25%、50%)的甲醇溶液分别进行一系列的光催化产氢反应实验。采用功率为300 W的氙灯为照射光源，以体积分数为20%的甲醇水溶液为模拟废水，采用盐酸和NaOH调节模拟废水的pH(分别为3.88、5.24、8.97、11.01)，开启光源(记录时间)进行光催化产氢实验，并于反应开始后的第30、60、90、120 min时取样进行相关测试。
2. 结果与讨论
2.1 Cu2O的微观形貌
采用扫描电子显微镜拍摄Cu2O薄膜的微观形貌(图 2)，采用电化学沉积法制备所得的Cu2O晶体大小相近(图 2A)，均匀且紧密地沉积在基底表面。Cu2O微晶直径约1.5 μm，Cu2O是规则的多面体状晶体(图 2A插图)。图 2B为Cu2O-TiO2复合薄膜光催化剂的横截面图，在Cu2O膜上涂覆TiO2之后，Cu2O微晶几乎被TiO2完全覆盖，而Cu2O-TiO2表面相对均匀平整，Cu2O-TiO2的厚度约36.2 μm。
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图 2 Cu2O薄膜表面及Cu2O-TiO2/Cu横截面的SEM图Figure 2. The SEM images of Cu2O film and the cross section plane of Cu2O-TiO2/Cu2.2 催化剂的晶相结构分析
图 3为Cu2O和Cu2O-TiO2光催化剂薄膜的XRD图谱，2θ=29.57°、36.43°、42.31°、61.38°处的衍射峰分别为Cu2O的(110)、(111)、(200)、(220)晶面，与Cu2O的PDF卡片(JCPDS 78-2076)一致，说明已成功制备出了Cu2O。2θ=25.32°和48.06°处的衍射峰分别为TiO2(101)和(200)晶面，同时在2θ=29.57°、36.43°、42.31°、61.38°处均发现了Cu2O的衍射峰。此外，在复合薄膜的XRD图谱中并未发现CuO晶体的衍射峰，这说明在Cu2O-TiO2的制备过程中，Cu2O(Cu+)未被氧化成CuO(Cu2+)，说明已制备出Cu2O-TiO2复合薄膜。
2.3 紫外-可见漫反射光谱及光致荧光光谱分析
图 4为Cu2O、TiO2和Cu2O-TiO2复合薄膜的UV-Vis DRS谱，反映了不同物质对光的吸收性质。TiO2只吸收紫外光(λ＜400 nm)，在可见光区域(λ=400~800 nm)基本无吸收；而Cu2O在波长400~600 nm范围出现最大的吸收峰，说明Cu2O对可见光有响应；Cu2O-TiO2复合薄膜在波长400~800 nm吸收可见光的能力逐渐增强。相比Cu2O和TiO2，Cu2O-TiO2复合薄膜对太阳光有较强的响应能力，这是因为Cu2O能够响应可见光，Cu2O和TiO2形成异质结后能够有效提高Cu2O-TiO2对光的吸收性能，使其对太阳光的利用范围扩展至可见光区[18]。
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图 4 Cu2O、TiO2和Cu2O-TiO2的UV-Vis DRS谱Figure 4. The UV-Vis DRS spectra of Cu2O, TiO2 and Cu2O-TiO2当半导体受在光的激发下，电子从价带跃迁至导带并在价带留下空穴，当电子和空穴再通过复合发光，形成不同波长光的强度或能量分布的光谱图(图 5)。Cu2O、TiO2和Cu2O-TiO2的最强荧光峰均位于波长400~500 nm，TiO2、Cu2O和Cu2O-TiO2显示出相似的光谱曲线。TiO2的荧光强度最高，TiO2、Cu2O和Cu2O-TiO2表观量子产率系数分别为9.33%、5.95%和64.4%。由此表明，相比TiO2和Cu2O薄膜，Cu2O-TiO2复合薄膜的光生电子和空穴复合效率最小，这说明在Cu2O-TiO2内部存在快速的光生载流子的迁移和分离。
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图 5 Cu2O、TiO2和Cu2O-TiO2的光致荧光光谱Figure 5. The photofluorescence spectra of Cu2O, TiO2 and Cu2O-TiO22.4 TiO2质量分数对光催化活性的影响
为了获取更高的产氢效率，对Cu2O-TiO2复合光催化剂的组成进行优化，寻找光催化活性最好的TiO2质量分数。Cu2O-TiO2中TiO2的质量分数分别为0%、10.4%、13.9%、34.7%、41.7%、55.6%、100%，光催化时间为150 min，通过氢气的反应速率来确定最佳的TiO2质量分数。
由图 6明显看出：当TiO2质量分数为0%(纯Cu2O)时，产氢活性最低，产氢速率仅为8.6 mmol/(h·m2)。然而随着TiO2质量分数的增加，产氢速率会不断上升。当TiO2质量分数为34.7%时，产氢速率最高(93.12 mmol/(h·m2))，是Cu2O的10.8倍。但是当TiO2质量分数进一步增加时，Cu2O-TiO2产氢速率会不断下降。当TiO2质量分数为100%(TiO2)时，产氢速率降至13.5 mmol/(h·m2)。通过PL光谱分析可知，Cu2O-TiO2薄膜产氢性能的提高是由于TiO2和Cu2O之间构成的异质结使Cu2O-TiO2薄膜能拓展材料对光的吸收波长范围，并且具有更强的光吸收能力，同时e-与h+得到有效分离，Cu2O与TiO2构成异质结，e-迁移至TiO2较低的导带上，而h+主要聚集在Cu2O较低价带上，从而促进光生电子-空穴对的分离，提高了Cu2O-TiO2薄膜的产氢活性。然而当TiO2质量分数进一步增加(大于34.7%)时，产氢速率开始下降，可能是由于TiO2质量分数过高降低了光的渗透率，使传播至Cu2O表面的光减少，导致内层的Cu2O未得到充分激发，从而减弱了Cu2O和TiO2的协同作用。这也说明TiO2和Cu2O之间形成的异质结能更有效地降低e-与h+的复合率，从而提高光催化的产氢速率。
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图 6 TiO2质量分数对产氢性能的影响Figure 6. The effect of TiO2 loading on the properties of hydrogen production2.5 Cu2O-TiO2不同光源对产氢性能的影响
表 1为Cu2O-TiO2在不同光源(紫外光、可见光、模拟太阳光)下的产氢速率，Cu2O-TiO2复合薄膜在紫外光和太阳光的照射下均能产生氢气，其中在太阳光下产氢速率最高，但可见光的照射下几乎不产生氢气。由UV-Vis漫反射光谱(图 4)可知，Cu2O-TiO2复合催化剂既可以吸收紫外光，又可吸收可见光。而在太阳光下的产氢速率是紫外光下产氢速率的2.45倍，这说明Cu2O-TiO2不仅利用了紫外光，还利用了太阳光中的可见光，使Cu2O导带上的部分光生电子能迁移至较高电位的TiO2导带上，TiO2价带上的光生空穴会迁移至较低电位的Cu2O价带上，有利于促进光生电子的分离。然而在可见光条件下，Cu2O-TiO2薄膜不能产生氢气，这可能是因为可见光只能使Cu2O激发，其能量达不到还原水(H2O)或者氢离子(H+)产生氢气所需能量；此外，外层的TiO2覆盖在Cu2O表面，在可见光下TiO2没有被激发，Cu2O-TiO2异质结没有发挥作用，导致e-和h+的数量减少，因此无氢气产生。
表 1 Cu2O-TiO2在不同光源下的产氢速率Table 1. The hydrogen production rate of Cu2O-TiO2 under different light sources光源 产氢速率/(mmol·h-1·m-2) 紫外光 38.03 太阳光 93.12 可见光 未检出 2.6 Cu2O-TiO2光催化产氢的影响因素
以甲醇溶液为模拟废水，分别在不同甲醇浓度和不同pH条件下进行产氢实验。随着甲醇体积分数的逐渐增加，产氢速率不断增加(图 7)。当甲醇体积分数为50%时，产氢速率达到了最大值，这是因为甲醇作为牺牲剂可以产生羟基自由基(·OH)消耗了光催化剂的h+，促进了光生载流子的分离，有利于H+得电子被还原生成氢气(H2)，因此甲醇体积分数越大，能够消耗大量电子空穴，从而使产氢速率越高。在反应前期和中期(30~90 min)，体积分数为5%~20%的甲醇产氢速率基本在同一水平上，直到在反应的后期(90~120 min)才开始明显分化。同一时间点，甲醇体积分数越高，产氢速率越高。
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图 7 甲醇质量分数对产氢量的影响Figure 7. The effect of methanol mass fraction on hydrogen productionpH对产氢量的影响如图 8所示，在pH 3.88条件下，产氢量最大。这是因为：一方面，在空穴还原H+产氢的过程中，pH越低溶液中H+浓度越高，越有利于电子还原产氢；另一方面，产氢速率并不是随pH的升高而降低，pH 8.97下的产氢量明显比pH 5.24下的产氢量大，这主要是因为OH-可与空穴反应生成羟基自由基，进而降解甲醇，在弱碱性条件下，溶液中OH-浓度较高，有利于空穴的消耗，从而促进光生电子与空穴的分离，最终提高了产氢效率。
3. 结论
通过电化学沉积法和涂覆法制备出致密而均匀的Cu2O-TiO2复合薄膜光催化剂，证明了其具有较高的光催化活性。相比单一的Cu2O和TiO2，所制得的Cu2O-TiO2复合薄膜对太阳光有较宽的光谱响应能力，能够降低电子和空穴的复合率，提高对太阳能的利用率，从而提高氢气的产量。单一Cu2O或TiO2光催化剂的产氢效率不及Cu2O-TiO2复合光催化剂的产氢效率高：当TiO2质量分数为34.7%时，光催化产氢速率最大，且随着甲醇体积分数的增加而增大；当甲醇体积分数为50%时，产氢速率最大。产氢量容易受到溶液中的H+和OH-浓度的影响：pH越小，越能提供更多的H+和电子结合，生成H2；而在弱碱性条件下OH-与空穴结合，有利于空穴的消耗，从而促进光生电子与空穴的分离，提高产氢效率。
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[1] 菅保霞, 姜强, 赵蔚, 等.大数据背景下自适应学习个性特征模型研究——基于元分析视角[J].远程教育杂志, 2017, 35(4):87-96. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=672643087 JIAN B X, JIANG Q, ZHAO W, et al. Research on stu-dents' personality traits modeling in adaptive learning on the background of big data:based on the perspective of meta-analysis[J]. Journal of Distance Education, 2017, 35(4):87-96. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=672643087
[2] 祝智庭, 沈德梅.基于大数据的教育技术研究新范式[J].电化教育研究, 2013(10):5-13. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dhjyyj201310001 [3] 何克抗.大数据面面观[J].电化教育研究, 2014(10):12-13. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dhjyyj201410002 [4] 教育部.教育部关于印发《教育信息化"十三五"规划》的通知[EB/OL]. (2016-06-07)[2020-05-16]. http://www.moe.edu.cn/srcsite/A16/s3342/201606/t20160622_269367.html. [5] 教育部.教育部关于印发《教育信息化2.0行动计划》的通知[EB/OL]. (2018-04-13)[2020-05-16]. http://www.moe.gov.cn/srcsite/A16/s3342/201804/t20180425_334188.html. [6] 国务院.国务院关于印发新一代人工智能发展规划的通知[EB/OL]. (2017-07-20)[2020-05-16]. http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm [7] 刘和海, 潘阳. "以学习者为中心":赋权理论视角下的个性化学习实践逻辑[J].中国电化教育, 2018(8):100-106. LIU H H, PAN Y. "Students as the center":personalized learning practice logic from the perspective of empowerment theory[J]. China Educational Technology, 2018(8):100-106.
[8] 郭朝晖, 王楠, 刘建设.国内外自适应学习平台的现状分析研究[J].电化教育研究, 2016(4):55-61. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dhjyyj201604008 GUO Z H, WANG N, LIU J S. Analysis of the current research status of adaptive learning platforms[J]. e-Education Research, 2016(4):55-61. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dhjyyj201604008
[9] Educause. Key Issues in teaching and learning 2016[DB/OL]. (2016-02-05)[2020-05-06]. http://www.educause.edu/eli/initiatives/key-issues-in-teaching-and-learning.
[10] CHAPLOT D S, RHIM E, KIM J. Personalized adaptive learning using neural networks[C]//Proceedings of the 3rd ACM Conference on Learning. New York: ACM, 2016: 165-168.
[11] 姜强, 赵蔚, 李松, 等.个性化自适应学习研究——大数据时代数字化学习的新常态[J].中国电化教育, 2016(2):25-32. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=667836747 JIANG Q, ZHAO W, LI S, et al. Research on personalized adaptive learning—a new normal form of digital learning in big data era[J]. China Educational Technology, 2016(2):25-32. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=667836747
[12] 叶海智, 杨柳, 黄宏涛, 等.面向认知诊断的能力等级自适应试题推送模型构建及应用[J].电化教育研究, 2019(11):93-98;113. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dhjyyj201911013 YE H Z, YANG L, HUANG H T, et al. Construction and application of an adaptive test push model for cognitive diagnosis[J]. e-Education Research, 2019(11):93-98;113. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dhjyyj201911013
[13] LIU Q, TONG S W, LIU C R, et al. Exploiting cognitive structure for adaptive learning[C]//Proceedings of the 25th ACM SIGKDD International Conference on Know-ledge Discovery & Data Mining. New York: ACM, 2019: 627-635.
[14] 崔向平, 徐娟.自适应学习技术的应用、问题及趋势——访美国俄亥俄州立大学大卫·斯坦恩教授[J].开放教育研究, 2019, 25(5):4-10. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=kfjyyj201905002 CUI X P, XU J. Application, issues, and trends of adaptive learning technology:an interview with Professor David Stein of Ohio State University[J]. Open Education Research, 2019, 25(5):4-10. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=kfjyyj201905002
[15] 师亚飞, 彭红超, 童名文.基于学习画像的精准个性化学习路径生成性推荐策略研究[J].中国电化教育, 2019(5):84-91. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgdhjy201905013 SHI Y F, PENG H C, TONG M W. Research on generative paths recommendation strategies of precisive persona-lized learning based on learning profile[J]. China Educational Technology, 2019(5):84-91. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgdhjy201905013
[16] 原福永, 冯凯东, 李晨, 等.融合用户经历的多策略自适应推荐模型[J].小型微型计算机系统, 2019, 40(7):1410-1415. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xxwxjsjxt201907011 YUAN F Y, FENG K D, LI C, et al. User experience based adaptive recommendation model[J]. Journal of Chinese Computer Systems, 2019, 40(7):1410-1415. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xxwxjsjxt201907011
[17] XIE H, CHU H C, HWANG G J, et al. Trends and development in technology-enhanced adaptive/personalized lear-ning:a systematic review of journal publications from 2007 to 2017[J]. Computers & Education, 2019, 140:1-16.
[18] 李菲茗, 叶艳伟, 李晓菲, 等.知识追踪模型在教育领域的应用:2008—2017年相关研究的综述[J].中国远程教育, 2019(7):86-91. http://www.cnki.com.cn/Article/CJFDTotal-DDJY201907012.htm [19] ROY S, MADHYASTHA M, LAWRENCE S, et al. Inferring concept prerequisite relations from online educational resources[C]//Proceedings of the AAAI Conference on Ar-tificial Intelligence. Honolulu, Hawaii: AAAI Press, 2019, 33: 9589-9594.
[20] 李振, 周东岱.教育知识图谱的概念模型与构建方法研究[J].电化教育研究, 2019(8):78-86. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dhjyyj201908010 LI Z, ZHOU D D. Research on conceptual model and construction method of educational knowledge graph[J]. e-Education Research, 2019(8):78-86. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dhjyyj201908010
[21] NATHANI D, CHAUHAN J, SHARMA C, et al. Learning attention-based embeddings for relation prediction in knowledge graphs[C]//Proceedings of the 57th Confe-rence of the Association for Computational Linguistics. Florence: Association for Computational Linguistics, 2019: 1-10.
[22] XU J C, QIU X P, CHEN K, et al. Knowledge graph representation with jointly structural and textual encoding[C]//Proceedings of the 26th International Joint Confe-rence on Artificial Intelligence. Melbourne: Morgan Kaufmann Publishers Inc, 2017: 1318-1324.
[23] PIECH C, BASSEN J, HUANG J, et al. Deep knowledge tracing[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal, Quebec: MIT Press, 2015: 505-513.
[24] YEUNG C K, YEUNG D Y. Addressing two problems in deep knowledge tracing via prediction-consistent regularization[C]//Proceedings of the 5th Annual ACM Confe-rence on Learning at Scale. New York: ACM, 2018: 1-10.
[25] KIM D, PARK C, OH J, et al. Deep hybrid recommender systems via exploiting document context and statistics of items[J]. Information Sciences, 2017, 417:72-87. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=1a314923edbfcb778f94a7f442a8169a
[26] NABIZADEH A H, GONÇALVES D, GAMA S, et al. Ada-ptive learning path recommender approach using auxiliary learning objects[J]. Computers & Education, 2020, 147:1-17.
[27] ZHOU Y W, HUANG C Q, HU Q T, et al. Personalized learning full-path recommendation model based on LSTM neural networks[J]. Information Sciences, 2018, 444:135-152. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=d0d691997ba4c9a87293929466b0a66b
[28] CHEN P H, LU Y, ZHENG V W, et al. Knowedu:a system to construct knowledge graph for education[J]. IEEE Access, 2018, 6:31553-31563. http://ieeexplore.ieee.org/document/8362657
[29] JI G L, LIU K, HE S Z, et al. Distant supervision for relation extraction with sentence-level attention and entity descriptions[C]//Proceedings of the 31st AAAI Conference on Artificial Intelligence. San Francisco, California: AAAI Press, 2017: 3060-3066.
[30] WANG L, SY A, LIU L, et al. Learning to represent stu-dent knowledge on programming exercises using deep lear-ning[C]//Proceedings of the 10th International Confe-rence on Educational Data Mining. Wuhan, China: International Educational Data Mining Society, 2017: 324-329.
[31] XIONG X L, ZHAO S Y, VAN INWEGEN E G, et al. Going deeper with deep knowledge tracing[C]//Proceedings of the 9th International Conference on Educational Data Mining. Raleigh, North Carolina: International Educational Data Mining Society, 2016: 545-550.
[32] CORBETT A T, ANDERSON J R. Knowledge tracing:mode-ling the acquisition of procedural knowledge[J]. User Mode-ling and User-Adapted Interaction, 1994, 4(4):253-278.
[33] KÄSER T, KLINGLER S, SCHWING A G, et al. Dynamic Bayesian networks for student modeling[J]. IEEE Tran-sactions on Learning Technologies, 2017, 10(4):450-462.
[34] LIN Y K, SHEN S Q, LIU Z Y, et al. Neural relation extraction with selective attention over instances[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin: Association for Computational Linguistics, 2016: 2124-2133.
[35] OKUBO F, YAMASHITA T, SHIMADA A, et al. A neural network approach for students' performance prediction[C]//Proceedings of the 7th International Learning Analytics & Knowledge Conference. New York: ACM, 2017: 598-599.
[36] NORMADHI N B A, SHUIB L, NASIR H N M, et al. Identification of personal traits in adaptive learning environment:systematic literature review[J]. Computers & Education, 2019, 130:168-190.
[37] 佩特·约翰内斯, 拉里·拉格斯多姆.自适应学习:溯源、前景与误区[J].中国远程教育, 2018(7):43-53;80. JOHANES P, LAGERSTROM L. Adative learning:tra-cing, prospect and misunderstanding[J]. Distance Education in China, 2018(7):43-53;80.
[38] 梁迎丽, 刘陈.人工智能教育应用的现状分析、典型特征与发展趋势[J].中国电化教育, 2018(3):24-30. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=674704183 LIANG Y L, LIU C. The application status, typical cha-racteristics and development trends of artificial intelligence in education[J]. China Educational Technology, 2018(3):24-30. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=674704183
[39] NOVAK J D.学习、创造与使用知识概念图促进企业和学校的学习变革[M].赵国庆, 吴金闪, 唐京京, 等译.北京: 人民邮电出版社, 2016. [40] 李振, 周东岱, 王勇. "人工智能+"视域下的教育知识图谱:内涵、技术框架与应用研究[J].远程教育杂志, 2019, 37(4):42-53. LI Z, ZHOU D D, WANG Y. Research of educational knowledge graph from the perspective of "artificial intelligence+":connotation, technical framework and application[J]. Journal of Distance Education, 2019, 37(4):42-53.
[41] 马相春, 钟绍春, 徐妲.大数据视角下个性化自适应学习系统支撑模型及实现机制研究[J].中国电化教育, 2017(4):97-102. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgdhjy201704015 MA X C, ZHONG S C, XU D. Research on support model and implementation mechanism of personalized adaptive learning system from the perspective of big data[J]. China Educational Technology, 2017(4):97-102. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgdhjy201704015
[42] CHAPLOT D, KOEDINGER K R. Data-driven automated induction of prerequisite structure graphs[C]//Procee-dings of 9th International Conference on Educational Data Mining. Raleigh, North Carolina: International Educational Data Mining Society, 2016: 318-323.
[43] SOCHER R, CHEN D Q, MANNING C D, et al. Reaso-ning with neural tensor networks for knowledge base completion[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada: Curran Associates Inc, 2013: 926-934.
[44] BORDES A, USUNIER N, GARCIA-DURAN A, et al. Translating embeddings for modeling multi-relational data[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems.Lake Tahoe, Nevada: Curran Associates Inc, 2013: 2787-2795.
[45] JI G L, HE S Z, XU L H, et al. Knowledge graph embe-dding via dynamic mapping matrix[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Confe-rence on Natural Language Processing. Beijing: Association for Computer Linguistics, 2015: 687-696.
[46] KAZEMI S M, POOLE D. Simple embedding for link prediction in knowledge graph[C]//Proceedings of the 32nd Annual Conference on Neural Information Processing Systems. Montréal, Canada: Neural Information Processing Sys-tems, 2018: 4284-4295.
[47] EBBINGHAUS H. Memory:a contribution to experimental psychology[J]. Annals of Neurosciences, 2013, 20(4): 155-156.
[48] MATAYOSHI J, GRANZIOL U, DOBLE C, et al. Forge-tting curves and testing effect in an adaptive learning and assessment system[C]//Proceedings of the 11th International Conference on Educational Data Mining. Buffalo, NY: International Educational Data Mining Society, 2018: 607-612.
[49] HENZE N, NEJDL W. Adaptation in open corpus hypermedia[J]. International Journal of Artificial Intelligence in Education, 2001, 12(4):325-350. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=CC0212447533
[50] TRIANTAFILLOU E, POMPORTSIS A, DEMETRIADIS S, et al. The value of adaptivity based on cognitive style:an empirical study[J]. British Journal of Educational Technology, 2004, 35(1):95-106.
[51] LIANG Y, CHEN H R. The research of video resource personalized recommendation system based on education website[C]//Proceedings of the 9th International Confe-rence on Computer Science & Education. New York: ACM, 2014: 898-902.
[52] HAN Q Y, GAO F, WANG H. Ontology-based learning object recommendation for cognitive considerations[C]//Proceedings of the 8th World Congress on Intelligent Control and Automation. Jinan: IEEE, 2010: 2746-2750.
[53] KUSUMAWARDANI S S, PRAKOSO R S, SANTOSA P I. Using ontology for providing content recommendation based on learning styles inside e-learning[C]//Procee-dings of the 2nd International Conference on Artificial Intelligence, Modelling and Simulation. New York: ACM, 2014: 276-281.
[54] SHISHEHCHI S, BANIHASHEM S Y, ZIN N A M, et al. Review of personalized recommendation techniques for learners in e-learning systems[C]//Proceedings of the 2011 International Conference on Semantic Technology and Information Retrieval. Putrajaya: IEEE, 2011: 277-281.
[55] ZHAO C Y, WANG J L. Network education video recommendation algorithm based on context and trust relationship[C]//Proceedings of the 2013 IEEE 4th International Conference on Software Engineering and Service Science. Beijing: IEEE, 2013: 537-540.
[56] ŠPILKA M, POSOLDOVA A, ROZINAJ G, et al. Importance of recommendation system in modern forms of lear-ning[C]//Proceedings of the 2016 International Confe-rence on Systems, Signals and Image Processing. Bratislava, Slovakia: IEEE, 2016: 1-4.
[57] 陈敏, 余胜泉, 杨现民, 等.泛在学习的内容个性化推荐模型设计——以"学习元"平台为例[J].现代教育技术, 2011(6):13-18. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xdjyjs201106003 CHEN M, YU S Q, YANG X M, et al. The personalized recommendation model design of content in the ubiquitous learning——taking "Learning Cell" for example[J]. Modern Educational Technology, 2011(6):13-18. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xdjyjs201106003
[58] TANG S, PETERSON J C, PARDOS Z A. Deep neural networks and how they apply to sequential education data[C]//Proceedings of the 3rd ACM Conference on Lear-ning at Scale. New York: ACM, 2016: 321-324.
[59] 余胜泉, 李晓庆.基于大数据的区域教育质量分析与改进研究[J].电化教育研究, 2017(7):5-12. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=672492952 YU S Q, LI X Q. Research on the analysis and improvement of regional education quality based on big data[J]. e-Education Research, 2017(7):5-12. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=672492952
[60] 姜强, 赵蔚, 李松, 等.大数据背景下的精准个性化学习路径挖掘研究——基于AprioriAll的群体行为分析[J].电化教育研究, 2018(2):45-52. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=674308242 JIANG Q, ZHAO W, LI S, et al. Research on the mining of precise personalized learning path in age of big data:analysis of group learning behaviors based on AprioriAll[J]. e-Education Research, 2018(2):45-52. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=674308242
[61] HUANG Z, XU W, YU K. Bidirectional LSTM-CRF mo-dels for sequence tagging[J]. arXiv, (2015-08-09)[2020-05-16]. https://arxiv.org/abs/1508.01991.
[62] 陈伟, 吴友政, 陈文亮, 等.基于BiLSTM-CRF的关键词自动抽取[J].计算机科学, 2018, 45(S1):91-96;113. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjkx2018z1019 CHEN W, WU Y Z, CHEN W L, et al. Automatic keyword extraction based on BiLSTM-CRF[J]. Computer Science, 2018, 45(S1):91-96;113. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjkx2018z1019
[63] MA X Z, HOVY E. End-to-end sequence labeling via bi-directional lstm-cnns-crf[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Lingui-stics. Berlin: Association for Computer Linguistics, 2016: 1064-1074.
[64] LI J, YE D H, SHANG S. Adversarial transfer for named entity boundary detection with pointer networks[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence. Macao: Morgan Kaufmann Publishers Inc, 2019: 5053-5059.
[65] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[J/OL]. arXiv, (2015-08-09)[2020-05-16]. https://arxiv.org/abs/1508.01991.
[66] SCHLICHTKRULL M, KIPF T N, BLOEM P, et al. Mode-ling relational data with graph convolutional networks[C]//Proceedings of the European Semantic Web Conference. Berlin: Springer, 2018: 593-607.
[67] KHAJAH M, LINDSEY R V, MOZER M C. How deep is knowledge tracing?[C]//Proceedings of the 9th International Conference on Educational Data Mining. Raleigh, North Carolina: International Educational Data Mining Society, 2016: 94-101.
[68] NAKAGAWA H, IWASAWA Y, MATSUO Y. Graph-based knowledge tracing: modeling student proficiency using graph neural network[C]//Proceedings of the 2019 IEEE/WIC/ACM International Conference on Web Intelligence. New York: ACM, 2019: 156-163.
[69] BATTAGLIA P W, HAMRICK J B, BAPST V, et al. Relational inductive biases, deep learning, and graph networks[J]. arXiv, (2018-01-04)[2020-05-16]. https://arxiv.org/abs/1806.01261.
[70] GILMER J, SCHOENHOLZ S S, RILEY P F, et al. Neural message passing for quantum chemistry[C]//Proceedings of the 34th International Conference on Machine Learning. New York: ACM, 2017: 1263-1272.
[71] WANG X L, GIRSHICK R, GUPTA A, et al. Non-local neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT: IEEE, 2018: 7794-7803.
[72] PANDEY S, KARYPIS G. A self-attentive model for knowledge tracing[C]//Proceedings of the 12th International Conference on Educational Data Mining. Montréal: International Educational Data Mining Society, 2019: 384-389.
[73] GONZÁLEZ-BRENES J, HUANG Y, BRUSILOVSKY P. General features in knowledge tracing to model multiple subskills, temporal item response theory, and expert knowledge[C]//Proceedings of the 7th International Conference on Educational Data Mining. London: International Educational Data Mining Society, 2014: 84-91.
[74] CAI Y L, NIU Z D, WANG Y W, et al. Learning trend analysis and prediction based on knowledge tracing and regression analysis[C]//Proceedings of the International Conference on Database Systems for Advanced Applications. Berlin: Springer, 2015: 29-41.
[75] KHAJAH M M, HUANG Y, GONZÁLEZ-BRENES J P, et al. Integrating knowledge tracing and item response theory: a tale of two frameworks[C]//Proceedings of 4th International Workshop on Personalization Approaches in Learning Environments. Aalborg: CEUR Workshop, 2014, 1181: 7-15.
[76] WANG X, HE X N, CAO Y X, et al. Kgat: knowledge graph attention network for recommendation[C]//Proceedings of the 25th ACM SIGKDD International Confe-rence on Knowledge Discovery & Data Mining. New York: ACM, 2019: 950-958.
[77] PLIAKOS K, JOO S H, PARK J Y, et al. Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems[J]. Computers & Education, 2019, 137:91-103.
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